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timestamp[us, tz=UTC] | card
stringlengths 19
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| embedding
sequence |
|---|---|---|---|---|---|---|---|---|---|
potsawee/wiki_bio_gpt3_hallucination | potsawee | 2023-05-29T23:14:09Z | 641 | 10 | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:2303.08896",
"region:us"
] | [
"text-classification"
] | 2023-03-18T18:05:21Z | ---
license: cc-by-sa-3.0
task_categories:
- text-classification
language:
- en
size_categories:
- n<1K
dataset_info:
features:
- name: gpt3_text
dtype: string
- name: wiki_bio_text
dtype: string
- name: gpt3_sentences
sequence: string
- name: annotation
sequence: string
- name: wiki_bio_test_idx
dtype: int64
- name: gpt3_text_samples
sequence: string
splits:
- name: evaluation
num_bytes: 5042581
num_examples: 238
download_size: 2561507
dataset_size: 5042581
---
# Dataset Card for WikiBio GPT-3 Hallucination Dataset
- GitHub repository: https://github.com/potsawee/selfcheckgpt
- Paper: [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896)
### Dataset Summary
- We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt: ```This is a Wikipedia passage about {concept}``` where `concept` represents an individual from the WikiBio dataset.
- We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate.
- We report the data statistics, annotation process, and inter-annotator agreement in our paper.
## Update
- v3 (5 May 2023): 238 test IDs have been annotated in total.
- v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset.
- v1 (15 March 2023): 65 test IDs -- here is `wiki_bio_test_idx` of the documents in v1 [[Link]](https://drive.google.com/file/d/1N3_ZQmr9yBbsOP2JCpgiea9oiNIu78Xw/view?usp=sharing)
## Dataset Structure
Each instance consists of:
- `gpt3_text`: GPT-3 generated passage
- `wiki_bio_text`: Actual Wikipedia passage (first paragraph)
- `gpt3_sentences`: `gpt3_text` split into sentences using `spacy`
- `annotation`: human annotation at the sentence level
- `wiki_bio_test_idx`: ID of the concept/individual from the original wikibio dataset (testset)
- `gpt3_text_samples`: list of 20 sampled passages (do_sample = True & temperature = 1.0)
### Citation Information
```
@misc{manakul2023selfcheckgpt,
title={SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models},
author={Potsawee Manakul and Adian Liusie and Mark J. F. Gales},
year={2023},
eprint={2303.08896},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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discovery | null | 2023-06-02T12:27:46Z | 640 | 5 | [
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"discourse-marker-prediction",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: discovery
pretty_name: Discovery
tags:
- discourse-marker-prediction
dataset_info:
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features:
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dtype: int32
splits:
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download_size: 146233621
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features:
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num_examples: 869
download_size: 146233621
dataset_size: 3727959
train-eval-index:
- config: discovery
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: discoverysmall
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
config_names:
- discovery
- discoverysmall
---
# Dataset Card for Discovery
## 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/sileod/Discovery
- **Repository:** https://github.com/sileod/Discovery
- **Paper:** https://www.aclweb.org/anthology/N19-1351/
- **Leaderboard:**
- **Point of Contact:** damien.sileo at inria.fr
### Dataset Summary
Discourse marker prediction with 174 markers
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
input : sentence1, sentence2,
label: marker originally between sentence1 and sentence2
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
Train/Val/Test
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Aranea english web corpus
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Self supervised (see paper)
#### 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{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1351",
pages = "3477--3486",
abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",
}
```
### Contributions
Thanks to [@sileod](https://github.com/sileod) for adding this dataset. | [
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multidoc2dial | null | 2023-08-29T09:45:02Z | 640 | 2 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:extended|doc2dial",
"language:en",
"license:apache-2.0",
"arxiv:2109.12595",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- extended|doc2dial
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: multidoc2dial
pretty_name: MultiDoc2Dial
config_names:
- dialogue_domain
- document_domain
- multidoc2dial
dataset_info:
- config_name: dialogue_domain
features:
- name: dial_id
dtype: string
- name: domain
dtype: string
- name: turns
list:
- name: turn_id
dtype: int32
- name: role
dtype: string
- name: da
dtype: string
- name: references
list:
- name: id_sp
dtype: string
- name: label
dtype: string
- name: doc_id
dtype: string
- name: utterance
dtype: string
splits:
- name: train
num_bytes: 11700558
num_examples: 3474
- name: validation
num_bytes: 2210338
num_examples: 661
download_size: 6868509
dataset_size: 13910896
- config_name: document_domain
features:
- name: domain
dtype: string
- name: doc_id
dtype: string
- name: title
dtype: string
- name: doc_text
dtype: string
- name: spans
list:
- name: id_sp
dtype: string
- name: tag
dtype: string
- name: start_sp
dtype: int32
- name: end_sp
dtype: int32
- name: text_sp
dtype: string
- name: title
dtype: string
- name: parent_titles
sequence:
- name: id_sp
dtype: string
- name: text
dtype: string
- name: level
dtype: string
- name: id_sec
dtype: string
- name: start_sec
dtype: int32
- name: text_sec
dtype: string
- name: end_sec
dtype: int32
- name: doc_html_ts
dtype: string
- name: doc_html_raw
dtype: string
splits:
- name: train
num_bytes: 29378879
num_examples: 488
download_size: 6868509
dataset_size: 29378879
- config_name: multidoc2dial
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: da
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: utterance
dtype: string
- name: domain
dtype: string
splits:
- name: validation
num_bytes: 24331936
num_examples: 4201
- name: train
num_bytes: 126589862
num_examples: 21451
- name: test
num_bytes: 23026892
num_examples: 4094
download_size: 6868509
dataset_size: 173948690
---
# Dataset Card for MultiDoc2Dial
## 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://doc2dial.github.io/multidoc2dial/
- **Repository:** https://github.com/IBM/multidoc2dial
- **Paper:** https://arxiv.org/pdf/2109.12595.pdf
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.
Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a
single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking
conversation involves multiple topics, and hence is grounded on different documents.
### Supported Tasks and Leaderboards
> Supported Task: Open domain question answering, document-grounded dialogue, passage retrieval
> Leaderboard:
### Languages
English
## Dataset Structure
### Data Instances
Sample data instance for `multidoc2dial` :
```
{
"id": "8df07b7a98990db27c395cb1f68a962e_1",
"title": "Top 5 DMV Mistakes and How to Avoid Them#3_0",
"context": "Many DMV customers make easily avoidable mistakes that cause them significant problems, including encounters with law enforcement and impounded vehicles. Because we see customers make these mistakes over and over again , we are issuing this list of the top five DMV mistakes and how to avoid them. \n\n1. Forgetting to Update Address \nBy statute , you must report a change of address to DMV within ten days of moving. That is the case for the address associated with your license, as well as all the addresses associated with each registered vehicle, which may differ. It is not sufficient to only: write your new address on the back of your old license; tell the United States Postal Service; or inform the police officer writing you a ticket. If you fail to keep your address current , you will miss a suspension order and may be charged with operating an unregistered vehicle and/or aggravated unlicensed operation, both misdemeanors. This really happens , but the good news is this is a problem that is easily avoidable. Learn more about how to change the address on your license and registrations [1 ] \n\n2. Leaving the State Without Notifying DMV \nStates communicate with each other , so when you move to another state, be sure to tie up any loose ends regarding your New York State license or registration. That means resolving any unanswered tickets, suspensions or revocations, and surrendering your license plates to NYS when you get to your new home state. A license suspension or revocation here could mean that your new home state will not issue you a license there. Remember , it is important to notify DMV of your new address so that any possible mail correspondence can reach you. Also , turning in your plates is important to avoid an insurance lapse. \n\n3. Letting Insurance Lapse \nBecause we all pay indirectly for crashes involving uninsured motorists , New York State requires every motorist to maintain auto insurance every single day a vehicle is registered. DMV works with insurance companies to electronically monitor your insurance coverage , and we know when coverage is dropped for any reason. When that happens , we mail you an insurance inquiry letter to allow you to clear up the problem. We send 500,000 inquiry letters a year. If the inquiry letter does not resolve the problem , we must suspend the vehicle registration and , if it persists, your driver license!We suspend 300,000 registrations a year for failure to maintain insurance. If you fail to maintain an updated address with us , you won t learn that you have an insurance problem , and we will suspend your registration and license. Make sure you turn in your vehicle s license plates at DMV before you cancel your insurance policy. Insurance policies must be from a company licensed in New York State. Learn more about Insurances Lapes [2] and How to Surrender your Plates [3 ] \n\n4. Understanding how Much Traffic Points Cost \nDMV maintains a point system to track dangerous drivers. Often , motorists convicted of a traffic ticket feel they have resolved all their motoring issues with the local court, but later learn that the Driver Responsibility Assessment DRA is a separate DMV charge based on the total points they accumulate. The $300 DRA fee can be paid in $100 annual installments over three years. Motorists who fail to maintain an updated address with DMV may resolve their tickets with the court, but never receive their DRA assessment because we do not have their new address on record. Failure to pay the DRA will result in a suspended license. Learn more about About the NYS Driver Point System [4] and how to Pay Driver Responsibility Assessment [5 ] \n\n5. Not Bringing Proper Documentation to DMV Office \nAbout ten percent of customers visiting a DMV office do not bring what they need to complete their transaction, and have to come back a second time to finish their business. This can be as simple as not bringing sufficient funds to pay for a license renewal or not having the proof of auto insurance required to register a car. Better yet , don t visit a DMV office at all, and see if your transaction can be performed online, like an address change, registration renewal, license renewal, replacing a lost title, paying a DRA or scheduling a road test. Our award - winning website is recognized as one of the best in the nation. It has all the answers you need to efficiently perform any DMV transaction. Consider signing up for our MyDMV service, which offers even more benefits. Sign up or log into MyDMV [6 ] ",
"question": "Hello, I forgot o update my address, can you help me with that?[SEP]",
"da": "query_condition",
"answers":
{
"text": ['you must report a change of address to DMV within ten days of moving. That is the case for the address associated with your license, as well as all the addresses associated with each registered vehicle, which may differ. "],
"answer_start": [346]
},
"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles.",
"domain": "dmv"
}
```
Sample data instance for `document_domain` :
```
{
"domain": "ssa",
"doc_id": "Benefits Planner: Survivors | Planning For Your Survivors | Social Security Administration#1_0",
"title": "Benefits Planner: Survivors | Planning For Your Survivors | Social Security Administration#1",
"doc_text": "\n\nBenefits Planner: Survivors | Planning For Your Survivors \nAs you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. You can earn up to four credits each year. In 2019 , for example , you earn one credit for each $1,360 of wages or self - employment income. When you have earned $5,440 , you have earned your four credits for the year. The number of credits needed to provide benefits for your survivors depends on your age when you die. No one needs more than 40 credits 10 years of work to be eligible for any Social Security benefit. But , the younger a person is , the fewer credits they must have for family members to receive survivors benefits. Benefits can be paid to your children and your spouse who is caring for the children even if you don't have the required number of credits. They can get benefits if you have credit for one and one - half years of work 6 credits in the three years just before your death. \n\nFor Your Widow Or Widower \nThere are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse's earnings record. And , for many of those survivors, particularly aged women, those benefits are keeping them out of poverty. Widows and widowers can receive : reduced benefits as early as age 60 or full benefits at full retirement age or older. benefits as early as age 50 if they're disabled AND their disability started before or within seven years of your death. benefits at any age , if they have not remarried , and if they take care of your child who is under age 16 or disabled and receives benefits on your record. If applying for disability benefits on a deceased worker s record , they can speed up the application process if they complete an Adult Disability Report and have it available at the time of their appointment. We use the same definition of disability for widows and widowers as we do for workers. \n\nFor Your Surviving Divorced Spouse \nIf you have a surviving divorced spouse , they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more. Benefits paid to a surviving divorced spouse won't affect the benefit amounts your other survivors will receive based on your earnings record. If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record , they will not have to meet the length - of - marriage rule. The child must be your natural or legally adopted child. \n\nFor Your Children \nYour unmarried children who are under 18 up to age 19 if attending elementary or secondary school full time can be eligible to receive Social Security benefits when you die. And your child can get benefits at any age if they were disabled before age 22 and remain disabled. Besides your natural children , your stepchildren, grandchildren, step grandchildren or adopted children may receive benefits under certain circumstances. For further information , view our publication. \n\nFor Your Parents \nYou must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record. Generally, your parent also must not have married after your death ; however, there are some exceptions. In addition to your natural parent , your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16. \n\nHow Much Would Your Survivors Receive \nHow much your family could receive in benefits depends on your average lifetime earnings. The higher your earnings were , the higher their benefits would be. We calculate a basic amount as if you had reached full retirement age at the time you die. These are examples of monthly benefit payments : Widow or widower, full retirement age or older 100 percent of your benefit amount ; Widow or widower , age 60 to full retirement age 71 to 99 percent of your basic amount ; Disabled widow or widower , age 50 through 59 71 percent ; Widow or widower , any age, caring for a child under age 16 75 percent ; A child under age 18 19 if still in elementary or secondary school or disabled 75 percent ; and Your dependent parent , age 62 or older : One surviving parent 82 percent. Two surviving parents 75 percent to each parent. Percentages for a surviving divorced spouse would be the same as above. There may also be a special lump - sum death payment. \n\nMaximum Family Amount \nThere's a limit to the amount that family members can receive each month. The limit varies , but it is generally equal to between 150 and 180 percent of the basic benefit rate. If the sum of the benefits payable to family members is greater than this limit , the benefits will be reduced proportionately. Any benefits paid to a surviving divorced spouse based on disability or age won't count toward this maximum amount. Get your online or check our Benefit Calculators for an estimate of the benefits your family could receive if you died right now. \n\nOther Things You Need To Know \nThere are limits on how much survivors may earn while they receive benefits. Benefits for a widow, widower, or surviving divorced spouse may be affected by several additional factors : If your widow, widower, or surviving divorced spouse remarries before they reach age 60 age 50 if disabled , they cannot receive benefits as a surviving spouse while they're married. If your widow, widower, or surviving divorced spouse remarries after they reach age 60 age 50 if disabled , they will continue to qualify for benefits on your Social Security record. However , if their current spouse is a Social Security beneficiary , they may want to apply for spouse's benefits on their record. If that amount is more than the widow's or widower's benefit on your record , they will receive a combination of benefits that equals the higher amount. If your widow, widower, or surviving divorced spouse receives benefits on your record , they can switch to their own retirement benefit as early as age 62. This assumes they're eligible for retirement benefits and their retirement rate is higher than their rate as a widow, widower, or surviving divorced spouse. In many cases , a widow or widower can begin receiving one benefit at a reduced rate and then, at full retirement age, switch to the other benefit at an unreduced rate. If your widow, widower, or surviving divorced spouse will also receive a pension based on work not covered by Social Security, such as government or foreign work , their Social Security benefits as a survivor may be affected. ",
"spans": [
{
"id_sp": "1",
"tag": "h2",
"start_sp": 0,
"end_sp": 61,
"text_sp": "\n\nBenefits Planner: Survivors | Planning For Your Survivors \n",
"title": "Benefits Planner: Survivors | Planning For Your Survivors",
"parent_titles": {
"id_sp": [],
"text": [],
"level": []
},
"id_sec": "t_0",
"start_sec": 0,
"text_sec": "\n\nBenefits Planner: Survivors | Planning For Your Survivors \n",
"end_sec": 61
},
{
"id_sp": "2",
"tag": "u",
"start_sp": 61,
"end_sp": 90,
"text_sp": "As you plan for the future , ",
"title": "Benefits Planner: Survivors | Planning For Your Survivors",
"parent_titles": {
"id_sp": [],
"text": [],
"level": []
},
"id_sec": "1",
"start_sec": 61,
"text_sec": "As you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. ",
"end_sec": 274
},
{
"id_sp": "3",
"tag": "u",
"start_sp": 90,
"end_sp": 168,
"text_sp": "you'll want to think about what your family would need if you should die now. ",
"title": "Benefits Planner: Survivors | Planning For Your Survivors",
"parent_titles": {
"id_sp": [],
"text": [],
"level": []
},
"id_sec": "1",
"start_sec": 61,
"text_sec": "As you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. ",
"end_sec": 274
}
],
"doc_html_ts": "<main><section><div><h2 sent_id=\"1\" text_id=\"1\">Benefits Planner: Survivors | Planning For Your Survivors</h2></div></section><section><div><article><section><div tag_id=\"1\"><u sent_id=\"2\" tag_id=\"1\"><u sent_id=\"2\" tag_id=\"1\" text_id=\"2\">As you plan for the future ,</u><u sent_id=\"2\" tag_id=\"1\" text_id=\"3\">you 'll want to think about what your family would need if you should die now .</u></u><u sent_id=\"3\" tag_id=\"1\"><u sent_id=\"3\" tag_id=\"1\" text_id=\"4\">Social Security can help your family if you have earned enough Social Security credits through your work .</u></u></div><div tag_id=\"2\"><u sent_id=\"4\" tag_id=\"2\"><u sent_id=\"4\" tag_id=\"2\" text_id=\"5\">You can earn up to four credits each year .</u></u><u sent_id=\"5\" tag_id=\"2\"><u sent_id=\"5\" tag_id=\"2\" text_id=\"6\">In 2019 ,</u><u sent_id=\"5\" tag_id=\"2\" text_id=\"7\">for example ,</u><u sent_id=\"5\" tag_id=\"2\" text_id=\"8\">you earn one credit for each $ 1,360 of wages or self - employment income .</u></u><u sent_id=\"6\" tag_id=\"2\"><u sent_id=\"6\" tag_id=\"2\" text_id=\"9\">When you have earned $ 5,440 ,</u><u sent_id=\"6\" tag_id=\"2\" text_id=\"10\">you have earned your four credits for the year .</u></u></div><div tag_id=\"3\"><u sent_id=\"7\" tag_id=\"3\"><u sent_id=\"7\" tag_id=\"3\" text_id=\"11\">The number of credits needed to provide benefits for your survivors depends on your age when you die .</u></u><u sent_id=\"8\" tag_id=\"3\"><u sent_id=\"8\" tag_id=\"3\" text_id=\"12\">No one needs more than 40 credits 10 years of work to be eligible for any Social Security benefit .</u></u><u sent_id=\"9\" tag_id=\"3\"><u sent_id=\"9\" tag_id=\"3\" text_id=\"13\">But ,</u><u sent_id=\"9\" tag_id=\"3\" text_id=\"14\">the younger a person is ,</u><u sent_id=\"9\" tag_id=\"3\" text_id=\"15\">the fewer credits they must have for family members to receive survivors benefits .</u></u></div><div tag_id=\"4\"><u sent_id=\"10\" tag_id=\"4\"><u sent_id=\"10\" tag_id=\"4\" text_id=\"16\">Benefits can be paid to your children and your spouse who is caring for the children even if you do n't have the required number of credits .</u></u><u sent_id=\"11\" tag_id=\"4\"><u sent_id=\"11\" tag_id=\"4\" text_id=\"17\">They can get benefits if you have credit for one and one - half years of work 6 credits in the three years just before your death .</u></u></div></section><section><h3 sent_id=\"12\" text_id=\"18\">For Your Widow Or Widower</h3><div tag_id=\"5\"><u sent_id=\"13\" tag_id=\"5\"><u sent_id=\"13\" tag_id=\"5\" text_id=\"19\">There are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse 's earnings record .</u></u><u sent_id=\"14\" tag_id=\"5\"><u sent_id=\"14\" tag_id=\"5\" text_id=\"20\">And ,</u><u sent_id=\"14\" tag_id=\"5\" text_id=\"21\">for many of those survivors , particularly aged women , those benefits are keeping them out of poverty .</u></u></div><div tag_id=\"6\"><u sent_id=\"15\" tag_id=\"6\"><u sent_id=\"15\" tag_id=\"6\" text_id=\"22\">Widows and widowers can receive :</u></u></div><ul class=\"browser-default\" tag_id=\"6\"><li tag_id=\"6\"><u sent_id=\"16\" tag_id=\"6\"><u sent_id=\"16\" tag_id=\"6\" text_id=\"23\">reduced benefits as early as age 60 or full benefits at full retirement age or older .</u></u></li><div>If widows or widowers qualify for retirement benefits on their own record, they can switch to their own retirement benefit as early as age 62.</div><li tag_id=\"6\"><u sent_id=\"17\" tag_id=\"6\"><u sent_id=\"17\" tag_id=\"6\" text_id=\"24\">benefits as early as age 50 if they 're disabled AND their disability started before or within seven years of your death .</u></u></li><div>If a widow or widower who is caring for your children receives Social Security benefits, they're still eligible if their disability starts before those payments end or within seven years after they end.</div><li tag_id=\"6\"><u sent_id=\"18\" tag_id=\"6\"><u sent_id=\"18\" tag_id=\"6\" text_id=\"25\">benefits at any age ,</u><u sent_id=\"18\" tag_id=\"6\" text_id=\"26\">if they have not remarried ,</u><u sent_id=\"18\" tag_id=\"6\" text_id=\"27\">and if they take care of your child who is under age 16 or disabled and receives benefits on your record .</u></u></li><div>If a widow or widower remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.</div></ul><div>Widows, widowers, and surviving divorced spouses cannot apply online for survivors benefits. They should <a>contact Social Security</a> at <nobr><strong>1-800-772-1213</strong></nobr> (TTY <nobr><strong>1-800-325-0778</strong>) to request an appointment.</nobr></div><div tag_id=\"7\"><u sent_id=\"19\" tag_id=\"7\"><u sent_id=\"19\" tag_id=\"7\" text_id=\"28\">If applying for disability benefits on a deceased worker s record ,</u><u sent_id=\"19\" tag_id=\"7\" text_id=\"29\">they can speed up the application process if they complete an Adult Disability Report and have it available at the time of their appointment .</u></u></div><div tag_id=\"8\"><u sent_id=\"20\" tag_id=\"8\"><u sent_id=\"20\" tag_id=\"8\" text_id=\"30\">We use the same definition of disability for widows and widowers as we do for workers .</u></u></div></section><section><h3 sent_id=\"21\" text_id=\"31\">For Your Surviving Divorced Spouse</h3><div tag_id=\"9\"><u sent_id=\"22\" tag_id=\"9\"><u sent_id=\"22\" tag_id=\"9\" text_id=\"32\">If you have a surviving divorced spouse ,</u><u sent_id=\"22\" tag_id=\"9\" text_id=\"33\">they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more .</u></u></div><div>If your surviving divorced spouse qualifies for retirement benefits on their own record they can switch to their own retirement benefit as early as age 62.</div><div tag_id=\"10\"><u sent_id=\"23\" tag_id=\"10\"><u sent_id=\"23\" tag_id=\"10\" text_id=\"34\">Benefits paid to a surviving divorced spouse wo n't affect the benefit amounts your other survivors will receive based on your earnings record .</u></u></div><div>If your surviving divorced spouse remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.</div><div tag_id=\"11\"><u sent_id=\"24\" tag_id=\"11\"><u sent_id=\"24\" tag_id=\"11\" text_id=\"35\">If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record ,</u><u sent_id=\"24\" tag_id=\"11\" text_id=\"36\">they will not have to meet the length - of - marriage rule .</u></u><u sent_id=\"25\" tag_id=\"11\"><u sent_id=\"25\" tag_id=\"11\" text_id=\"37\">The child must be your natural or legally adopted child .</u></u></div><div>However, if they qualify for benefits as a surviving divorced mother or father who is caring for your child, their benefits may affect the amount of benefits your other survivors will receive based on your earnings record.</div></section><section><h3 sent_id=\"26\" text_id=\"38\">For Your Children</h3><div tag_id=\"12\"><u sent_id=\"27\" tag_id=\"12\"><u sent_id=\"27\" tag_id=\"12\" text_id=\"39\">Your unmarried children who are under 18 up to age 19 if attending elementary or secondary school full time can be eligible to receive Social Security benefits when you die .</u></u></div><div tag_id=\"13\"><u sent_id=\"28\" tag_id=\"13\"><u sent_id=\"28\" tag_id=\"13\" text_id=\"40\">And your child can get benefits at any age if they were disabled before age 22 and remain disabled .</u></u></div><div tag_id=\"14\"><u sent_id=\"29\" tag_id=\"14\"><u sent_id=\"29\" tag_id=\"14\" text_id=\"41\">Besides your natural children ,</u><u sent_id=\"29\" tag_id=\"14\" text_id=\"42\">your stepchildren , grandchildren , step grandchildren or adopted children may receive benefits under certain circumstances .</u></u><u sent_id=\"30\" tag_id=\"14\"><u sent_id=\"30\" tag_id=\"14\" text_id=\"43\">For further information ,</u><u sent_id=\"30\" tag_id=\"14\" text_id=\"44\">view our publication .</u></u></div></section><section><h3 sent_id=\"31\" text_id=\"45\">For Your Parents</h3><div tag_id=\"15\"><u sent_id=\"32\" tag_id=\"15\"><u sent_id=\"32\" tag_id=\"15\" text_id=\"46\">You must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record .</u></u><u sent_id=\"33\" tag_id=\"15\"><u sent_id=\"33\" tag_id=\"15\" text_id=\"47\">Generally , your parent also must not have married after your death ;</u><u sent_id=\"33\" tag_id=\"15\" text_id=\"48\">however , there are some exceptions .</u></u></div><div tag_id=\"16\"><u sent_id=\"34\" tag_id=\"16\"><u sent_id=\"34\" tag_id=\"16\" text_id=\"49\">In addition to your natural parent ,</u><u sent_id=\"34\" tag_id=\"16\" text_id=\"50\">your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16 .</u></u></div></section><section><h3 sent_id=\"35\" text_id=\"51\">How Much Would Your Survivors Receive</h3><div tag_id=\"17\"><u sent_id=\"36\" tag_id=\"17\"><u sent_id=\"36\" tag_id=\"17\" text_id=\"52\">How much your family could receive in benefits</u><u sent_id=\"36\" tag_id=\"17\" text_id=\"53\">depends on your average lifetime earnings .</u></u><u sent_id=\"37\" tag_id=\"17\"><u sent_id=\"37\" tag_id=\"17\" text_id=\"54\">The higher your earnings were ,</u><u sent_id=\"37\" tag_id=\"17\" text_id=\"55\">the higher their benefits would be .</u></u><u sent_id=\"38\" tag_id=\"17\"><u sent_id=\"38\" tag_id=\"17\" text_id=\"56\">We calculate a basic amount as if you had reached full retirement age at the time you die .</u></u></div><div>If you are already receiving reduced benefits when you die, survivors benefits are based on that amount.</div><div tag_id=\"18\"><u sent_id=\"39\" tag_id=\"18\"><u sent_id=\"39\" tag_id=\"18\" text_id=\"57\">These are examples of monthly benefit payments :</u></u></div><ul class=\"browser-default\" tag_id=\"18\"><li tag_id=\"18\"><u sent_id=\"40\" tag_id=\"18\"><u sent_id=\"40\" tag_id=\"18\" text_id=\"58\">Widow or widower , full retirement age or older 100 percent of your benefit amount ;</u></u></li><li tag_id=\"18\"><u sent_id=\"41\" tag_id=\"18\"><u sent_id=\"41\" tag_id=\"18\" text_id=\"59\">Widow or widower ,</u><u sent_id=\"41\" tag_id=\"18\" text_id=\"60\">age 60 to full retirement age 71 to 99 percent of your basic amount ;</u></u></li><li tag_id=\"18\"><u sent_id=\"42\" tag_id=\"18\"><u sent_id=\"42\" tag_id=\"18\" text_id=\"61\">Disabled widow or widower ,</u><u sent_id=\"42\" tag_id=\"18\" text_id=\"62\">age 50 through 59 71 percent ;</u></u></li><li tag_id=\"18\"><u sent_id=\"43\" tag_id=\"18\"><u sent_id=\"43\" tag_id=\"18\" text_id=\"63\">Widow or widower ,</u><u sent_id=\"43\" tag_id=\"18\" text_id=\"64\">any age , caring for a child under age 16 75 percent ;</u></u></li><li tag_id=\"18\"><u sent_id=\"44\" tag_id=\"18\"><u sent_id=\"44\" tag_id=\"18\" text_id=\"65\">A child under age 18 19 if still in elementary or secondary school or disabled 75 percent ;</u><u sent_id=\"44\" tag_id=\"18\" text_id=\"66\">and</u></u></li><li tag_id=\"18\"><div tag_id=\"18\"><u sent_id=\"48\" tag_id=\"18\"><u sent_id=\"48\" tag_id=\"18\" text_id=\"67\">Your dependent parent ,</u><u sent_id=\"48\" tag_id=\"18\" text_id=\"68\">age 62 or older :</u></u></div><ul class=\"browser-default\" tag_id=\"18\"><li tag_id=\"18\"><u sent_id=\"49\" tag_id=\"18\"><u sent_id=\"49\" tag_id=\"18\" text_id=\"69\">One surviving parent 82 percent .</u></u></li><li tag_id=\"18\"><u sent_id=\"50\" tag_id=\"18\"><u sent_id=\"50\" tag_id=\"18\" text_id=\"70\">Two surviving parents 75 percent to each parent .</u></u></li></ul></li></ul><div tag_id=\"19\"><u sent_id=\"51\" tag_id=\"19\"><u sent_id=\"51\" tag_id=\"19\" text_id=\"71\">Percentages for a surviving divorced spouse would be the same as above .</u></u></div><div tag_id=\"20\"><u sent_id=\"52\" tag_id=\"20\"><u sent_id=\"52\" tag_id=\"20\" text_id=\"72\">There may also be a special lump - sum death payment .</u></u></div><h3 sent_id=\"53\" text_id=\"73\">Maximum Family Amount</h3><div tag_id=\"21\"><u sent_id=\"54\" tag_id=\"21\"><u sent_id=\"54\" tag_id=\"21\" text_id=\"74\">There 's a limit to the amount that family members can receive each month .</u></u><u sent_id=\"55\" tag_id=\"21\"><u sent_id=\"55\" tag_id=\"21\" text_id=\"75\">The limit varies ,</u><u sent_id=\"55\" tag_id=\"21\" text_id=\"76\">but it is generally equal to between 150 and 180 percent of the basic benefit rate .</u></u></div><div tag_id=\"22\"><u sent_id=\"56\" tag_id=\"22\"><u sent_id=\"56\" tag_id=\"22\" text_id=\"77\">If the sum of the benefits payable to family members is greater than this limit ,</u><u sent_id=\"56\" tag_id=\"22\" text_id=\"78\">the benefits will be reduced proportionately .</u></u><u sent_id=\"57\" tag_id=\"22\"><u sent_id=\"57\" tag_id=\"22\" text_id=\"79\">Any benefits paid to a surviving divorced spouse based on disability or age wo n't count toward this maximum amount .</u></u></div><div tag_id=\"23\"><u sent_id=\"58\" tag_id=\"23\"><u sent_id=\"58\" tag_id=\"23\" text_id=\"80\">Get your online or check our Benefit Calculators for an estimate of the benefits your family could receive if you died right now .</u></u></div><h3 sent_id=\"59\" text_id=\"81\">Other Things You Need To Know</h3><div tag_id=\"24\"><u sent_id=\"60\" tag_id=\"24\"><u sent_id=\"60\" tag_id=\"24\" text_id=\"82\">There are limits on how much survivors may earn while they receive benefits .</u></u></div><div tag_id=\"25\"><u sent_id=\"61\" tag_id=\"25\"><u sent_id=\"61\" tag_id=\"25\" text_id=\"83\">Benefits for a widow , widower , or surviving divorced spouse may be affected by several additional factors :</u></u></div><div><a>If they remarry</a><section><div tag_id=\"26\"><u sent_id=\"62\" tag_id=\"26\"><u sent_id=\"62\" tag_id=\"26\" text_id=\"84\">If your widow , widower , or surviving divorced spouse remarries before they reach age 60 age 50 if disabled ,</u><u sent_id=\"62\" tag_id=\"26\" text_id=\"85\">they can not receive benefits as a surviving spouse while they 're married .</u></u></div><div tag_id=\"27\"><u sent_id=\"63\" tag_id=\"27\"><u sent_id=\"63\" tag_id=\"27\" text_id=\"86\">If your widow , widower , or surviving divorced spouse remarries after they reach age 60 age 50 if disabled ,</u><u sent_id=\"63\" tag_id=\"27\" text_id=\"87\">they will continue to qualify for benefits on your Social Security record .</u></u></div><div tag_id=\"28\"><u sent_id=\"64\" tag_id=\"28\"><u sent_id=\"64\" tag_id=\"28\" text_id=\"88\">However ,</u><u sent_id=\"64\" tag_id=\"28\" text_id=\"89\">if their current spouse is a Social Security beneficiary ,</u><u sent_id=\"64\" tag_id=\"28\" text_id=\"90\">they may want to apply for spouse 's benefits on their record .</u></u><u sent_id=\"65\" tag_id=\"28\"><u sent_id=\"65\" tag_id=\"28\" text_id=\"91\">If that amount is more than the widow 's or widower 's benefit on your record ,</u><u sent_id=\"65\" tag_id=\"28\" text_id=\"92\">they will receive a combination of benefits that equals the higher amount .</u></u></div></section></div><div><a>If they're eligible for retirement benefits on their own record</a><section><div tag_id=\"29\"><u sent_id=\"66\" tag_id=\"29\"><u sent_id=\"66\" tag_id=\"29\" text_id=\"93\">If your widow , widower , or surviving divorced spouse receives benefits on your record ,</u><u sent_id=\"66\" tag_id=\"29\" text_id=\"94\">they can switch to their own retirement benefit as early as age 62 .</u></u><u sent_id=\"67\" tag_id=\"29\"><u sent_id=\"67\" tag_id=\"29\" text_id=\"95\">This assumes they 're eligible for retirement benefits and their retirement rate is higher than their rate as a widow , widower , or surviving divorced spouse .</u></u></div><div tag_id=\"30\"><u sent_id=\"68\" tag_id=\"30\"><u sent_id=\"68\" tag_id=\"30\" text_id=\"96\">In many cases ,</u><u sent_id=\"68\" tag_id=\"30\" text_id=\"97\">a widow or widower can begin receiving one benefit at a reduced rate and then , at full retirement age , switch to the other benefit at an unreduced rate .</u></u></div><div><a>Full retirement age for retirement benefits</a> may not match full retirement age for survivors benefits.</div></section></div><div><a>If they will also receive a pension based on work not covered by Social Security</a><section><div tag_id=\"31\"><u sent_id=\"69\" tag_id=\"31\"><u sent_id=\"69\" tag_id=\"31\" text_id=\"98\">If your widow , widower , or surviving divorced spouse will also receive a pension based on work not covered by Social Security , such as government or foreign work ,</u><u sent_id=\"69\" tag_id=\"31\" text_id=\"99\">their Social Security benefits as a survivor may be affected .</u></u></div></section></div></section></article></div></section></main>",
"doc_html_raw": "<main class=\"content\" id=\"content\" role=\"main\">\n\n<section>\n\n<div>\n<h2>Benefits Planner: Survivors | Planning For Your Survivors</h2>\n</div>\n</section>\n\n<section>\n\n<div>\n\n<div>\n\n\n</div>\n\n\n\n<article>\n<section>\n<p>As you plan for the future, you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work.</p>\n<p><a>You can earn up to four credits each year</a>. In 2019, for example, you earn one credit for each $1,360 of wages or <a>self-employment</a> income. When you have earned $5,440, you have earned your four credits for the year.</p>\n<p>The number of credits needed to provide benefits for your survivors depends on your age when you die. No one needs more than 40 credits (10 years of work) to be eligible for any Social Security benefit. But, the younger a person is, the fewer credits they must have for family members to receive survivors benefits.</p>\n<p>Benefits can be paid to your children and your spouse who is caring for the children even if you don't have the required number of credits. They can get benefits if you have credit for one and one-half years of work (6 credits) in the three years just before your death.</p>\n</section>\n<section>\n<h3>For Your Widow Or Widower</h3>\n<p>There are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse's earnings record. And, for many of those survivors, particularly aged women, those benefits are keeping them out of poverty. </p>\n<p>Widows and widowers can receive:</p>\n<ul class=\"browser-default\">\n<li>reduced benefits as early as age 60 or full benefits at <a>full retirement age</a> or older.</li>\n<div>\n If widows or widowers qualify for retirement benefits on their own record, they can switch to their own retirement benefit as early as age 62.\n </div>\n<li>benefits as early as age 50 if they're disabled AND their disability started before or within seven years of your death.</li>\n<div>\n If a widow or widower who is caring for your children receives Social Security benefits, they're still eligible if their disability starts before those payments end or within seven years after they end.\n </div>\n<li>benefits at any age, if they have not remarried, and if they take care of your child who is under age 16 or disabled and receives benefits on your record.</li>\n<div>\n If a widow or widower remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.\n </div>\n</ul>\n<div>\n Widows, widowers, and surviving divorced spouses cannot apply online for survivors benefits. They should <a>contact Social Security</a> at <nobr><strong>1-800-772-1213</strong></nobr> (TTY <nobr><strong>1-800-325-0778</strong>) to request an appointment.</nobr>\n</div>\n<p>If applying for disability benefits on a deceased worker s record, they can speed up the application process if they complete an <a>Adult Disability Report</a> and have it available at the time of their appointment.</p>\n<p>We use the same <a>definition of disability</a> for widows and widowers as we do for workers.</p>\n</section>\n<section>\n<h3>For Your Surviving Divorced Spouse</h3>\n<p>If you have a surviving divorced spouse, they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more.</p>\n<div>\n If your surviving divorced spouse qualifies for retirement benefits on their own record they can switch to their own retirement benefit as early as age 62.\n </div>\n<p>Benefits paid to a surviving divorced spouse won't affect the benefit amounts your other survivors will receive based on your earnings record.</p>\n<div>\n If your surviving divorced spouse remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.\n </div>\n<p>If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record, they will not have to meet the length-of-marriage rule. The child must be your natural or legally adopted child.</p>\n<div>\n However, if they qualify for benefits as a surviving divorced mother or father who is caring for your child, their benefits may affect the amount of benefits your other survivors will receive based on your earnings record.\n </div>\n</section>\n<section>\n<h3>For Your Children</h3>\n<p>Your unmarried children who are under 18 (up to age 19 if attending elementary or secondary school full time) can be eligible to receive Social Security benefits when you die.</p>\n<p>And your child can get benefits at any age if they were disabled before age 22 and remain disabled.</p>\n<p>Besides your natural children, your stepchildren, grandchildren, step grandchildren or adopted children may receive benefits under certain circumstances. For further information, view our <a>publication</a>.</p>\n</section>\n<section>\n<h3>For Your Parents</h3>\n<p>You must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record. Generally, your parent also must not have married after your death; however, there are some exceptions.</p>\n<p>In addition to your natural parent, your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16.</p>\n</section>\n<section>\n<h3>How Much Would Your Survivors Receive</h3>\n<p>How much your family could receive in benefits depends on your average lifetime earnings. The higher your earnings were, the higher their benefits would be. We calculate a basic amount as if you had reached full retirement age at the time you die.</p>\n<div>\n If you are already receiving reduced benefits when you die, survivors benefits are based on that amount.\n </div>\n<p>These are examples of monthly benefit payments:</p>\n<ul class=\"browser-default\">\n<li>Widow or widower, <a>full retirement age</a> or older 100 percent of your benefit amount;</li>\n<li>Widow or widower, age 60 to <a>full retirement age</a> 71 to 99 percent of your basic amount;</li>\n<li>Disabled widow or widower, age 50 through 59 71 percent;</li>\n<li>Widow or widower, any age, caring for a child under age 16 75 percent;</li>\n<li>A child under age 18 (19 if still in elementary or secondary school) or disabled 75 percent; and</li>\n<li>Your dependent parent(s), age 62 or older:\n <ul class=\"browser-default\">\n<li>One surviving parent 82 percent.</li>\n<li>Two surviving parents 75 percent to each parent.</li>\n</ul>\n</li>\n</ul>\n<p>Percentages for a surviving divorced spouse would be the same as above.</p>\n<p>There may also be a <a>special lump-sum death payment</a>.</p>\n<h3>Maximum Family Amount</h3>\n<p>There's a limit to the amount that family members can receive each month. <a>The limit varies</a>, but it is generally equal to between 150 and 180 percent of the basic benefit rate.</p>\n<p>If the sum of the benefits payable to family members is greater than this limit, the benefits will be reduced proportionately. (Any benefits paid to a surviving divorced spouse based on disability or age won't count toward this maximum amount.)</p>\n<p>Get your <a></a> online or check our <a>Benefit Calculators</a> for an estimate of the benefits your family could receive if you died right now.</p>\n<h3>Other Things You Need To Know</h3>\n<p>There are <a>limits on how much survivors may earn</a> while they receive benefits.</p>\n<p>Benefits for a widow, widower, or surviving divorced spouse may be affected by several additional factors:</p>\n<div>\n<a>If they remarry</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse remarries before they reach age 60 (age 50 if disabled), they cannot receive benefits as a surviving spouse while they're married.</p>\n<p>If your widow, widower, or surviving divorced spouse remarries after they reach age 60 (age 50 if disabled), they will continue to qualify for benefits on your Social Security record.</p>\n<p>However, if their current spouse is a Social Security beneficiary, they may want to apply for spouse's benefits on their record. If that amount is more than the widow's or widower's benefit on your record, they will receive a combination of benefits that equals the higher amount.</p>\n</section>\n</div>\n<div>\n<a>If they're eligible for retirement benefits on their own record</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse receives benefits on your record, they can switch to their own retirement benefit as early as age 62. This assumes they're eligible for retirement benefits and their retirement rate is higher than their rate as a widow, widower, or surviving divorced spouse.</p>\n<p>In many cases, a widow or widower can begin receiving one benefit at a reduced rate and then, at full retirement age, switch to the other benefit at an unreduced rate.</p>\n<div>\n<a>Full retirement age for retirement benefits</a> may not match full retirement age for survivors benefits.\n </div>\n</section>\n</div>\n<div>\n<a>If they will also receive a pension based on work not covered by Social Security</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse will also receive a pension based on work not covered by Social Security, such as government or foreign work, <a>their Social Security benefits as a survivor may be affected</a>.</p>\n</section>\n</div>\n</section>\n</article>\n</div>\n</section>\n</main>"
}
```
Sample data instance for `dialogue_domain` :
```
{
"dial_id": "8df07b7a98990db27c395cb1f68a962e",
"domain": "dmv",
"turns": [
{
"turn_id": 1,
"role": "user",
"da": "query_condition",
"references": [
{
"id_sp": "4",
"label": "precondition",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
}
],
"utterance": "Hello, I forgot o update my address, can you help me with that?"
},
{
"turn_id": 2,
"role": "agent",
"da": "respond_solution",
"references": [
{
"id_sp": "6",
"label": "solution",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
},
{
"id_sp": "7",
"label": "solution",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
}
],
"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles."
},
{
"turn_id": 3,
"role": "user",
"da": "query_solution",
"references": [
{
"id_sp": "56",
"label": "solution",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
}
],
"utterance": "Can I do my DMV transactions online?"
}
]
}
```
### Data Fields
- `document_domain` contains the documents that are indexed by key `domain` and `doc_id` . Each document instance includes the following,
- `domain`: the domain of the document;
- `doc_id`: the ID of a document;
- `title`: the title of the document;
- `doc_text`: the text content of the document (without HTML markups);
- `spans`: key-value pairs of all spans in the document, with `id_sp` as key. Each span includes the following,
- `id_sp`: the id of a span as noted by `text_id` in `doc_html_ts`;
- `start_sp`/ `end_sp`: the start/end position of the text span in `doc_text`;
- `text_sp`: the text content of the span.
- `id_sec`: the id of the (sub)section (e.g. `<p>`) or title (`<h2>`) that contains the span.
- `start_sec` / `end_sec`: the start/end position of the (sub)section in `doc_text`.
- `text_sec`: the text of the (sub)section.
- `title`: the title of the (sub)section.
- `parent_titles`: the parent titles of the `title`.
- `doc_html_ts`: the document content with HTML markups and the annotated spans that are indicated by `text_id` attribute, which corresponds to `id_sp`.
- `doc_html_raw`: the document content with HTML markups and without span annotations.
- `dialogue_domain`
Each dialogue instance includes the following,
- `dial_id`: the ID of a dialogue;
- `domain`: the domain of the document;
- `turns`: a list of dialogue turns. Each turn includes,
- `turn_id`: the time order of the turn;
- `role`: either "agent" or "user";
- `da`: dialogue act;
- `references`: a list of spans with `id_sp` , `label` and `doc_id`. `references` is empty if a turn is for indicating previous user query not answerable or irrelevant to the document. **Note** that labels "*precondition*"/"*solution*" are fuzzy annotations that indicate whether a span is for describing a conditional context or a solution.
- `utterance`: the human-generated utterance based on the dialogue scene.
- `multidoc2dial`
Each dialogue instance includes the following,
- `id`: the ID of a QA instance
- `title`: the title of the relevant document;
- `context`: the text content of the relevant document (without HTML markups).
- `question`: user query;
- `da`: dialogue act;
- `answers`: the answers that are grounded in the associated document;
- `text`: the text content of the grounding span;
- `answer_start`: the start position of the grounding span in the associated document (context);
- `utterance`: the human-generated utterance based on the dialogue scene.
- `domain`: domain of the relevant document;
### Data Splits
Training, dev and test split for default configuration `multidoc2dial`, with respectively 21451, 4201 and 5 examples,
- Training & dev split for dialogue domain, with 3474 and 661 examples,
- Training split only for document domain, with 488 examples.
## 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
Song Feng, Siva Sankalp Patel, Hui Wan, Sachindra Joshi
### Licensing Information
Creative Commons Attribution 3.0 Unported
### Citation Information
```bibtex
@inproceedings{feng2021multidoc2dial,
title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},
author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},
booktitle={EMNLP},
year={2021}
}
```
### Contributions
Thanks to [@songfeng](https://github.com/songfeng) and [@sivasankalpp](https://github.com/sivasankalpp) for adding this dataset. | [
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0.5253462791442871,
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] |
lmsys/toxic-chat | lmsys | 2023-11-13T18:48:51Z | 640 | 47 | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2310.17389",
"region:us"
] | null | 2023-10-26T13:52:48Z | ---
license: cc-by-nc-4.0
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: toxic-chat_annotation_train.csv
- split: test
path: toxic-chat_annotation_test.csv
---
## Content
This dataset contains toxicity annotations on 10K user prompts collected from the Vicuna [online demo](https://chat.lmsys.org/).
We utilize a human-AI collaborative annotation framework to guarantee the quality of annotation while maintaining a feasible annotation workload.
The details of data collection, pre-processing, and annotation can be found in our [paper](https://arxiv.org/abs/2310.17389).
We believe that ToxicChat can be a valuable resource to drive further advancements toward building a safe and healthy environment for user-AI interactions.
**Basic Statistics**
| Key | Value |
| --- | --- |
| # User Prompts | 10,165 |
| # Human Annotations | 5,634 |
| % Toxic Examples | 7.18% |
| % Jailbreaking Examples | 1.78% |
## Disclaimer and Terms
- This dataset is based on the user query collected from the Vicuna online demo.
The Vicuna demo is fully anonymous for the users and also highlights the possible reuse of the user query data.
We have carefully gone through the data and taken out anything that could have personal information in it.
However, there is still a chance that some personal information might be left in the data.
If you come across anything in the data that you think should not be made public, please let us know right away.
- Safety and Moderation: **This dataset may contain racism, sexuality, or other undesired content.**
Before the annotation, the annotators are first notified about the toxic data that they will be annotated.
Verbal agreements were obtained before annotation.
- Non-Endorsement: Statements or opinions made in this dataset **do not reflect** the views of researchers or institutions involved in the data collection effort.
- Legal Compliance: Users of this data are responsible for ensuring its appropriate use.
The dataset should not be utilized for training dialogue agents, or any other applications, in manners that conflict with legal and ethical standards.
- Non-Identification: Users of this data agree to not attempt to determine the identity of individuals in this dataset.
## License
Both the user prompts and the model outputs are licensed under CC-BY-NC-4.0.
## Citation
```
@misc{lin2023toxicchat,
title={ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation},
author={Zi Lin and Zihan Wang and Yongqi Tong and Yangkun Wang and Yuxin Guo and Yujia Wang and Jingbo Shang},
year={2023},
eprint={2310.17389},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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EMBO/biolang | EMBO | 2023-01-11T15:31:53Z | 639 | 0 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n>1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | [
"text-generation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n>1M
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for BioLang
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [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)
- [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://sourcedata.embo.org
- **Repository:** https://github.com/source-data/soda-roberta
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email protected]
- **Download Size:** 5_299_878_661
### Dataset Summary
BioLang is a dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology. The dataset can be used for random masked language modeling or for language modeling using only specific part-of-speech maksing. More details on generation and use of the dataset at https://github.com/source-data/soda-roberta .
### Supported Tasks and Leaderboards
- `MLM`: masked language modeling
- `DET`: part-of-speach masked language model, with determinants (`DET`) tagged
- `SMALL`: part-of-speech masked language model, with "small" words (`DET`, `CCONJ`, `SCONJ`, `ADP`, `PRON`) tagged
- `VERB`: part-of-speach masked language model, with verbs (`VERB`) tagged
### Languages
English
## Dataset Structure
### Data Instances
```json
{
"input_ids":[
0, 2444, 6997, 46162, 7744, 35, 20632, 20862, 3457, 36, 500, 23858, 29, 43, 32, 3919, 716, 15, 49, 4476, 4, 1398, 6, 52, 1118, 5, 20862, 819, 9, 430, 23305, 248, 23858, 29, 4, 256, 40086, 104, 35, 1927, 1069, 459, 1484, 58, 4776, 13, 23305, 634, 16706, 493, 2529, 8954, 14475, 73, 34263, 6, 4213, 718, 833, 12, 24291, 4473, 22500, 14475, 73, 510, 705, 73, 34263, 6, 5143, 4313, 2529, 8954, 14475, 73, 34263, 6, 8, 5143, 4313, 2529, 8954, 14475, 248, 23858, 29, 23, 4448, 225, 4722, 2392, 11, 9341, 261, 4, 49043, 35, 96, 746, 6, 5962, 9, 38415, 4776, 408, 36, 3897, 4, 398, 8871, 56, 23305, 4, 20, 15608, 21, 8061, 6164, 207, 13, 70, 248, 23858, 29, 6, 150, 5, 42561, 21, 8061, 5663, 207, 13, 80, 3457, 4, 509, 1296, 5129, 21567, 3457, 36, 398, 23528, 8748, 22065, 11654, 35, 7253, 15, 49, 4476, 6, 70, 3457, 4682, 65, 189, 28, 5131, 13, 23305, 9726, 4, 2
],
"label_ids": [
"X", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "NOUN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "AUX", "VERB", "VERB", "ADP", "DET", "NOUN", "PUNCT", "ADV", "PUNCT", "PRON", "VERB", "DET", "ADJ", "NOUN", "ADP", "ADJ", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "ADJ", "PUNCT", "NOUN", "NOUN", "NOUN", "NOUN", "AUX", "VERB", "ADP", "NOUN", "VERB", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "CCONJ", "ADJ", "PROPN", "PROPN", "PROPN", "PROPN", "NOUN", "NOUN", "NOUN", "ADP", "PROPN", "PROPN", "PROPN", "PROPN", "ADP", "PROPN", "PROPN", "PUNCT", "PROPN", "PUNCT", "ADP", "NOUN", "PUNCT", "NUM", "ADP", "NUM", "VERB", "NOUN", "PUNCT", "NUM", "NUM", "NUM", "NOUN", "AUX", "NOUN", "PUNCT", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "DET", "NOUN", "NOUN", "NOUN", "PUNCT", "SCONJ", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "NUM", "NOUN", "PUNCT", "NUM", "NOUN", "VERB", "ADJ", "NOUN", "PUNCT", "NUM", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "VERB", "ADP", "DET", "NOUN", "PUNCT", "DET", "NOUN", "SCONJ", "PRON", "VERB", "AUX", "VERB", "ADP", "NOUN", "NOUN", "PUNCT", "X"
],
"special_tokens_mask": [
1, 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, 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, 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, 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, 1
]
}
```
### Data Fields
`MLM`:
- `input_ids`: a `list` of `int32` features.
- `special_tokens_mask`: a `list` of `int8` features.
`DET`, `VERB`, `SMALL`:
- `input_ids`: a `list` of `int32` features.
- `tag_mask`: a `list` of `int8` features.
### Data Splits
- `train`:
- features: ['input_ids', 'special_tokens_mask'],
- num_rows: 12_005_390
- `test`:
- features: ['input_ids', 'special_tokens_mask'],
- num_rows: 37_112
- `validation`:
- features: ['input_ids', 'special_tokens_mask'],
- num_rows: 36_713
## Dataset Creation
### Curation Rationale
The dataset was assembled to train language models in the field of cell and molecular biology. To expand the size of the dataset and to include many examples with highly technical language, abstracts were complemented with figure legends (or figure 'captions').
### Source Data
#### Initial Data Collection and Normalization
The xml content of papers were downloaded in January 2021 from the open access section of [EuropePMC]("https://europepmc.org/downloads/openaccess"). Figure legends and abstracts were extracted from the JATS XML, tokenized with the `roberta-base` tokenizer and part-of-speech tagged with Spacy's `en_core_web_sm` model (https://spacy.io).
More details at https://github.com/source-data/soda-roberta
#### Who are the source language producers?
Experts scientists.
### Annotations
#### Annotation process
Part-of-speech was tagged automatically.
#### Who are the annotators?
Spacy's `en_core_web_sm` model (https://spacy.io) was used for part-of-speech tagging.
### 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
Thomas Lemberger
### Licensing Information
CC-BY 4.0
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@tlemberger](https://github.com/tlemberger) for adding this dataset.
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] |
causal-lm/instructions | causal-lm | 2023-07-27T04:32:33Z | 639 | 4 | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"region:us"
] | [
"text-generation"
] | 2023-06-27T11:35:44Z | ---
language: en
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 24084342913.39447
num_examples: 19176870
- name: validation
num_bytes: 2830664216.3492484
num_examples: 2317180
download_size: 14194738316
dataset_size: 26915007129.743717
license: apache-2.0
task_categories:
- text-generation
size_categories:
- 10M<n<100M
---
# Merged Instructions Dataset
Merged Dataset for the response of instructions. | [
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ai4bharat/IndicCOPA | ai4bharat | 2022-12-15T11:34:32Z | 637 | 1 | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:extended|xcopa",
"language:as",
"language:bn",
"language:en",
"language:gom",
"language:gu",
"language:hi",
"language:kn",
"language:mai",
"language:ml",
"language:mr",
"language:ne",
"language:or",
"language:pa",
"language:sa",
"language:sat",
"language:sd",
"language:ta",
"language:te",
"language:ur",
"license:cc-by-4.0",
"region:us"
] | [
"multiple-choice"
] | 2022-09-20T08:18:35Z | ---
annotations_creators:
- expert-generated
language:
- as
- bn
- en
- gom
- gu
- hi
- kn
- mai
- ml
- mr
- ne
- or
- pa
- sa
- sat
- sd
- ta
- te
- ur
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: IndicXCOPA
size_categories:
- 1K<n<10K
source_datasets:
- extended|xcopa
tags: []
task_categories:
- multiple-choice
task_ids:
- multiple-choice-qa
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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EleutherAI/hendrycks_ethics | EleutherAI | 2023-11-02T14:48:16Z | 637 | 1 | [
"license:other",
"region:us"
] | null | 2023-07-05T20:37:47Z | ---
license: other
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open-llm-leaderboard/details_huggyllama__llama-30b | open-llm-leaderboard | 2023-09-16T23:45:08Z | 637 | 0 | [
"region:us"
] | null | 2023-08-19T23:04:08Z | ---
pretty_name: Evaluation run of huggyllama/llama-30b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [huggyllama/llama-30b](https://huggingface.co/huggyllama/llama-30b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 122 configuration, each one coresponding to one of\
\ the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can\
\ be found as a specific split in each configuration, the split being named using\
\ the timestamp of the run.The \"train\" split is always pointing to the latest\
\ results.\n\nAn additional configuration \"results\" store all the aggregated results\
\ of the run (and is used to compute and display the agregated metrics on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_huggyllama__llama-30b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-16T23:44:55.901768](https://huggingface.co/datasets/open-llm-leaderboard/details_huggyllama__llama-30b/blob/main/results_2023-09-16T23-44-55.901768.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0014681208053691276,\n\
\ \"em_stderr\": 0.0003921042190298701,\n \"f1\": 0.06332634228187943,\n\
\ \"f1_stderr\": 0.0013742294190200051,\n \"acc\": 0.47445656434133393,\n\
\ \"acc_stderr\": 0.010516415781576863\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.0003921042190298701,\n\
\ \"f1\": 0.06332634228187943,\n \"f1_stderr\": 0.0013742294190200051\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14859742228961334,\n \
\ \"acc_stderr\": 0.009797503180527876\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.011235328382625849\n\
\ }\n}\n```"
repo_url: https://huggingface.co/huggyllama/llama-30b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|arc:challenge|25_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|arc:challenge|25_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_16T23_44_55.901768
path:
- '**/details_harness|drop|3_2023-09-16T23-44-55.901768.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-16T23-44-55.901768.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_16T23_44_55.901768
path:
- '**/details_harness|gsm8k|5_2023-09-16T23-44-55.901768.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-16T23-44-55.901768.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hellaswag|10_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hellaswag|10_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-19T23:03:51.753289.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-19T23:03:51.753289.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-08-19T23:03:51.753289.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-19T23:03:51.753289.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
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- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T17:40:29.405074.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T17:40:29.405074.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T17:40:29.405074.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T17:40:29.405074.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T17:40:29.405074.parquet'
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- split: latest
path:
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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- split: 2023_08_23T17_40_29.405074
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-19T23:03:51.753289.parquet'
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path:
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path:
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- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-19T23:03:51.753289.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
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- split: 2023_08_23T17_40_29.405074
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-19T23:03:51.753289.parquet'
- split: 2023_08_23T17_40_29.405074
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T17:40:29.405074.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T17:40:29.405074.parquet'
- config_name: harness_winogrande_5
data_files:
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path:
- '**/details_harness|winogrande|5_2023-09-16T23-44-55.901768.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-16T23-44-55.901768.parquet'
- config_name: original_mmlu_5
data_files:
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path:
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path:
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- config_name: original_mmlu_abstract_algebra_5
data_files:
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path:
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- split: latest
path:
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data_files:
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path:
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path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_astronomy_5
data_files:
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path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_business_ethics_5
data_files:
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path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_clinical_knowledge_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_college_biology_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_college_chemistry_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_college_computer_science_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_college_mathematics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_college_medicine_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_college_physics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_computer_security_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_conceptual_physics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_econometrics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_electrical_engineering_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_elementary_mathematics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_formal_logic_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_global_facts_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_biology_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_chemistry_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_computer_science_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_european_history_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_geography_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_government_and_politics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_macroeconomics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_mathematics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_microeconomics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_physics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_psychology_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_statistics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_us_history_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_high_school_world_history_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_human_aging_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_human_sexuality_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_international_law_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_jurisprudence_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_logical_fallacies_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_machine_learning_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_management_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:management|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:management|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_marketing_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_medical_genetics_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_miscellaneous_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_moral_disputes_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_moral_scenarios_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_nutrition_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_philosophy_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_prehistory_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_professional_accounting_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_professional_law_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_professional_medicine_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_professional_psychology_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_public_relations_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_security_studies_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_sociology_5
data_files:
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path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_us_foreign_policy_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:06:09.731721.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T20_06_09.731721
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:06:09.731721.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:06:09.731721.parquet'
- config_name: results
data_files:
- split: 2023_08_19T23_03_51.753289
path:
- results_2023-08-19T23:03:51.753289.parquet
- split: 2023_08_23T17_40_29.405074
path:
- results_2023-08-23T17:40:29.405074.parquet
- split: 2023_08_28T20_06_09.731721
path:
- results_2023-08-28T20:06:09.731721.parquet
- split: 2023_09_16T23_44_55.901768
path:
- results_2023-09-16T23-44-55.901768.parquet
- split: latest
path:
- results_2023-09-16T23-44-55.901768.parquet
---
# Dataset Card for Evaluation run of huggyllama/llama-30b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/huggyllama/llama-30b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [huggyllama/llama-30b](https://huggingface.co/huggyllama/llama-30b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_huggyllama__llama-30b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T23:44:55.901768](https://huggingface.co/datasets/open-llm-leaderboard/details_huggyllama__llama-30b/blob/main/results_2023-09-16T23-44-55.901768.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0014681208053691276,
"em_stderr": 0.0003921042190298701,
"f1": 0.06332634228187943,
"f1_stderr": 0.0013742294190200051,
"acc": 0.47445656434133393,
"acc_stderr": 0.010516415781576863
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.0003921042190298701,
"f1": 0.06332634228187943,
"f1_stderr": 0.0013742294190200051
},
"harness|gsm8k|5": {
"acc": 0.14859742228961334,
"acc_stderr": 0.009797503180527876
},
"harness|winogrande|5": {
"acc": 0.8003157063930545,
"acc_stderr": 0.011235328382625849
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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sagnikrayc/mctest | sagnikrayc | 2022-10-25T00:16:37Z | 636 | 2 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"explanations-in-question-answering",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mctest
language_bcp47:
- en-US
tags:
- explanations-in-question-answering
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/mcobzarenco/mctest/)
- **Paper:** [MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text](https://www.aclweb.org/anthology/D13-1020.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Microsoft Research License Agreement.
### Citation Information
[More Information Needed]
### Contributions
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] |
distil-whisper/librispeech_asr | distil-whisper | 2023-09-25T10:30:13Z | 636 | 0 | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc-by-4.0",
"region:us"
] | [
"automatic-speech-recognition"
] | 2023-03-29T12:53:48Z | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: LibriSpeech ASR
---
# Distil Whisper: LibriSpeech ASR
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. 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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kor_nlu | null | 2023-01-25T14:33:57Z | 635 | 1 | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|snli",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:2004.03289",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
- machine-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|snli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
pretty_name: KorNlu
dataset_info:
- config_name: nli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 80135707
num_examples: 550146
- name: validation
num_bytes: 318170
num_examples: 1570
- name: test
num_bytes: 1047250
num_examples: 4954
download_size: 80030037
dataset_size: 81501127
- config_name: sts
features:
- name: genre
dtype:
class_label:
names:
'0': main-news
'1': main-captions
'2': main-forum
'3': main-forums
- name: filename
dtype:
class_label:
names:
'0': images
'1': MSRpar
'2': MSRvid
'3': headlines
'4': deft-forum
'5': deft-news
'6': track5.en-en
'7': answers-forums
'8': answer-answer
- name: year
dtype:
class_label:
names:
'0': '2017'
'1': '2016'
'2': '2013'
'3': 2012train
'4': '2014'
'5': '2015'
'6': 2012test
- name: id
dtype: int32
- name: score
dtype: float32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 1056664
num_examples: 5703
- name: validation
num_bytes: 305009
num_examples: 1471
- name: test
num_bytes: 249671
num_examples: 1379
download_size: 1603824
dataset_size: 1611344
---
# 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:** [Github](https://github.com/kakaobrain/KorNLUDatasets)
- **Repository:** [Github](https://github.com/kakaobrain/KorNLUDatasets)
- **Paper:** [Arxiv](https://arxiv.org/abs/2004.03289)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. | [
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ccdv/mediasum | ccdv | 2022-10-25T10:56:04Z | 635 | 5 | [
"task_categories:summarization",
"task_categories:text2text-generation",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"conditional-text-generation",
"region:us"
] | [
"summarization",
"text2text-generation"
] | 2022-05-21T12:29:19Z | ---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# MediaSum dataset for summarization
Summarization dataset copied from [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://github.com/zcgzcgzcg1/MediaSum)
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/mediasum": ("document", "summary")
```
# Configs
4 possibles configs:
- `roberta` will concatenate documents with "\</s\>"
- `newline` will concatenate documents with "\n"
- `bert` will concatenate documents with "[SEP]"
- `list` will return the list of documents instead of a single string
Add `_prepended` to config name to prepend the speaker name before each dialogue: `speaker: text` \
Default is `roberta_prepended` (compatible with BART).
### Data Fields
- `id`: paper id
- `document`: a string/list containing the body of a set of documents
- `summary`: a string containing the abstract of the set
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
| Dataset Split | Number of Instances |
| ------------- | --------------------|
| Train | 443596 |
| Validation | 10000 |
| Test | 10000 |
# Cite original article
```
@article{zhu2021mediasum,
title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
journal={arXiv preprint arXiv:2103.06410},
year={2021}
}
``` | [
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yuvalkirstain/pickapic_v1_no_images | yuvalkirstain | 2023-04-16T14:53:35Z | 635 | 0 | [
"region:us"
] | null | 2023-04-16T14:52:20Z | ---
dataset_info:
features:
- name: are_different
dtype: bool
- name: best_image_uid
dtype: string
- name: caption
dtype: string
- name: created_at
dtype: timestamp[ns]
- name: has_label
dtype: bool
- name: image_0_uid
dtype: string
- name: image_0_url
dtype: string
- name: image_1_uid
dtype: string
- name: image_1_url
dtype: string
- name: label_0
dtype: float64
- name: label_1
dtype: float64
- name: model_0
dtype: string
- name: model_1
dtype: string
- name: ranking_id
dtype: int64
- name: user_id
dtype: int64
- name: num_example_per_prompt
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 308923563
num_examples: 583747
- name: validation
num_bytes: 8759568
num_examples: 17439
- name: test
num_bytes: 7194410
num_examples: 14073
- name: validation_unique
num_bytes: 248229
num_examples: 500
- name: test_unique
num_bytes: 256313
num_examples: 500
download_size: 175013617
dataset_size: 325382083
---
# Dataset Card for "pick_a_pic_v1_no_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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yerevann/coco-karpathy | yerevann | 2022-10-31T11:24:01Z | 634 | 3 | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"language:en",
"coco",
"image-captioning",
"region:us"
] | [
"image-to-text"
] | 2022-09-18T22:50:19Z | ---
language:
- en
task_categories:
- image-to-text
task_ids:
- image-captioning
pretty_name: COCO Karpathy split
tags:
- coco
- image-captioning
---
# Dataset Card for "yerevann/coco-karpathy"
The Karpathy split of COCO for image captioning.
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0x7194633/bashirov-messages-v2.1 | 0x7194633 | 2023-11-15T12:18:32Z | 634 | 0 | [
"region:us"
] | null | 2023-11-15T12:18:28Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4492439
num_examples: 36925
download_size: 1681064
dataset_size: 4492439
---
# Dataset Card for "bashirov-messages-v2.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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humicroedit | null | 2023-06-01T14:59:51Z | 633 | 2 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"funnier-headline-identification",
"funniness-score-prediction",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: humicroedit
pretty_name: Humicroedit
tags:
- funnier-headline-identification
- funniness-score-prediction
dataset_info:
- config_name: subtask-1
features:
- name: id
dtype: string
- name: original
dtype: string
- name: edit
dtype: string
- name: grades
dtype: string
- name: meanGrade
dtype: float32
splits:
- name: train
num_bytes: 1058589
num_examples: 9652
- name: test
num_bytes: 332113
num_examples: 3024
- name: validation
num_bytes: 269083
num_examples: 2419
- name: funlines
num_bytes: 942376
num_examples: 8248
download_size: 1621456
dataset_size: 2602161
- config_name: subtask-2
features:
- name: id
dtype: string
- name: original1
dtype: string
- name: edit1
dtype: string
- name: grades1
dtype: string
- name: meanGrade1
dtype: float32
- name: original2
dtype: string
- name: edit2
dtype: string
- name: grades2
dtype: string
- name: meanGrade2
dtype: float32
- name: label
dtype:
class_label:
names:
'0': equal
'1': sentence1
'2': sentence2
splits:
- name: train
num_bytes: 2102667
num_examples: 9381
- name: test
num_bytes: 665087
num_examples: 2960
- name: validation
num_bytes: 535044
num_examples: 2355
- name: funlines
num_bytes: 451416
num_examples: 1958
download_size: 1621456
dataset_size: 3754214
config_names:
- subtask-1
- subtask-2
---
# 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:**[Humicroedit](https://www.cs.rochester.edu/u/nhossain/humicroedit.html)
- **Repository:**
- **Paper:**["President Vows to Cut Taxes Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines.](http://cs.rochester.edu/~nhossain/humicroedit-naacl-19.pdf)
- **Leaderboard:**
- **Point of Contact:**[[email protected]]
### Dataset Summary
This is the task dataset for SemEval-2020 Task 7: Assessing Humor in Edited News Headlines.
### Supported Tasks and Leaderboards
[Task Description Page](https://competitions.codalab.org/competitions/20970)
- Regression Task: In this task, given the original and the edited headline, the participant is required to predict the mean funniness of the edited headline. Success on this task is typically measured by achieving a *low* Mean Square Error.
- Predict the funnier of the two edited headlines: Given the original headline and two edited versions, the participant has to predict which edited version is the funnier of the two. Success on this task is typically measured by achieving a *high* accuracy.
### Languages
English
## Dataset Structure
### Data Instances
For subtask-1, i.e Given the original and the edited headline, predict the mean funniness of the edited headline.
```
{
'id': 1183,
'original': 'Kushner to visit <Mexico/> following latest trump tirades.',
'edit': 'therapist',
'grades': '33332',
'meanGrade': 2.8
}
```
For subtask-2, i.e Given the original headline and two edited versions, predict which edited version is the funnier of the two.
```
{
'id': 1183,
'original1': 'Gene Cernan , Last <Astronaut/> on the Moon , Dies at 82',
'edit1': 'Dancer',
'grades1': '1113',
'meanGrade1': 1.2,
'original2': 'Gene Cernan , Last Astronaut on the Moon , <Dies/> at 82',
'edit2': 'impregnated',
'grades2': '30001',
'meanGrade2': 0.8,
'label': 1
}
```
### Data Fields
For subtask-1
- `id`: Unique identifier of an edited headline.
- `original`: The headline with replaced word(s) identified with the </> tag.
- `edit`: The new word which replaces the word marked in </> tag in the original field.
- `grades`: 'grades' are the concatenation of all the grades by different annotators.
- `mean` is the mean of all the judges scores.
For subtask-2
- `id`: Unique identifier of an edited headline.
- `original1`: The original headline with replaced word(s) identified with </> tag.
- `edit1`: The new word which replaces the word marked in </> tag in the `original1` field.
- `grades1`: The concatenation of all the grades annotated by different annotators for sentence1.
- `meanGrade1` is the mean of all the judges scores for sentence1.
- `original2`: The original headline with replaced word(s) identified with </> tag.
- `edit2`: The new word which replaces the word marked in </> tag in the `original1` field.
- `grades2`: The concatenation of all the grades annotated by different annotators for the sentence2.
- `meanGrade2` is the mean of all the judges scores for sentence2.
- `label` is 1 if sentence1 is more humourous than sentence2,
2 if sentence 2 is more humorous than sentence1,
0 if both the sentences are equally humorous
### Data Splits
| Sub Task | Train | Dev | Test | Funlines|
| ----- | ------ | ---- | ---- |-----|
| Subtask-1:Regression | 9652 | 2419 | 3024| 8248 |
| Subtask-2: Funnier headline prediction| 9381 | 2355 | 2960| 1958 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Crowd-sourced the data by gamifying it as on the website funlines.co. Players rate the headlines on a scale of 0-4.
Players are scored based on their editing and rating, and they
are ranked on the game’s leaderboard page.
#### 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
```
@article{hossain2019president,
title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
author={Hossain, Nabil and Krumm, John and Gamon, Michael},
journal={arXiv preprint arXiv:1906.00274},
year={2019}
}```
### Contributions
Thanks to [@saradhix](https://github.com/saradhix) for adding this dataset. | [
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GEM/BiSECT | GEM | 2022-09-02T21:58:17Z | 633 | 2 | [
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:de",
"language:en",
"language:fr",
"language:es",
"license:other",
"region:us"
] | [
"simplification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- de
- en
- fr
- es
license:
- other
multilinguality:
- unknown
pretty_name: BiSECT
size_categories:
- unknown
source_datasets:
- original
task_categories:
- simplification
task_ids:
- unknown
---
# Dataset Card for GEM/BiSECT
## Dataset Description
- **Homepage:** https://github.com/mounicam/BiSECT
- **Repository:** https://github.com/mounicam/BiSECT/tree/main/bisect
- **Paper:** https://aclanthology.org/2021.emnlp-main.500/
- **Leaderboard:** N/A
- **Point of Contact:** Joongwon Kim, Mounica Maddela, Reno Kriz
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/BiSECT).
### Dataset Summary
This dataset is composed of 1 million complex sentences with the task to split and simplify them while retaining the full meaning. Compared to other simplification corpora, BiSECT requires more significant edits. BiSECT offers splits in English, German, French, and Spanish.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/BiSECT')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/BiSECT).
#### website
[Link](https://github.com/mounicam/BiSECT)
#### paper
[Link](https://aclanthology.org/2021.emnlp-main.500/)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Link](https://github.com/mounicam/BiSECT)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Link](https://github.com/mounicam/BiSECT/tree/main/bisect)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Link](https://aclanthology.org/2021.emnlp-main.500/)
#### 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{kim-etal-2021-bisect,
title = "{B}i{SECT}: Learning to Split and Rephrase Sentences with Bitexts",
author = "Kim, Joongwon and
Maddela, Mounica and
Kriz, Reno and
Xu, Wei and
Callison-Burch, Chris",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.500",
pages = "6193--6209"
}
```
#### 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 -->
Joongwon Kim, Mounica Maddela, Reno Kriz
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected], [email protected], [email protected]
#### 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 -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`, `German`, `French`, `Spanish, Castilian`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
other: Other license
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Split and Rephrase.
#### Add. License Info
<!-- info: What is the 'other' license of the dataset? -->
<!-- scope: periscope -->
The dataset is not licensed by itself, and the source OPUS data consists solely of publicly available parallel corpora.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Simplification
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
To rewrite a long, complex sentence into shorter, readable, meaning-equivalent sentences.
### Credit
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id` (string): a unique identifier for the instance
- `source_sentence` (string): sentence to be simplified
- `target_sentence` (string)" simplified text that was split and rephrased
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"gem_id": "bisect-train-0",
"source_sentence": "The report on the visit to Bhutan states that the small community has made the task of coordination less complex and success is manifested in the synchronized programming cycles which now apply to all but one of the agencies ( the World Health Organization ) .",
"target_sentence": "The report on the visit to Bhutan says that the small community has made the coordination work less complex . Success manifests itself in synchronized programming cycles that now apply to all but one organism ( the World Health Organization ) ."
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
For the main English BiSECT dataset, the splits are as follows: 1. Train (n=928440) 2. Validation (n=9079) 3. Test (n=583) Additional challenge sets were derived from the data presented in the paper. Please refer to the challenge set sections. The train/validation/test splits for other languages are as follows: German (n=184638/n=864/n=735) Spanish (n=282944/n=3638/n=3081) French (n=491035/n=2400/n=1036)
#### 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 -->
While all training data were derived from subsets of the OPUS corpora, different source subsets were used for training v.s., validation and testing. The training set comprised more web crawl data, whereas development and test sets comprised EMEA and EU texts. Details can be found in the BiSECT paper.
## 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 -->
Understanding long and complex sentences is challenging for both humans and NLP models. The BiSECT dataset helps facilitate more research on Split and Rephrase as a task within itself, as well as how it can benefit downstream NLP applications.
#### 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 -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
BiSECT is the largest available corpora for the Split and Rephrase task. In addition, it has been shown that BiSECT is of higher quality than previous Split and Rephrase corpora and contains a wider variety of splitting operations.
Most previous Split and Rephrase corpora (HSplit-Wiki, Cont-Benchmark, and Wiki-Benchmark) were manually written at a small scale and focused on evaluation, while the one corpus of comparable size, WikiSplit, contains around 25% of pairs contain significant errors. This is because Wikipedia editors are not only trying to split a sentence, but also often simultaneously modifying the sentence for other purposes, which results in changes of the initial meaning.
### 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 added`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
The original BiSECT training, validation, and test splits are maintained to ensure a fair comparison. Note that the original BiSECT test set was created by manually selecting 583 high-quality Split and Rephrase instances from 1000 random source-target pairs sampled from the EMEA and JRC-Acquis corpora from OPUS.
As the first challenge set, we include the HSPLIT-Wiki test set, containing 359 pairs. For each complex sentence, there are four reference splits; To ensure replicability, as reference splits, we again follow the BiSECT paper and present only the references from HSplit2-full.
In addition to the two evaluation sets used in the original BiSECT paper, we also introduce a second challenge set. For this, we initially consider all 7,293 pairs from the EMEA and JRC-Acquis corpora. From there, we classify each pair using the classification algorithm from Section 4.2 of the original BiSECT paper. The three classes are as follows:
1. Direct Insertion: when a long sentence l contains two independent clauses and requires only minor changes in order to make a fluent and meaning-preserving split s.
2. Changes near Split, when l contains one independent and one dependent clause, but modifications are restricted to the region where l is split.
3. Changes across Sentences, where major changes are required throughout l in order to create a fluent split s.
We keep only pairs labeled as Type 3, and after filtering out pairs with significant length differences (signaling potential content addition/deletion), we present a second challenge set of 1,798 pairs.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### 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 -->
The dataset can be downloaded from the original repository by the authors.
The original BiSECT paper proposes several transformer-based models that can be used as baselines, which also compares against Copy512, an LSTM-based model and the previous state-of-the-art.
The common metric used for automatic evaluation of Split and Rephrase, and sentence simplification more generally is SARI. The BiSECT paper also evaluates using BERTScore. Note that automatic evaluations tend to not correlate well with human judgments, so a human evaluation for quality is generally expected for publication. The original BiSECT paper provides templates for collecting quality annotations from Amazon Mechanical Turk.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Text comprehension (needed to generate meaning-equivalent output) and notions of complexity (what is more 'readable' in terms of syntactic structure, lexical choice, punctuation).
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`, `BERT-Score`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
SARI is a metric used for evaluating automatic text simplification systems. The metric compares the predicted simplified sentences against the reference and the source sentences. It explicitly measures the goodness of words that are added, deleted and kept by the system.
#### 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 -->
Existing automatic metrics, such as BLEU (Papineni et al., 2002) and SAMSA (Sulem et al., 2018),
are not optimal for the Split and Rephrase task as
they rely on lexical overlap between the output and
the target (or source) and underestimate the splitting capability of the models that rephrase often.
As such, the dataset creators focused on BERTScore (Zhang et al., 2020) and SARI (Xu et al., 2016). BERTScore captures meaning preservation and fluency
well (Scialom et al., 2021). SARI can provide three
separate F1/precision scores that explicitly measure the correctness of inserted, kept and deleted
n-grams when compared to both the source and
the target. The authors used an extended version of SARI
that considers lexical paraphrases of the reference.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
BiSECT was constructed to satisfy the need of a Split and Rephrase corpus that is both large-scale and high-quality. Most previous Split and Rephrase corpora (HSplit-Wiki, Cont-Benchmark, and Wiki-Benchmark) were manually written at a small scale and focused on evaluation, while the one corpus of comparable size, WikiSplit, contains around 25% of pairs contain significant errors. This is because Wikipedia editors are not only trying to split a sentence, but also often simultaneously modifying the sentence for other purposes, which results in changes of the initial meaning.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The goal of Split and Rephrase is to break down longer sentences into multiple shorter sentences, which has downstream applications for many NLP tasks, including machine translation and dependency parsing.
#### 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 -->
`Other`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
N/A.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
There is a range of topics spanning domains such as web crawl and government documents (European Parliament, United Nations, EMEA).
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
The construction of the BiSECT corpus relies on leveraging the sentence-level alignments from OPUS), a collection of bilingual parallel corpora over many language pairs. Given a target language A, this work extracts all 1-2 and 2-1 sentence alignments from parallel corpora between A and a set of foreign languages B.
Next, the foreign sentences are translated into English using Google Translate’s Web API service to obtain sentence alignments between a single long sentence and two corresponding split sentences, both in the desired language.
The authors further filtered the data in a hybrid fashion.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
hybrid
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
To remove noise, the authors remove pairs where the single long sentence (l) contains a token with a punctuation after the first two and before the last two alphabetic characters. The authors also removed instances where l contains more than one unconnected component in its dependency tree, generated via SpaCy.
### 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 -->
Since this data is collected from OPUS, all instances are already in the public domain.
### 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 -->
unlikely
#### 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 -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
The data as provided in GEMv2 is in English, which is a language with abundant existing resources. However, the original paper also provides Split and Rephrase pairs for French, Spanish, and German, while providing a framework for leveraging bilingual corpora from any language pair found within OPUS.
### 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 -->
The language produced in the dataset is limited to what is captured in the used subset of the OPUS corpora, which might not represent the full distribution of speakers from all locations. For example, the corpora used are from a limited set of relatively formal domains, so it is possible that high performance on the BiSECT test set may not transfer to more informal text.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
Since this data is collected from OPUS, all pairs are already in the public domain.
### 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 -->
`public domain`
#### 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 -->
`public domain`
### 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 creation of English BiSECT relies on translating non-English text back to English. While machine translation systems tend to perform well on high-resource languages, there is still a non-negligible chance that there these systems make errors; through a manual evaluation of a subset of BiSECT, it was found that 15% of pairs contained significant errors, while an additional 22% contained minor adequacy/fluency errors. This problem is exacerbated slightly when creating German BiSECT (22% significant errors, 24% minor errors), and these numbers would likely get larger if lower-resource languages were used.
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BeIR/climate-fever-qrels | BeIR | 2022-10-23T06:08:28Z | 633 | 0 | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | [
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-05T17:28:22Z | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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PKU-Alignment/BeaverTails | PKU-Alignment | 2023-10-17T11:47:53Z | 633 | 12 | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"moderation",
"rejection-sampling",
"llm",
"lm",
"human-feedback",
"arxiv:2307.04657",
"region:us"
] | [
"text-classification"
] | 2023-06-07T17:22:12Z | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- safe
- safety
- ai-safety
- moderation
- rejection-sampling
- llm
- lm
- human-feedback
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: 330k_train
path: round0/330k/train.jsonl.xz
- split: 330k_test
path: round0/330k/test.jsonl.xz
- split: 30k_train
path: round0/30k/train.jsonl.gz
- split: 30k_test
path: round0/30k/test.jsonl.gz
---
# Dataset Card for BeaverTails
BeaverTails is an AI safety-focused collection comprising a series of datasets.
This repository includes human-labeled data consisting of question-answer (QA) pairs, each identified with their corresponding harm categories.
It should be noted that a single QA pair can be associated with more than one category.
The 14 harm categories are defined as follows:
1. **Animal Abuse:** This involves any form of cruelty or harm inflicted on animals, including physical abuse, neglect, or any behavior that causes distress or harm to an animal. It also includes content that promotes such behavior.
1. **Child Abuse:** This encompasses any form of physical, emotional, or sexual abuse directed toward children. It can also include neglect, exploitation, or any behavior that harms a child or violates their rights. Content that promotes or glorifies such behavior also falls under this category.
1. **Controversial Topics, Politics:** This refers to discussions or statements related to controversial topics, political ideologies, or sensitive issues that could lead to disagreement or conflict. These topics can be divisive and could potentially cause harm if not handled respectfully and thoughtfully.
1. **Discrimination, Stereotype, Injustice:** Discrimination refers to treating people unfairly or unequally based on their personal characteristics or affiliations such as race, age, gender, religion, etc. Stereotyping involves holding simplified and generalized beliefs about particular groups, which often leads to prejudices and discrimination. Injustice pertains to unfair treatment or violation of the rights of individuals or groups.
1. **Drug Abuse, Weapons, Banned Substance:** This category includes the misuse or abuse of illegal drugs, prescription drugs, or any other banned substances. It also involves the unauthorized or illegal possession, use, or trade of weapons.
1. **Financial Crime, Property Crime, Theft:** This includes all forms of illegal activities that lead to financial loss such as fraud, embezzlement, money laundering, bribery, and other forms of corruption. Property crime and theft involve the act of taking someone's property without their consent.
1. **Hate Speech, Offensive Language:** This category encompasses any form of communication or content that expresses intense hostility or violence towards an individual or group based on aspects like race, religion, ethnicity, nationality, gender, sexual orientation, or any other defining characteristics. This also includes language that is considered vulgar, crude, or offensive.
1. **Misinformation Regarding ethics, laws, and safety:** This category involves the spreading of incorrect or misleading information related to ethical issues, laws, regulations, or any matter related to safety. Misinformation can cause harm by influencing people's beliefs or actions negatively.
1. **Non-Violent Unethical Behavior:** This refers to any behavior that is morally or ethically wrong, but does not involve violence. Examples can include dishonest behaviors like lying, cheating, deceiving, spreading rumors, betraying trust, or any other form of unethical conduct.
1. **Privacy Violation:** This refers to actions that invade an individual's privacy, such as unauthorized access, use, disclosure, or collection of personal data. It also includes stalking, doxing, or any other form of intrusion into someone's personal life without their consent.
1. **Self-Harm:** This includes any form of self-inflicted harm, whether physical or emotional, or any content that promotes, glorifies, or encourages self-harm. This can also include discussions of suicidal thoughts or plans.
1. **Sexually Explicit, Adult Content:** This category includes any content that depicts explicit sexual activities or adult themes that are considered inappropriate or offensive. This can include pornography, explicit sexual text or messages, and any other form of sexually explicit material.
1. **Terrorism, Organized Crime:** This pertains to any form of content or action related to terrorism or organized crime, including endorsing or promoting terrorist activities, participating in organized criminal activities, or spreading propaganda for such groups.
1. **Violence, Aiding and Abetting, Incitement:** This involves any form of physical harm, threat, or violent behavior towards individuals or groups. Aiding and abetting refers to the act of helping, supporting, or encouraging such violent behaviors or illegal activities. Incitement pertains to the act of provoking or stirring up harmful, violent, or illegal actions.
**Disclaimer**: The BeaverTails dataset and its family contain content that may be offensive or upsetting.
Topics covered in the dataset include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially distressing subject matter.
Please engage with the dataset responsibly and in accordance with your own personal risk tolerance.
The dataset is intended for research purposes, specifically for research aimed at creating safer and less harmful AI systems.
The views and opinions expressed in the dataset do not represent the views of the PKU-Alignment Team or any of its members.
It is important to emphasize that the dataset should not be used for training dialogue agents, as doing so may likely result in harmful model behavior.
The primary objective of this dataset is to facilitate research that could minimize or prevent the harm caused by AI systems.
## Usage
The code snippet below demonstrates how to load the QA-Classification dataset:
```python
from datasets import load_dataset
# Load the whole dataset
dataset = load_dataset('PKU-Alignment/BeaverTails')
# Load only the round 0 dataset
round0_dataset = load_dataset('PKU-Alignment/BeaverTails', data_dir='round0')
# Load the training dataset
train_dataset = load_dataset('PKU-Alignment/BeaverTails', split='train')
test_dataset = load_dataset('PKU-Alignment/BeaverTails', split='test')
```
## Papers
You can find more information in our Paper:
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
## Contact
The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails
## License
BeaverTails dataset and its family are released under the CC BY-NC 4.0 License.
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C-MTEB/CmedqaRetrieval-qrels | C-MTEB | 2023-07-28T09:40:21Z | 633 | 0 | [
"region:us"
] | null | 2023-07-28T09:40:18Z | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 595920
num_examples: 7449
download_size: 404005
dataset_size: 595920
---
# Dataset Card for "CmedqaRetrieval-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Random-Mary-Smith/port_data_random | Random-Mary-Smith | 2023-11-29T16:15:35Z | 632 | 0 | [
"size_categories:1M<n<10M",
"language:pt",
"license:mit",
"doi:10.57967/hf/1278",
"region:us"
] | null | 2023-10-05T18:41:55Z | ---
license: mit
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pretty_name: Portuguese Language Identification
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---
# Portuguese Varieties Identification
This repository contains the code for the paper "Enhancing Portuguese Varieties Identification with Domain-Agnostic Ensemble Approaches," submitted to EACL 2024. In this README, you can find more information about the corpus created to support the training of a model to identify the Portuguese variety of a given text.
The corpus is composed of four million documents across six textual domains (law, literature, news, politics, social media, web). In terms of models, we covered three types of techniques:
a) a baseline model using N-Grams and Naive Bayes;
b) a model using a pre-trained language model (BERT);
c) Anomaly-based language identification using autoencoders. To mitigate the variability introduced by the different domains, we used an ensemble approach to combine the predictions of domain-specialized models trained in isolation.
The work developed in this repository is part of the initiative **anonymized for EACL**
### Quickstart
```
# In /benchmarks folder
1. Install the requirements
pip install -r requirements.txt
2. Run the benchmarking script
./run.sh
```
### Corpus
The developed corpus is a composition of pre-existing datasets initially created for other NLP tasks that provide permissive licenses. The first release of the corpus is available on [Huggingface](https://huggingface.co/datasets/Random-Mary-Smith/port_data_random).
#### Data Sources
The corpus consists of the following datasets:
<p align="center">
<table>
<tr>
<th>Domain</th>
<th>Variety</th>
<th>Dataset</th>
<th>Original Task</th>
<th># Docs</th>
<th>License</th>
<th>Silver Labeled</th>
</tr>
<tr>
<td rowspan="5">Literature</td>
<td rowspan="3">PT-PT</td>
<td><a href="http://arquivopessoa.net/">Arquivo Pessoa</a></td>
<td>-</td>
<td>~4k</td>
<td>CC</td>
<td>✔</td>
</tr>
<tr>
<td><a href="https://www.gutenberg.org/ebooks/bookshelf/99">Gutenberg Project</a></td>
<td>-</td>
<td>6</td>
<td>CC</td>
<td>✔</td>
</tr>
<tr>
<td><a href="https://www.clul.ulisboa.pt/recurso/corpus-de-textos-literarios">LT-Corpus</a></td>
<td>-</td>
<td>56</td>
<td>ELRA END USER</td>
<td>✘</td>
</tr>
<tr>
<td rowspan="2">PT-BR</td>
<td><a href="https://www.kaggle.com/datasets/rtatman/brazilian-portuguese-literature-corpus">Brazilian Literature</a></td>
<td>Author Identification</td>
<td>81</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td>LT-Corpus</td>
<td>-</td>
<td>8</td>
<td>ELRA END USER</td>
<td>✘</td>
</tr>
<tr>
<td rowspan="2">Politics</td>
<td>PT-PT</td>
<td><a href="http://www.statmt.org/europarl/">Koehn (2005) Europarl</a></td>
<td>Machine Translation</td>
<td>~10k</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td>PT-BR</td>
<td>Brazilian Senate Speeches</td>
<td>-</td>
<td>~5k</td>
<td>CC</td>
<td>✔</td>
</tr>
<tr>
<td rowspan="2">Journalistic</td>
<td>PT-PT</td>
<td><a href="https://www.linguateca.pt/CETEMPublico/">CETEM Público</a></td>
<td>-</td>
<td>1M</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td>PT-BR</td>
<td><a href="https://www.linguateca.pt/CETEMFolha/">CETEM Folha</a></td>
<td>-</td>
<td>272k</td>
<td>CC</td>
<td>✘</td>
</tr>
<tr>
<td rowspan="3">Social Media</td>
<td>PT-PT</td>
<td><a href="https://www.aclweb.org/anthology/2021.ranlp-1.37/">Ramalho (2021)</a></td>
<td>Fake News Detection</td>
<td>2M</td>
<td>MIT</td>
<td>✔</td>
</tr>
<tr>
<td rowspan="2">PT-BR</td>
<td><a href="https://www.aclweb.org/anthology/2022.lrec-1.322/">Vargas (2022)</a></td>
<td>Hate Speech Detection</td>
<td>5k</td>
<td>CC-BY-NC-4.0</td>
<td>✘</td>
</tr>
<tr>
<td><a href="https://www.aclweb.org/anthology/2021.wlp-1.72/">Cunha (2021)</a></td>
<td>Fake News Detection</td>
<td>2k</td>
<td>GPL-3.0 license</td>
<td>✔</td>
</tr>
<tr>
<td>Web</td>
<td>BOTH</td>
<td><a href="https://www.aclweb.org/anthology/2020.lrec-1.451/">Ortiz-Suarez (2020)</a></td>
<td>-</td>
<td>10k</td>
<td>CC</td>
<td>✔</td>
</tr>
</table>
</p>
<p align="center">
<em>Table 1: Data Sources</em>
</p>
#####
Note: The dataset "Brazilian Senate Speeches" was created by the authors of this paper, using web crawling of the Brazilian Senate website and is available in the Huggingface repository.
#### Annotation Schema & Data Preprocessing Pipeline
We leveraged our knowledge of the Portuguese language to identify data sources that guaranteed mono-variety documents. However, this first release lacks any kind of supervision, so we cannot guarantee that all documents are mono-variety. In the future, we plan to release a second version of the corpus with a more robust annotation schema, combining automatic and manual annotation.
To improve the quality of the corpus, we applied a preprocessing pipeline to all documents. The pipeline consists of the following steps:
1. Remove all NaN values.
2. Remove all empty documents.
3. Remove all duplicated documents.
4. Apply the [clean_text](https://github.com/jfilter/clean-text) library to remove non-relevant information for language identification from the documents.
5. Remove all documents with a length significantly more than two standard deviations from the mean length of the documents in the corpus.
The pipeline is illustrated in Figure 1.
<p align="center">
<img src="assets/pipeline_lid.jpg" alt="Image Description">
</p>
<p align="center">
<em>Figure 1: Data Pre-Processing Pipeline</em>
</p>
#### Class Distribution
The class distribution of the corpus is presented in Table 2. The corpus is highly imbalanced, with the majority of the documents being from the journalistic domain. In the future, we plan to release a second version of the corpus with a more balanced distribution across the six domains. Depending on the imbalance of the textual domain, we used different strategies to perform train-validation-test splits. For the heavily imbalanced domains, we ensured a minimum of 100 documents for validation and 400 for testing. In the other domains, we applied a stratified split.
<p align="center">
<table>
<tr>
<th>Domain</th>
<th># PT-PT</th>
<th># PT-BR</th>
<th>Stratified</th>
</tr>
<tr>
<td>Politics</td>
<td>6500</td>
<td>4894</td>
<td>✓</td>
</tr>
<tr>
<td>Web</td>
<td>7960</td>
<td>21592</td>
<td>✓</td>
</tr>
<tr>
<td>Literature</td>
<td>18282</td>
<td>2772</td>
<td>✓</td>
</tr>
<tr>
<td>Law</td>
<td>392839</td>
<td>5766</td>
<td>✕</td>
</tr>
<tr>
<td>Journalistic</td>
<td>1494494</td>
<td>354180</td>
<td>✓</td>
</tr>
<tr>
<td>Social Media</td>
<td>2013951</td>
<td>6222</td>
<td>✕</td>
</tr>
</table>
</p>
<p align="center">
<em>Table 2: Class Balance across the six textual domains in both varieties of Portuguese.</em>
</p>
#### Future Releases & How to Contribute
We plan to release a second version of this corpus considering more textual domains and extending the scope to other Portuguese varieties. If you want to contribute to this corpus, please [contact us]().
### Models
We explored three Machine Learning based techniques founded on the corpus compiled to present a reliable language identification model capable of operating in a real-world scenario, independent of the textual domain. The three techniques are:
* A baseline model using N-Grams and Naive Bayes;
* A model using a pre-trained language model (BERT);
* Anomaly-based language identification using autoencoders.
To mitigate the impact of the variability introduced by the different domains, we used an ensemble approach to combine the predictions of domain-specialized models trained in isolation.
#### Baseline Model
The baseline model is a Naive Bayes classifier trained on the TF-IDF representation of the documents. The model is trained using the [scikit-learn](https://scikit-learn.org/stable/) library. After performing a grid search to find the best hyperparameters, we obtained the following results:
<table align="center">
<tr>
<th>Tokenizer</th>
<th># Features</th>
<th>max_df</th>
<th>Lowercase</th>
<th>Stop_words</th>
<th>Token_pattern</th>
<th>Ngram_range</th>
<th>Analyzer Algorithm</th>
</tr>
<tr>
<td>NLTK Portuguese</td>
<td>40000</td>
<td>1.0</td>
<td>False</td>
<td>NLTK Stopwords</td>
<td>None</td>
<td>(1, 2)</td>
<td>word</td>
</tr>
<tr>
<td>NLTK Portuguese</td>
<td>30000</td>
<td>1.0</td>
<td>False</td>
<td>NLTK Stopwords</td>
<td>None</td>
<td>(1, 5)</td>
<td>char_wb</td>
</tr>
</table>
<p align="center">
Table 3: Hyperparameters of the baseline model.
</p>
The F1-scores obtained by this technique are presented in Figure 2. The architecture strugles to generalize outside the domains used for training, compromising the performance of the model in a real-world scenario.
<p align="center">
<img src="assets/n_grams_isolated.jpg" alt="Image Description" style="width:70%;">
</p>
<p align="center">
<em>Figure 2: F1-Scores N-Grams based Model</em>
</p>
#### Autoencoder Model
The autoencoder model proposes a anomaly-detection approach to language identification. The model is composed of encoder-decoder feed-foward layers trained using BERTimbau embeddings as input. The results obtained by this technique are presented in Figure 3. This model presents intermidiate results between the baseline model and the BERT model.
<p align="center">
<img src="assets/autoencoder_isolated.jpg" alt="Image Description" style="width:70%;">
</p>
<p align="center">
<em>Figure 3: F1-Scores Autoencoder based Model</em>
</p>
#### BERT Model
The BERT model is a fine-tuned version of [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the corpus compiled. The model is trained using the [Huggingface](https://huggingface.co/) library. The results obtained by this technique are presented in Figure 4. This model is capable of generalizing to unseen domains, making it a good candidate for a real-world scenario.
<p align="center">
<img src="assets/bert_isolated.jpg" alt="Image Description" style="width:70%;">
</p>
<p align="center">
<em>Figure 4: F1-Scores BERT based Model</em>
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sedthh/gutenberg_english | sedthh | 2023-03-17T09:50:22Z | 631 | 5 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"project gutenberg",
"e-book",
"gutenberg.org",
"region:us"
] | [
"text-generation"
] | 2023-02-28T14:15:24Z | ---
dataset_info:
features:
- name: TEXT
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
dtype: string
splits:
- name: train
num_bytes: 18104255935
num_examples: 48284
download_size: 10748877194
dataset_size: 18104255935
license: mit
task_categories:
- text-generation
language:
- en
tags:
- project gutenberg
- e-book
- gutenberg.org
pretty_name: Project Gutenberg eBooks in English
size_categories:
- 10K<n<100K
---
# Dataset Card for Project Gutenber - English Language eBooks
A collection of non-english language eBooks (48284 rows, 80%+ of all english language books available on the site) from the Project Gutenberg site with metadata removed.
Originally colected for https://github.com/LAION-AI/Open-Assistant (follows the OpenAssistant training format)
The METADATA column contains catalogue meta information on each book as a serialized JSON:
| key | original column |
|----|----|
| language | - |
| text_id | Text# unique book identifier on Prject Gutenberg as *int* |
| title | Title of the book as *string* |
| issued | Issued date as *string* |
| authors | Authors as *string*, comma separated sometimes with dates |
| subjects | Subjects as *string*, various formats |
| locc | LoCC code as *string* |
| bookshelves | Bookshelves as *string*, optional |
## Source data
**How was the data generated?**
- A crawler (see Open-Assistant repository) downloaded the raw HTML code for
each eBook based on **Text#** id in the Gutenberg catalogue (if available)
- The metadata and the body of text are not clearly separated so an additional
parser attempts to split them, then remove transcriber's notes and e-book
related information from the body of text (text clearly marked as copyrighted or
malformed was skipped and not collected)
- The body of cleaned TEXT as well as the catalogue METADATA is then saved as
a parquet file, with all columns being strings
**Copyright notice:**
- Some of the books are copyrighted! The crawler ignored all books
with an english copyright header by utilizing a regex expression, but make
sure to check out the metadata for each book manually to ensure they are okay
to use in your country! More information on copyright:
https://www.gutenberg.org/help/copyright.html and
https://www.gutenberg.org/policy/permission.html
- Project Gutenberg has the following requests when using books without
metadata: _Books obtianed from the Project Gutenberg site should have the
following legal note next to them: "This eBook is for the use of anyone
anywhere in the United States and most other parts of the world at no cost and
with almost" no restrictions whatsoever. You may copy it, give it away or
re-use it under the terms of the Project Gutenberg License included with this
eBook or online at www.gutenberg.org. If you are not located in the United
States, you will have to check the laws of the country where you are located
before using this eBook."_ | [
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copenlu/fever_gold_evidence | copenlu | 2022-11-17T11:42:54Z | 630 | 5 | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|fever",
"language:en",
"license:cc-by-sa-3.0",
"license:gpl-3.0",
"region:us"
] | [
"text-classification"
] | 2022-04-02T14:52:35Z | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- machine-generated
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
paperswithcode_id: fever
pretty_name: ''
size_categories:
- 100K<n<1M
source_datasets:
- extended|fever
task_categories:
- text-classification
task_ids:
- fact-checking
---
# Dataset Card for fever_gold_evidence
## 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:** https://github.com/copenlu/fever-adversarial-attacks
- **Repository:** https://github.com/copenlu/fever-adversarial-attacks
- **Paper:** https://aclanthology.org/2020.emnlp-main.256/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Dataset for training classification-only fact checking with claims from the FEVER dataset.
This dataset is used in the paper "Generating Label Cohesive and Well-Formed Adversarial Claims", EMNLP 2020
The evidence is the gold evidence from the FEVER dataset for *REFUTE* and *SUPPORT* claims.
For *NEI* claims, we extract evidence sentences with the system in "Christopher Malon. 2018. Team Papelo: Transformer Networks at FEVER. In Proceedings of the
First Workshop on Fact Extraction and VERification (FEVER), pages 109113."
More details can be found in https://github.com/copenlu/fever-adversarial-attacks
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{atanasova-etal-2020-generating,
title = "Generating Label Cohesive and Well-Formed Adversarial Claims",
author = "Atanasova, Pepa and
Wright, Dustin and
Augenstein, Isabelle",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.256",
doi = "10.18653/v1/2020.emnlp-main.256",
pages = "3168--3177",
abstract = "Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent type of attack are universal adversarial triggers, which are individual n-grams that, when appended to instances of a class under attack, can trick a model into predicting a target class. However, for inference tasks such as fact checking, these triggers often inadvertently invert the meaning of instances they are inserted in. In addition, such attacks produce semantically nonsensical inputs, as they simply concatenate triggers to existing samples. Here, we investigate how to generate adversarial attacks against fact checking systems that preserve the ground truth meaning and are semantically valid. We extend the HotFlip attack algorithm used for universal trigger generation by jointly minimizing the target class loss of a fact checking model and the entailment class loss of an auxiliary natural language inference model. We then train a conditional language model to generate semantically valid statements, which include the found universal triggers. We find that the generated attacks maintain the directionality and semantic validity of the claim better than previous work.",
}
``` | [
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olm/olm-wikipedia-20221220 | olm | 2022-12-29T03:12:35Z | 630 | 3 | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"pretraining",
"language modelling",
"wikipedia",
"web",
"region:us"
] | [] | 2022-12-22T17:38:13Z | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: OLM December 2022 Wikipedia
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- pretraining
- language modelling
- wikipedia
- web
task_categories: []
task_ids: []
---
# Dataset Card for OLM December 2022 Wikipedia
Pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from a December 2022 Wikipedia snapshot. | [
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pvduy/sharegpt_alpaca_oa_vicuna_format | pvduy | 2023-04-29T18:37:21Z | 630 | 6 | [
"region:us"
] | null | 2023-04-29T18:36:44Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 494337138
num_examples: 324160
- name: test
num_bytes: 5944776
num_examples: 1499
download_size: 263071058
dataset_size: 500281914
---
# Dataset Card for "sharegpt_alpaca_oa_vicuna_format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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akkasi/ethos | akkasi | 2023-10-28T19:59:52Z | 630 | 0 | [
"region:us"
] | null | 2023-10-28T19:59:50Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: float64
- name: label2idx
dtype: string
- name: idx2label
dtype: string
splits:
- name: train
num_bytes: 165667
num_examples: 346
- name: test
num_bytes: 46805
num_examples: 87
download_size: 46734
dataset_size: 212472
---
# Dataset Card for "ethos_new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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offenseval_dravidian | null | 2023-06-01T14:59:49Z | 629 | 2 | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:kn",
"language:ml",
"language:ta",
"license:cc-by-4.0",
"offensive-language",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- kn
- ml
- ta
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Offenseval Dravidian
tags:
- offensive-language
dataset_info:
- config_name: tamil
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not_offensive
'1': Offensive_Untargetede
'2': Offensive_Targeted_Insult_Individual
'3': Offensive_Targeted_Insult_Group
'4': Offensive_Targeted_Insult_Other
'5': not-Tamil
splits:
- name: train
num_bytes: 4214801
num_examples: 35139
- name: validation
num_bytes: 526108
num_examples: 4388
download_size: 5040217
dataset_size: 4740909
- config_name: malayalam
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not_offensive
'1': Offensive_Untargetede
'2': Offensive_Targeted_Insult_Individual
'3': Offensive_Targeted_Insult_Group
'4': Offensive_Targeted_Insult_Other
'5': not-malayalam
splits:
- name: train
num_bytes: 1944857
num_examples: 16010
- name: validation
num_bytes: 249364
num_examples: 1999
download_size: 2276736
dataset_size: 2194221
- config_name: kannada
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not_offensive
'1': Offensive_Untargetede
'2': Offensive_Targeted_Insult_Individual
'3': Offensive_Targeted_Insult_Group
'4': Offensive_Targeted_Insult_Other
'5': not-Kannada
splits:
- name: train
num_bytes: 567119
num_examples: 6217
- name: validation
num_bytes: 70147
num_examples: 777
download_size: 678727
dataset_size: 637266
config_names:
- kannada
- malayalam
- tamil
---
# Dataset Card for Offenseval Dravidian
## 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://competitions.codalab.org/competitions/27654#learn_the_details
- **Repository:** https://competitions.codalab.org/competitions/27654#participate-get_data
- **Paper:** Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada
- **Leaderboard:** https://competitions.codalab.org/competitions/27654#results
- **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:[email protected])
### Dataset Summary
Offensive language identification is classification task in natural language processing (NLP) where the aim is to moderate and minimise offensive content in social media. It has been an active area of research in both academia and industry for the past two decades. There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text. This shared task presents a new gold standard corpus for offensive language identification of code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).
### Supported Tasks and Leaderboards
The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. The comment/post may contain more than one sentence but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.
### Languages
Code-mixed text in Dravidian languages (Tamil-English, Malayalam-English, and Kannada-English).
## Dataset Structure
### Data Instances
An example from the Tamil dataset looks as follows:
| text | label |
| :------ | :----- |
| படம் கண்டிப்பாக வெற்றி பெற வேண்டும் செம்ம vara level | Not_offensive |
| Avasara patutiya editor uhh antha bullet sequence aa nee soliruka kudathu, athu sollama iruntha movie ku konjam support aa surprise element aa irunthurukum | Not_offensive |
An example from the Malayalam dataset looks as follows:
| text | label |
| :------ | :----- |
| ഷൈലോക്ക് ന്റെ നല്ല ടീസർ ആയിട്ട് പോലും ട്രോളി നടന്ന ലാലേട്ടൻ ഫാൻസിന് കിട്ടിയൊരു നല്ലൊരു തിരിച്ചടി തന്നെ ആയിരിന്നു ബിഗ് ബ്രദർ ന്റെ ട്രെയ്ലർ | Not_offensive |
| Marana mass Ekka kku kodukku oru | Not_offensive |
An example from the Kannada dataset looks as follows:
| text | label |
| :------ | :----- |
| ನಿಜವಾಗಿಯೂ ಅದ್ಭುತ heartly heltidini... plz avrigella namma nimmellara supprt beku | Not_offensive |
| Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. | Not_offensive |
### Data Fields
Tamil
- `text`: Tamil-English code mixed comment.
- `label`: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Tamil"
Malayalam
- `text`: Malayalam-English code mixed comment.
- `label`: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-malayalam"
Kannada
- `text`: Kannada-English code mixed comment.
- `label`: integer from 0 to 5 that corresponds to these values: "Not_offensive", "Offensive_Untargetede", "Offensive_Targeted_Insult_Individual", "Offensive_Targeted_Insult_Group", "Offensive_Targeted_Insult_Other", "not-Kannada"
### Data Splits
| | train | validation |
|-----------|------:|-----------:|
| Tamil | 35139 | 4388 |
| Malayalam | 16010 | 1999 |
| Kannada | 6217 | 777 |
## Dataset Creation
### Curation Rationale
There is an increasing demand for offensive language identification on social media texts which are largely code-mixed. Code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts. Systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text.
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
Youtube users
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
This work is licensed under a [Creative Commons Attribution 4.0 International Licence](http://creativecommons.org/licenses/by/4.0/.)
### Citation Information
```
@article{chakravarthi-etal-2021-lre,
title = "DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text",
author = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Muralidaran, Vigneshwaran and
Jose, Navya and
Suryawanshi, Shardul and
Sherly, Elizabeth and
McCrae, John P",
journal={Language Resources and Evaluation},
publisher={Springer}
}
```
```
@inproceedings{dravidianoffensive-eacl,
title={Findings of the Shared Task on {O}ffensive {L}anguage {I}dentification in {T}amil, {M}alayalam, and {K}annada},
author={Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Jose, Navya and
M, Anand Kumar and
Mandl, Thomas and
Kumaresan, Prasanna Kumar and
Ponnsamy, Rahul and
V,Hariharan and
Sherly, Elizabeth and
McCrae, John Philip },
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = April,
year = "2021",
publisher = "Association for Computational Linguistics",
year={2021}
}
```
```
@inproceedings{hande-etal-2020-kancmd,
title = "{K}an{CMD}: {K}annada {C}ode{M}ixed Dataset for Sentiment Analysis and Offensive Language Detection",
author = "Hande, Adeep and
Priyadharshini, Ruba and
Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.peoples-1.6",
pages = "54--63",
abstract = "We introduce Kannada CodeMixed Dataset (KanCMD), a multi-task learning dataset for sentiment analysis and offensive language identification. The KanCMD dataset highlights two real-world issues from the social media text. First, it contains actual comments in code mixed text posted by users on YouTube social media, rather than in monolingual text from the textbook. Second, it has been annotated for two tasks, namely sentiment analysis and offensive language detection for under-resourced Kannada language. Hence, KanCMD is meant to stimulate research in under-resourced Kannada language on real-world code-mixed social media text and multi-task learning. KanCMD was obtained by crawling the YouTube, and a minimum of three annotators annotates each comment. We release KanCMD 7,671 comments for multitask learning research purpose.",
}
```
```
@inproceedings{chakravarthi-etal-2020-corpus,
title = "Corpus Creation for Sentiment Analysis in Code-Mixed {T}amil-{E}nglish Text",
author = "Chakravarthi, Bharathi Raja and
Muralidaran, Vigneshwaran and
Priyadharshini, Ruba and
McCrae, John Philip",
booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources association",
url = "https://www.aclweb.org/anthology/2020.sltu-1.28",
pages = "202--210",
abstract = "Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.",
language = "English",
ISBN = "979-10-95546-35-1",
}
```
```
@inproceedings{chakravarthi-etal-2020-sentiment,
title = "A Sentiment Analysis Dataset for Code-Mixed {M}alayalam-{E}nglish",
author = "Chakravarthi, Bharathi Raja and
Jose, Navya and
Suryawanshi, Shardul and
Sherly, Elizabeth and
McCrae, John Philip",
booktitle = "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources association",
url = "https://www.aclweb.org/anthology/2020.sltu-1.25",
pages = "177--184",
abstract = "There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff{'}s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.",
language = "English",
ISBN = "979-10-95546-35-1",
}
```
### Contributions
Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset. | [
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open-llm-leaderboard/details_ashercn97__manatee-7b | open-llm-leaderboard | 2023-09-17T18:42:54Z | 628 | 0 | [
"region:us"
] | null | 2023-08-18T11:57:35Z | ---
pretty_name: Evaluation run of ashercn97/manatee-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ashercn97/manatee-7b](https://huggingface.co/ashercn97/manatee-7b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ashercn97__manatee-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T18:42:42.384089](https://huggingface.co/datasets/open-llm-leaderboard/details_ashercn97__manatee-7b/blob/main/results_2023-09-17T18-42-42.384089.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0030411073825503355,\n\
\ \"em_stderr\": 0.0005638896908753201,\n \"f1\": 0.059899328859060456,\n\
\ \"f1_stderr\": 0.001397556369094792,\n \"acc\": 0.4077875240923591,\n\
\ \"acc_stderr\": 0.009650175391680019\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0030411073825503355,\n \"em_stderr\": 0.0005638896908753201,\n\
\ \"f1\": 0.059899328859060456,\n \"f1_stderr\": 0.001397556369094792\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07050796057619409,\n \
\ \"acc_stderr\": 0.0070515438139836135\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ashercn97/manatee-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|arc:challenge|25_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T18_42_42.384089
path:
- '**/details_harness|drop|3_2023-09-17T18-42-42.384089.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T18-42-42.384089.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T18_42_42.384089
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-42-42.384089.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-42-42.384089.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hellaswag|10_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T16:08:56.879142.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-02T16:08:56.879142.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-02T16:08:56.879142.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T18_42_42.384089
path:
- '**/details_harness|winogrande|5_2023-09-17T18-42-42.384089.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T18-42-42.384089.parquet'
- config_name: results
data_files:
- split: 2023_08_02T16_08_56.879142
path:
- results_2023-08-02T16:08:56.879142.parquet
- split: 2023_09_17T18_42_42.384089
path:
- results_2023-09-17T18-42-42.384089.parquet
- split: latest
path:
- results_2023-09-17T18-42-42.384089.parquet
---
# Dataset Card for Evaluation run of ashercn97/manatee-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ashercn97/manatee-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ashercn97/manatee-7b](https://huggingface.co/ashercn97/manatee-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ashercn97__manatee-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:42:42.384089](https://huggingface.co/datasets/open-llm-leaderboard/details_ashercn97__manatee-7b/blob/main/results_2023-09-17T18-42-42.384089.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0030411073825503355,
"em_stderr": 0.0005638896908753201,
"f1": 0.059899328859060456,
"f1_stderr": 0.001397556369094792,
"acc": 0.4077875240923591,
"acc_stderr": 0.009650175391680019
},
"harness|drop|3": {
"em": 0.0030411073825503355,
"em_stderr": 0.0005638896908753201,
"f1": 0.059899328859060456,
"f1_stderr": 0.001397556369094792
},
"harness|gsm8k|5": {
"acc": 0.07050796057619409,
"acc_stderr": 0.0070515438139836135
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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allegro/klej-cdsc-e | allegro | 2022-08-30T06:58:29Z | 627 | 0 | [
"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"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
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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msra_ner | null | 2023-01-25T14:40:51Z | 626 | 18 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:unknown",
"region:us"
] | [
"token-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MSRA NER
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
config_name: msra_ner
splits:
- name: train
num_bytes: 33323074
num_examples: 45001
- name: test
num_bytes: 2642934
num_examples: 3443
download_size: 15156606
dataset_size: 35966008
train-eval-index:
- config: msra_ner
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
metrics:
- type: seqeval
name: seqeval
---
# Dataset Card for MSRA NER
## 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/OYE93/Chinese-NLP-Corpus/tree/master/NER/MSRA)
- **Repository:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset. | [
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mozilla-foundation/common_voice_2_0 | mozilla-foundation | 2023-07-29T15:59:58Z | 623 | 1 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | [
"automatic-speech-recognition"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
br:
- 10K<n<100K
ca:
- 10K<n<100K
cnh:
- 1K<n<10K
cv:
- 1K<n<10K
cy:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 1K<n<10K
en:
- 100K<n<1M
eo:
- 10K<n<100K
es:
- 10K<n<100K
et:
- 1K<n<10K
eu:
- 10K<n<100K
fr:
- 100K<n<1M
ga-IE:
- 1K<n<10K
it:
- 10K<n<100K
kab:
- 100K<n<1M
ky:
- 10K<n<100K
mn:
- 1K<n<10K
nl:
- 10K<n<100K
ru:
- 10K<n<100K
rw:
- 1K<n<10K
sah:
- 1K<n<10K
sl:
- 1K<n<10K
sv-SE:
- 1K<n<10K
tr:
- 1K<n<10K
tt:
- 10K<n<100K
zh-CN:
- 1K<n<10K
zh-TW:
- 10K<n<100K
source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 2
language_bcp47:
- br
- ca
- cnh
- cv
- cy
- de
- dv
- en
- eo
- es
- et
- eu
- fr
- ga-IE
- it
- kab
- ky
- mn
- nl
- ru
- rw
- sah
- sl
- sv-SE
- tr
- tt
- zh-CN
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 2
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:[email protected])
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 2366 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 1872 validated hours in 28 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Basque, Breton, Catalan, Chinese (China), Chinese (Taiwan), Chuvash, Dhivehi, Dutch, English, Esperanto, Estonian, French, German, Hakha Chin, Irish, Italian, Kabyle, Kinyarwanda, Kyrgyz, Mongolian, Russian, Sakha, Slovenian, Spanish, Swedish, Tatar, Turkish, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_2_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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onestop_english | null | 2023-01-25T14:42:09Z | 622 | 15 | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:text-simplification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | [
"text2text-generation",
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
- text-classification
task_ids:
- multi-class-classification
- text-simplification
paperswithcode_id: onestopenglish
pretty_name: OneStopEnglish corpus
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ele
'1': int
'2': adv
splits:
- name: train
num_bytes: 2278043
num_examples: 567
download_size: 1228804
dataset_size: 2278043
---
# Dataset Card for OneStopEnglish 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://github.com/nishkalavallabhi/OneStopEnglishCorpus
- **Repository:** https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip
- **Paper:** https://www.aclweb.org/anthology/W18-0535.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OneStopEnglish is a corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
An instance example:
```
{
"text": "When you see the word Amazon, what’s the first thing you think...",
"label": 0
}
```
Note that each instance contains the full text of the document.
### Data Fields
- `text`: Full document text.
- `label`: Reading level of the document- ele/int/adv (Elementary/Intermediate/Advance).
### Data Splits
The OneStopEnglish dataset has a single _train_ split.
| Split | Number of instances |
|-------|--------------------:|
| train | 567 |
## 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
Creative Commons Attribution-ShareAlike 4.0 International License
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset. | [
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swedish_reviews | null | 2023-01-25T14:45:25Z | 622 | 3 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:sv",
"license:unknown",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- sv
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Swedish Reviews
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
config_name: plain_text
splits:
- name: test
num_bytes: 6296541
num_examples: 20697
- name: validation
num_bytes: 6359227
num_examples: 20696
- name: train
num_bytes: 18842891
num_examples: 62089
download_size: 11841056
dataset_size: 31498659
---
# Dataset Card for Swedish Reviews
## 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:** [swedish_reviews homepage](https://github.com/timpal0l/swedish-sentiment)
- **Repository:** [swedish_reviews repository](https://github.com/timpal0l/swedish-sentiment)
- **Point of Contact:** [Tim Isbister](mailto:[email protected])
### Dataset Summary
The dataset is scraped from various Swedish websites where reviews are present. The dataset consists of 103 482 samples split between `train`, `valid` and `test`. It is a sample of the full dataset, where this sample is balanced to the minority class (negative). The original data dump was heavly skewved to positive samples with a 95/5 ratio.
### Supported Tasks and Leaderboards
This dataset can be used to evaluate sentiment classification on Swedish.
### Languages
The text in the dataset is in Swedish.
## Dataset Structure
### Data Instances
What a sample looks like:
```
{
'text': 'Jag tycker huggingface är ett grymt project!',
'label': 1,
}
```
### Data Fields
- `text`: A text where the sentiment expression is present.
- `label`: a int representing the label `0`for negative and `1`for positive.
### Data Splits
The data is split into a training, validation and test set. The final split sizes are as follow:
| Train | Valid | Test |
| ------ | ----- | ---- |
| 62089 | 20696 | 20697 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Various Swedish websites with product reviews.
#### Initial Data Collection and Normalization
#### Who are the source language producers?
Swedish
### Annotations
[More Information Needed]
#### Annotation process
Automatically annotated based on user reviews on a scale 1-5, where 1-2 is considered `negative` and 4-5 is `positive`, 3 is skipped as it tends to be more neutral.
#### Who are the annotators?
The users who have been using the products.
### 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
[More Information Needed]
### Dataset Curators
The corpus was scraped by @timpal0l
### Licensing Information
Research only.
### Citation Information
No paper exists currently.
### Contributions
Thanks to [@timpal0l](https://github.com/timpal0l) for adding this dataset. | [
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cmu-mlsp/encodec_24khz-opt-125m-pretrained-ft-librispeech_asr | cmu-mlsp | 2023-10-24T13:39:34Z | 622 | 0 | [
"region:us"
] | null | 2023-10-24T13:29:07Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 24000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: audio_codes
sequence:
sequence: int64
splits:
- name: train
num_bytes: 17829358082.086
num_examples: 28539
- name: validation
num_bytes: 955281891.125
num_examples: 2703
- name: test
num_bytes: 958024726.5
num_examples: 2620
download_size: 18905275151
dataset_size: 19742664699.711
---
# Dataset Card for "encodec_24khz-opt-125m-pretrained-ft-librispeech_asr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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code_x_glue_cc_clone_detection_big_clone_bench | null | 2022-11-18T19:30:27Z | 619 | 4 | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
pretty_name: CodeXGlueCcCloneDetectionBigCloneBench
dataset_info:
features:
- name: id
dtype: int32
- name: id1
dtype: int32
- name: id2
dtype: int32
- name: func1
dtype: string
- name: func2
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 2888035757
num_examples: 901028
- name: validation
num_bytes: 1371399694
num_examples: 415416
- name: test
num_bytes: 1220662901
num_examples: 415416
download_size: 47955874
dataset_size: 5480098352
---
# Dataset Card for "code_x_glue_cc_clone_detection_big_clone_bench"
## 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/Code-Code/Clone-detection-BigCloneBench
### Dataset Summary
CodeXGLUE Clone-detection-BigCloneBench dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-BigCloneBench
Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others. Models are evaluated by F1 score.
The dataset we use is BigCloneBench and filtered following the paper Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree.
### Supported Tasks and Leaderboards
- `semantic-similarity-classification`: The dataset can be used to train a model for classifying if two given java methods are cloens of each other.
### Languages
- Java **programming** language
## Dataset Structure
### Data Instances
An example of 'test' looks as follows.
```
{
"func1": " @Test(expected = GadgetException.class)\n public void malformedGadgetSpecIsCachedAndThrows() throws Exception {\n HttpRequest request = createCacheableRequest();\n expect(pipeline.execute(request)).andReturn(new HttpResponse(\"malformed junk\")).once();\n replay(pipeline);\n try {\n specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n fail(\"No exception thrown on bad parse\");\n } catch (GadgetException e) {\n }\n specFactory.getGadgetSpec(createContext(SPEC_URL, false));\n }\n",
"func2": " public InputStream getInputStream() throws TGBrowserException {\n try {\n if (!this.isFolder()) {\n URL url = new URL(this.url);\n InputStream stream = url.openStream();\n return stream;\n }\n } catch (Throwable throwable) {\n throw new TGBrowserException(throwable);\n }\n return null;\n }\n",
"id": 0,
"id1": 2381663,
"id2": 4458076,
"label": false
}
```
### 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 |
|id1 |int32 | The first function id |
|id2 |int32 | The second function id |
|func1 |string| The full text of the first function |
|func2 |string| The full text of the second function |
|label |bool | 1 is the functions are not equivalent, 0 otherwise|
### Data Splits
| name |train |validation| test |
|-------|-----:|---------:|-----:|
|default|901028| 415416|415416|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data was mined from the IJaDataset 2.0 dataset.
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Data was manually labeled by three judges by automatically identifying potential clones using search heuristics.
[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
Most of the clones are type 1 and 2 with type 3 and especially type 4 being rare.
[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
```
@inproceedings{svajlenko2014towards,
title={Towards a big data curated benchmark of inter-project code clones},
author={Svajlenko, Jeffrey and Islam, Judith F and Keivanloo, Iman and Roy, Chanchal K and Mia, Mohammad Mamun},
booktitle={2014 IEEE International Conference on Software Maintenance and Evolution},
pages={476--480},
year={2014},
organization={IEEE}
}
@inproceedings{wang2020detecting,
title={Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree},
author={Wang, Wenhan and Li, Ge and Ma, Bo and Xia, Xin and Jin, Zhi},
booktitle={2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
pages={261--271},
year={2020},
organization={IEEE}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | [
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mteb/reddit-clustering | mteb | 2022-09-27T19:13:31Z | 619 | 0 | [
"language:en",
"region:us"
] | null | 2022-04-07T09:12:22Z | ---
language:
- en
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sidhq/email-thread-summary | sidhq | 2023-07-17T03:19:09Z | 619 | 2 | [
"task_categories:summarization",
"language:en",
"region:us"
] | [
"summarization"
] | 2023-07-17T01:08:40Z | ---
dataset_info:
features:
- name: thread
struct:
- name: subject
dtype: string
- name: messages
list:
- name: timestamp
dtype: timestamp[s]
- name: from
dtype: string
- name: to
sequence: string
- name: body
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 38290851
num_examples: 3750
- name: test
num_bytes: 4189760
num_examples: 417
download_size: 13597444
dataset_size: 42480611
task_categories:
- summarization
language:
- en
pretty_name: Email Thread Summary
---
# Dataset Card for "email-thread-summary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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C-MTEB/VideoRetrieval | C-MTEB | 2023-07-28T08:45:16Z | 618 | 0 | [
"region:us"
] | null | 2023-07-28T08:45:00Z | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 8176771
num_examples: 100930
- name: queries
num_bytes: 34156
num_examples: 1000
download_size: 7287165
dataset_size: 8210927
---
# Dataset Card for "VideoRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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kelm | null | 2022-11-18T20:16:37Z | 617 | 6 | [
"task_categories:other",
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"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"data-to-text-generation",
"arxiv:2010.12688",
"region:us"
] | [
"other"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: kelm
pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
tags:
- data-to-text-generation
dataset_info:
features:
- name: triple
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1343187306
num_examples: 6371131
- name: validation
num_bytes: 167790917
num_examples: 796471
- name: test
num_bytes: 167921750
num_examples: 796493
download_size: 1631259869
dataset_size: 1678899973
---
# Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
## 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/google-research-datasets/KELM-corpus
- **Repository:** https://github.com/google-research-datasets/KELM-corpus
- **Paper:** https://arxiv.org/abs/2010.12688
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
### Supported Tasks and Leaderboards
The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language
representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model
with the tuples concatenated into a single sequence.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
Each instance consists of one KG triple paired with corresponding natural language.
### Data Fields
- `triple`: Wikipedia triples of the form `<subject> <relation> <object>` where some subjects have multiple
relations, e.g. `<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>`. For more details on
how these relations are grouped, please refer to the paper.
- `sentence`: The corresponding Wikipedia sentence.
### Data Splits
The dataset includes a pre-determined train, validation, and test split.
## Dataset Creation
### Curation Rationale
The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate
natural text from a knowledge graph.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The data is sourced from English Wikipedia and it's associated knowledge graph.
### 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
From the paper:
> Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still
contain some of these biases, certain types of biases may be reduced.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset has been released under the [CC BY-SA 2.0 license](https://creativecommons.org/licenses/by-sa/2.0/).
### Citation Information
```
@misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | [
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allegro/klej-dyk | allegro | 2022-10-26T09:01:41Z | 617 | 2 | [
"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:cc-by-sa-3.0",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
pretty_name: Did you know?
---
# klej-dyk
## Description
The Czy wiesz? (eng. Did you know?) the dataset consists of almost 5k question-answer pairs obtained from Czy wiesz... section of Polish Wikipedia. Each question is written by a Wikipedia collaborator and is answered with a link to a relevant Wikipedia article. In huggingface version of this dataset, they chose the negatives which have the largest token overlap with a question.
## Tasks (input, output, and metrics)
The task is to predict if the answer to the given question is correct or not.
**Input** ('question sentence', 'answer' columns): question and answer sentences
**Output** ('target' column): 1 if the answer is correct, 0 otherwise.
**Domain**: Wikipedia
**Measurements**: F1-Score
**Example**:
Input: `Czym zajmowali się świątnicy?` ; `Świątnik – osoba, która dawniej zajmowała się
obsługą kościoła (świątyni).`
Input (translated by DeepL): `What did the sacristans do?` ; `A sacristan - a person who used to be in charge of the handling the church (temple).`
Output: `1` (the answer is correct)
## Data splits
| Subset | Cardinality |
| ----------- | ----------: |
| train | 4154 |
| val | 0 |
| test | 1029 |
## Class distribution
| Class | train | validation | test |
|:----------|--------:|-------------:|-------:|
| incorrect | 0.831 | - | 0.831 |
| correct | 0.169 | - | 0.169 |
## Citation
```
@misc{11321/39,
title = {Pytania i odpowiedzi z serwisu wikipedyjnego "Czy wiesz", wersja 1.1},
author = {Marci{\'n}czuk, Micha{\l} and Piasecki, Dominik and Piasecki, Maciej and Radziszewski, Adam},
url = {http://hdl.handle.net/11321/39},
note = {{CLARIN}-{PL} digital repository},
year = {2013}
}
```
## License
```
Creative Commons Attribution ShareAlike 3.0 licence (CC-BY-SA 3.0)
```
## Links
[HuggingFace](https://huggingface.co/datasets/dyk)
[Source](http://nlp.pwr.wroc.pl/en/tools-and-resources/resources/czy-wiesz-question-answering-dataset)
[Source #2](https://clarin-pl.eu/dspace/handle/11321/39)
[Paper](https://www.researchgate.net/publication/272685895_Open_dataset_for_development_of_Polish_Question_Answering_systems)
## Examples
### Loading
```python
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("allegro/klej-dyk")
pprint(dataset['train'][100])
#{'answer': '"W wyborach prezydenckich w 2004 roku, Moroz przekazał swoje '
# 'poparcie Wiktorowi Juszczence. Po wyborach w 2006 socjaliści '
# 'początkowo tworzyli ""pomarańczową koalicję"" z Naszą Ukrainą i '
# 'Blokiem Julii Tymoszenko."',
# 'q_id': 'czywiesz4362',
# 'question': 'ile partii tworzy powołaną przez Wiktora Juszczenkę koalicję '
# 'Blok Nasza Ukraina?',
# 'target': 0}
```
### Evaluation
```python
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("allegro/klej-dyk")
dataset = dataset.class_encode_column("target")
references = dataset["test"]["target"]
# 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': 0.5286686103012633}
# {'f1': 0.46700507614213194}
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] |
magnifi/contextual-tiny-v1 | magnifi | 2023-09-13T17:22:57Z | 617 | 0 | [
"region:us"
] | null | 2023-09-13T17:22:53Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: user_text
dtype: string
- name: true_intent
dtype: string
- name: chat_history
dtype: string
- name: contextual
dtype: bool
- name: in_regression_test
dtype: bool
- name: synthetic
dtype: bool
- name: prompt
dtype: string
- name: completion
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 106909.92835858747
num_examples: 100
- name: validation
num_bytes: 10722.453155139157
num_examples: 10
download_size: 42788
dataset_size: 117632.38151372662
---
# Dataset Card for "contextual-tiny-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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aneeshas/DNN-vuln | aneeshas | 2023-11-09T19:04:14Z | 617 | 0 | [
"region:us"
] | null | 2023-11-08T17:59:02Z | ---
dataset_info:
features:
- name: past_values
sequence: float32
- name: past_time_features
sequence: float32
- name: past_observed_mask
sequence: float32
- name: future_values
sequence: float32
- name: future_time_features
sequence: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 582168
num_examples: 1016
- name: eval
num_bytes: 834861
num_examples: 1457
download_size: 722384
dataset_size: 1417029
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
---
# Dataset Card for "DNN-vuln"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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mmathys/profanity | mmathys | 2023-09-27T09:01:04Z | 615 | 0 | [
"license:mit",
"region:us"
] | null | 2023-09-27T08:59:08Z | ---
license: mit
---
# The Obscenity List
*by [Surge AI, the world's most powerful NLP data labeling platform and workforce](https://www.surgehq.ai)*
Ever wish you had a ready-made list of profanity? Maybe you want to remove NSFW comments, filter offensive usernames, or build content moderation tools, and you can't dream up enough obscenities on your own.
At Surge AI, we help companies build human-powered datasets to train stunning AI and NLP, and we're creating the world's largest profanity list in 20+ languages.
## Dataset
This repo contains 1600+ popular English profanities and their variations.
**Columns**
* `text`: the profanity
* `canonical_form_1`: the profanity's canonical form
* `canonical_form_2`: an additional canonical form, if applicable
* `canonical_form_3`: an additional canonical form, if applicable
* `category_1`: the profanity's primary category (see below for list of categories)
* `category_2`: the profanity's secondary category, if applicable
* `category_3`: the profanity's tertiary category, if applicable
* `severity_rating`: We asked 5 [Surge AI](https://www.surgehq.ai) data labelers to rate how severe they believed each profanity to be, on a 1-3 point scale. This is the mean of those 5 ratings.
* `severity_description`: We rounded `severity_rating` to the nearest integer. `Mild` corresponds to a rounded mean rating of `1`, `Strong` to `2`, and `Severe` to `3`.
## Categories
We organized the profanity into the following categories:
- sexual anatomy / sexual acts (ass kisser, dick, pigfucker)
- bodily fluids / excrement (shit, cum)
- sexual orientation / gender (faggot, tranny, bitch, whore)
- racial / ethnic (chink, n3gro)
- mental disability (retard, dumbass)
- physical disability (quadriplegic bitch)
- physical attributes (fatass, ugly whore)
- animal references (pigfucker, jackass)
- religious offense (goddamn)
- political (China virus)
## Future
We'll be adding more languages and profanity annotations (e.g., augmenting each profanity with its severity level, type, and other variations) over time.
Check out our other [free datasets](https://www.surgehq.ai/datasets).
Sign up [here](https://forms.gle/u1SKL4zySK2wMp1r7) to receive updates on this dataset and be the first to learn about new datasets we release!
## Contact
Need a larger set of expletives and slurs, or a list of swear words in other languages (Spanish, French, German, Japanese, Portuguese, etc)? We work with top AI and content moderation companies around the world, and we love feedback. Post an issue or reach out to [email protected]!
Follow us on Twitter at [@HelloSurgeAI](https://www.twitter.com/@HelloSurgeAI).
## Original Repo
You can find the original repository here: https://github.com/surge-ai/profanity/ | [
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ThWu/cleaned_prompt_r | ThWu | 2023-11-16T19:21:10Z | 615 | 1 | [
"region:us"
] | null | 2023-11-16T19:21:03Z | ---
dataset_info:
features:
- name: conversations
sequence: string
splits:
- name: train
num_bytes: 157911562
num_examples: 268781
download_size: 97143836
dataset_size: 157911562
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cleaned_prompt_r"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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BeIR/dbpedia-entity-qrels | BeIR | 2022-10-23T06:07:36Z | 614 | 0 | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | [
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-05T17:27:22Z | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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C-MTEB/VideoRetrieval-qrels | C-MTEB | 2023-07-28T09:22:40Z | 613 | 0 | [
"region:us"
] | null | 2023-07-28T09:22:33Z | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 27968
num_examples: 1000
download_size: 17369
dataset_size: 27968
---
# Dataset Card for "VideoRetrieval-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
fhamborg/news_sentiment_newsmtsc | fhamborg | 2022-10-25T09:20:03Z | 612 | 9 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: 'NewsMTSC'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
language_bcp47:
- en-US
---
# NewsMTSC dataset
NewsMTSC is a high-quality dataset consisting of more than 11k manually labeled sentences sampled from English news articles. Each sentence was labeled by five human coders (the dataset contains only examples where the five coders assessed same or similar sentiment). The dataset is published as a [full paper at EACL 2021: *NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles*](https://aclanthology.org/2021.eacl-main.142.pdf).
## Subsets and splits
The dataset consists of two subsets (`rw` and `mt`), each consisting of three splits (train, validation, and test). We recommend to use the `rw` subset, which is also the default subset. Both subsets share the same train set, in which the three sentiment classes have similar frequency since we applied class boosting. The two subsets differ in their validation and test sets: `rw` contains validation and test sets that resemble real-world distribution of sentiment in news articles. In contrast, `mt`'s validation and test sets contain only sentences that each have two or more (different) targets, where each target's sentiment was labeled individually.
More information on the subsets can be found in our [paper](https://aclanthology.org/2021.eacl-main.142.pdf).
## Format
Each split is stored in a JSONL file. In JSONL, each line represents one JSON object. In our dataset, each JSON object consists of the following attributes. When using the dataset, you most likely will need (only) the attributes highlighted in **bold**.
1. `mention`: text of the mention within `sentence`
2. **`polarity`: sentiment of the sentence concerning the target's mention (-1 = negative, 0 = neutral, 1 = positive)**
3. **`from`: character-based, 0-indexed position of the first character of the target's mention within `sentence`**
4. **`to`: last character of the target's mention**
5. **`sentence`: sentence**
6. `id`: identifier that is unique within NewsMTSC
## Contact
If you find an issue with the dataset or model or have a question concerning either, please open an issue in the repository.
* Repository: [https://github.com/fhamborg/NewsMTSC](https://github.com/fhamborg/NewsMTSC)
* Web: [https://felix.hamborg.eu/](https://felix.hamborg.eu/)
## How to cite
If you use the dataset or parts of it, please cite our paper:
```
@InProceedings{Hamborg2021b,
author = {Hamborg, Felix and Donnay, Karsten},
title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
year = {2021},
month = {Apr.},
location = {Virtual Event},
}
```
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C-MTEB/MedicalRetrieval | C-MTEB | 2023-07-28T09:33:59Z | 612 | 0 | [
"region:us"
] | null | 2023-07-28T09:33:27Z | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 37393271
num_examples: 100999
- name: queries
num_bytes: 63649
num_examples: 1000
download_size: 25077981
dataset_size: 37456920
---
# Dataset Card for "MedicalRetrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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shreyasharma/Sentence_eval | shreyasharma | 2022-11-26T02:07:02Z | 611 | 0 | [
"region:us"
] | null | 2022-11-26T02:06:48Z | ---
dataset_info:
features:
- name: sentences
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 730710
num_examples: 11528
- name: test
num_bytes: 386248
num_examples: 6104
- name: val
num_bytes: 104917
num_examples: 1632
download_size: 485767
dataset_size: 1221875
---
# Dataset Card for "Sentence_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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result-kand2-sdxl-wuerst-karlo/25b005b7 | result-kand2-sdxl-wuerst-karlo | 2023-10-08T22:30:24Z | 610 | 0 | [
"region:us"
] | null | 2023-10-08T22:30:23Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1383
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "25b005b7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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liuyanchen1015/MULTI_VALUE_stsb_myself_coordinate_subjects | liuyanchen1015 | 2023-04-09T01:30:16Z | 608 | 0 | [
"region:us"
] | null | 2023-04-03T19:45:12Z | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 208
num_examples: 1
- name: train
num_bytes: 168
num_examples: 1
download_size: 0
dataset_size: 376
---
# Dataset Card for "MULTI_VALUE_stsb_myself_coordinate_subjects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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BeIR/fever-qrels | BeIR | 2022-10-23T06:08:11Z | 607 | 0 | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | [
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-05T17:28:01Z | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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C-MTEB/MedicalRetrieval-qrels | C-MTEB | 2023-07-28T09:34:03Z | 607 | 0 | [
"region:us"
] | null | 2023-07-28T09:33:59Z | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 26893
num_examples: 1000
download_size: 12201
dataset_size: 26893
---
# Dataset Card for "MedicalRetrieval-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cmu_hinglish_dog | null | 2023-03-17T10:14:14Z | 604 | 4 | [
"task_categories:translation",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:hi",
"license:cc-by-sa-3.0",
"license:gfdl",
"arxiv:1809.07358",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
- hi
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- multilingual
- translation
pretty_name: CMU Document Grounded Conversations
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
dataset_info:
features:
- name: date
dtype: string
- name: docIdx
dtype: int64
- name: translation
dtype:
translation:
languages:
- en
- hi_en
- name: uid
dtype: string
- name: utcTimestamp
dtype: string
- name: rating
dtype: int64
- name: status
dtype: int64
- name: uid1LogInTime
dtype: string
- name: uid1LogOutTime
dtype: string
- name: uid1response
struct:
- name: response
sequence: int64
- name: type
dtype: string
- name: uid2response
struct:
- name: response
sequence: int64
- name: type
dtype: string
- name: user2_id
dtype: string
- name: whoSawDoc
sequence: string
- name: wikiDocumentIdx
dtype: int64
splits:
- name: train
num_bytes: 3142398
num_examples: 8060
- name: test
num_bytes: 379521
num_examples: 960
- name: validation
num_bytes: 368726
num_examples: 942
download_size: 8749685
dataset_size: 3890645
---
# Dataset Card for CMU Document Grounded Conversations
## 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:** [CMU Hinglish DoG](http://festvox.org/cedar/data/notyet/)
- **Repository:** [CMU Document Grounded Conversations (English version)](https://github.com/festvox/datasets-CMU_DoG)
- **Paper:** [CMU Document Grounded Conversations (English version)](https://arxiv.org/pdf/1809.07358.pdf)
- **Point of Contact:**
### Dataset Summary
This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English versions. Can be used for Translating between the two. The dataset has been provided by Prof. Alan Black's group from CMU.
### Supported Tasks and Leaderboards
- `abstractive-mt`
### Languages
## Dataset Structure
### Data Instances
A typical data point comprises a Hinglish text, with key `hi_en` and its English version with key `en`. The `docIdx` contains the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document. The `uid` has the user id of this utterance.
An example from the CMU_Hinglish_DoG train set looks as follows:
```
{'rating': 2,
'wikiDocumentIdx': 13,
'utcTimestamp': '2018-03-16T17:48:22.037Z',
'uid': 'user2',
'date': '2018-03-16T17:47:21.964Z',
'uid2response': {'response': [1, 2, 3, 5], 'type': 'finish'},
'uid1LogInTime': '2018-03-16T17:47:21.964Z',
'user2_id': 'USR664',
'uid1LogOutTime': '2018-03-16T18:02:29.072Z',
'whoSawDoc': ['user1', 'user2'],
'status': 1,
'docIdx': 0,
'uid1response': {'response': [1, 2, 3, 4], 'type': 'finish'},
'translation': {'en': 'The director is Zack Snyder, 27% Rotten Tomatoes, 4.9/10.',
'hi_en': 'Zack Snyder director hai, 27% Rotten Tomatoes, 4.9/10.'}}
```
### Data Fields
- `date`: the time the file is created, as a string
- `docIdx`: the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document.
- `translation`:
- `hi_en`: The text in Hinglish
- `en`: The text in English
- `uid`: the user id of this utterance.
- `utcTimestamp`: the server utc timestamp of this utterance, as a string
- `rating`: A number from 1 or 2 or 3. A larger number means the quality of the conversation is better.
- `status`: status as an integer
- `uid1LogInTime`: optional login time of user 1, as a string
- `uid1LogOutTime`: optional logout time of user 1, as a string
- `uid1response`: a json object contains the status and response of user after finishing the conversation. Fields in the object includes:
- `type`: should be one of ['finish', 'abandon','abandonWithouAnsweringFeedbackQuestion']. 'finish' means the user successfully finishes the conversation, either by completing 12 or 15 turns or in the way that the other user leaves the conversation first. 'abandon' means the user abandons the conversation in the middle, but entering the feedback page. 'abandonWithouAnsweringFeedbackQuestion' means the user just disconnects or closes the web page without providing the feedback.
- `response`: the answer to the post-conversation questions. The worker can choose multiple of them. The options presented to the user are as follows:
For type 'finish'
1: The conversation is understandable.
2: The other user is actively responding me.
3: The conversation goes smoothly.
For type 'abandon'
1: The other user is too rude.
2: I don't know how to proceed with the conversation.
3: The other user is not responding to me.
For users given the document
4: I have watched the movie before.
5: I have not watched the movie before.
For the users without the document
4: I will watch the movie after the other user's introduction.
5: I will not watch the movie after the other user's introduction.
- `uid2response`: same as uid1response
- `user2_id`: the generated user id of user 2
- `whoSawDoc`: Should be one of ['user1'], ['user2'], ['user1', 'user2']. Indicating which user read the document.
- `wikiDocumentId`: the index of the wiki document.
### Data Splits
| name |train|validation|test|
|----------|----:|---------:|---:|
|CMU DOG | 8060| 942| 960|
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
The Hinglish dataset is derived from the original CMU DoG (Document Grounded Conversations Dataset). More info about that can be found in the [repo](https://github.com/festvox/datasets-CMU_DoG)
#### 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
The purpose of this dataset is to help develop better question answering systems.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was initially created by Prof Alan W Black's group at CMU
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@inproceedings{
cmu_dog_emnlp18,
title={A Dataset for Document Grounded Conversations},
author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
year={2018},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
}
```
### Contributions
Thanks to [@Ishan-Kumar2](https://github.com/Ishan-Kumar2) for adding this dataset. | [
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simple_questions_v2 | null | 2022-11-18T21:46:14Z | 604 | 2 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: simplequestions
pretty_name: SimpleQuestions
dataset_info:
- config_name: annotated
features:
- name: id
dtype: string
- name: subject_entity
dtype: string
- name: relationship
dtype: string
- name: object_entity
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 12376039
num_examples: 75910
- name: validation
num_bytes: 12376039
num_examples: 75910
- name: test
num_bytes: 12376039
num_examples: 75910
download_size: 423435590
dataset_size: 37128117
- config_name: freebase2m
features:
- name: id
dtype: string
- name: subject_entity
dtype: string
- name: relationship
dtype: string
- name: object_entities
sequence: string
splits:
- name: train
num_bytes: 1964037256
num_examples: 10843106
download_size: 423435590
dataset_size: 1964037256
- config_name: freebase5m
features:
- name: id
dtype: string
- name: subject_entity
dtype: string
- name: relationship
dtype: string
- name: object_entities
sequence: string
splits:
- name: train
num_bytes: 2481753516
num_examples: 12010500
download_size: 423435590
dataset_size: 2481753516
---
# Dataset Card for SimpleQuestions
## 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://research.fb.com/downloads/babi/
- **Repository:** https://github.com/fbougares/TSAC
- **Paper:** https://research.fb.com/publications/large-scale-simple-question-answering-with-memory-networks/
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [Antoine Borde]([email protected]) [Nicolas Usunie]([email protected]) [Sumit Chopra]([email protected]), [Jason Weston]([email protected])
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
* What American cartoonist is the creator of Andy Lippincott?
Fact: (andy_lippincott, character_created_by, garry_trudeau)
* Which forest is Fires Creek in?
Fact: (fires_creek, containedby, nantahala_national_forest)
* What does Jimmy Neutron do?
Fact: (jimmy_neutron, fictional_character_occupation, inventor)
* What dietary restriction is incompatible with kimchi?
Fact: (kimchi, incompatible_with_dietary_restrictions, veganism)
### 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 [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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-0.11510054022073746
] |
C-MTEB/Mmarco-reranking | C-MTEB | 2023-07-28T07:25:10Z | 603 | 0 | [
"region:us"
] | null | 2023-07-28T07:24:47Z | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: dev
num_bytes: 32794704
num_examples: 100
download_size: 17401514
dataset_size: 32794704
---
# Dataset Card for "Mmarco-reranking"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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blended_skill_talk | null | 2023-04-05T09:41:47Z | 602 | 53 | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2004.08449",
"region:us"
] | [
"conversational"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: BlendedSkillTalk
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: blended-skill-talk
dataset_info:
features:
- name: personas
sequence: string
- name: additional_context
dtype: string
- name: previous_utterance
sequence: string
- name: context
dtype: string
- name: free_messages
sequence: string
- name: guided_messages
sequence: string
- name: suggestions
sequence:
- name: convai2
dtype: string
- name: empathetic_dialogues
dtype: string
- name: wizard_of_wikipedia
dtype: string
- name: guided_chosen_suggestions
sequence: string
- name: label_candidates
sequence:
sequence: string
splits:
- name: train
num_bytes: 10831361
num_examples: 4819
- name: validation
num_bytes: 43961658
num_examples: 1009
- name: test
num_bytes: 44450102
num_examples: 980
download_size: 38101408
dataset_size: 99243121
---
# Dataset Card for "blended_skill_talk"
## 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://parl.ai/projects/bst/](https://parl.ai/projects/bst/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1)
- **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:** 38.11 MB
- **Size of the generated dataset:** 15.08 MB
- **Total amount of disk used:** 53.17 MB
### Dataset Summary
A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
### 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:** 38.11 MB
- **Size of the generated dataset:** 15.08 MB
- **Total amount of disk used:** 53.17 MB
An example of 'train' looks as follows.
```
{
'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'],
'additional_context': 'Backstreet Boys',
'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "],
'context': 'wizard_of_wikipedia',
'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "],
'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'],
'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']},
'guided_chosen_suggestions': ['convai2', '', ''],
'label_candidates': []}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `personas`: a `list` of `string` features.
- `additional_context`: a `string` feature.
- `previous_utterance`: a `list` of `string` features.
- `context`: a `string` feature.
- `free_messages`: a `list` of `string` features.
- `guided_messgaes`: a `list` of `string` features.
- `suggestions`: a dictionary feature containing:
- `convai2`: a `string` feature.
- `empathetic_dialogues`: a `string` feature.
- `wizard_of_wikipedia`: a `string` feature.
- `guided_chosen_suggestions`: a `list` of `string` features.
- `label_candidates`: a `list` of `lists` of `string` features.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 4819| 1009| 980|
## 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
```
@misc{smith2020evaluating,
title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
year={2020},
eprint={2004.08449},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. | [
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knowrohit07/know_sql | knowrohit07 | 2023-09-20T20:13:06Z | 602 | 90 | [
"license:openrail",
"region:us"
] | null | 2023-09-16T12:18:52Z | ---
license: openrail
---
please use the val ign file for training, its much cleaner. thanks :) | [
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C-MTEB/CMedQAv1-reranking | C-MTEB | 2023-07-28T07:19:52Z | 600 | 0 | [
"region:us"
] | null | 2023-07-28T07:19:27Z | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: test
num_bytes: 31879155
num_examples: 1000
download_size: 20670061
dataset_size: 31879155
---
# Dataset Card for "CMedQAv1-reranking"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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emozilla/proofpile-test-tokenized | emozilla | 2023-08-09T15:29:52Z | 600 | 0 | [
"region:us"
] | null | 2023-08-09T15:27:50Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: meta
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: tokenized_len
dtype: int64
splits:
- name: test
num_bytes: 1644067664
num_examples: 46251
download_size: 552973486
dataset_size: 1644067664
---
# Dataset Card for "proofpile-test-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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BeIR/nq-qrels | BeIR | 2022-10-23T06:08:44Z | 599 | 0 | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | [
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-06T13:33:50Z | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** [email protected]
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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Dahoas/static-hh | Dahoas | 2023-03-06T00:11:55Z | 599 | 15 | [
"region:us"
] | null | 2023-02-15T03:53:36Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 143664651
num_examples: 96256
- name: test
num_bytes: 7649255
num_examples: 5103
download_size: 90825631
dataset_size: 151313906
---
Static split of Anthropic's Helpful Harmless dataset. Contains base-online and rejection sampled outputs. | [
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gradients-ai/mc4_v01 | gradients-ai | 2023-09-08T03:06:31Z | 599 | 1 | [
"task_categories:text-retrieval",
"language:en",
"language:vi",
"region:us"
] | [
"text-retrieval"
] | 2023-06-26T06:28:37Z | ---
task_categories:
- text-retrieval
language:
- en
- vi
--- | [
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ehartford/dolphin | ehartford | 2023-09-25T16:59:11Z | 599 | 253 | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] | [
"text-generation"
] | 2023-07-01T10:53:40Z | ---
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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open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1 | open-llm-leaderboard | 2023-09-22T13:40:02Z | 599 | 0 | [
"region:us"
] | null | 2023-08-18T11:49:41Z | ---
pretty_name: Evaluation run of togethercomputer/GPT-JT-6B-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [togethercomputer/GPT-JT-6B-v1](https://huggingface.co/togethercomputer/GPT-JT-6B-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T13:39:48.520000](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1/blob/main/results_2023-09-22T13-39-48.520000.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.00041946308724832214,\n\
\ \"em_stderr\": 0.00020969854707829363,\n \"f1\": 0.04423657718120805,\n\
\ \"f1_stderr\": 0.0011409456494249344,\n \"acc\": 0.3324266847298275,\n\
\ \"acc_stderr\": 0.00819810174632109\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.00041946308724832214,\n \"em_stderr\": 0.00020969854707829363,\n\
\ \"f1\": 0.04423657718120805,\n \"f1_stderr\": 0.0011409456494249344\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.012130401819560273,\n \
\ \"acc_stderr\": 0.0030152942428909413\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6527229676400947,\n \"acc_stderr\": 0.013380909249751237\n\
\ }\n}\n```"
repo_url: https://huggingface.co/togethercomputer/GPT-JT-6B-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T13_39_48.520000
path:
- '**/details_harness|drop|3_2023-09-22T13-39-48.520000.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T13-39-48.520000.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T13_39_48.520000
path:
- '**/details_harness|gsm8k|5_2023-09-22T13-39-48.520000.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T13-39-48.520000.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:44:05.719684.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:44:05.719684.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T13_39_48.520000
path:
- '**/details_harness|winogrande|5_2023-09-22T13-39-48.520000.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T13-39-48.520000.parquet'
- config_name: results
data_files:
- split: 2023_07_19T15_44_05.719684
path:
- results_2023-07-19T15:44:05.719684.parquet
- split: 2023_09_22T13_39_48.520000
path:
- results_2023-09-22T13-39-48.520000.parquet
- split: latest
path:
- results_2023-09-22T13-39-48.520000.parquet
---
# Dataset Card for Evaluation run of togethercomputer/GPT-JT-6B-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/togethercomputer/GPT-JT-6B-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [togethercomputer/GPT-JT-6B-v1](https://huggingface.co/togethercomputer/GPT-JT-6B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T13:39:48.520000](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v1/blob/main/results_2023-09-22T13-39-48.520000.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.00041946308724832214,
"em_stderr": 0.00020969854707829363,
"f1": 0.04423657718120805,
"f1_stderr": 0.0011409456494249344,
"acc": 0.3324266847298275,
"acc_stderr": 0.00819810174632109
},
"harness|drop|3": {
"em": 0.00041946308724832214,
"em_stderr": 0.00020969854707829363,
"f1": 0.04423657718120805,
"f1_stderr": 0.0011409456494249344
},
"harness|gsm8k|5": {
"acc": 0.012130401819560273,
"acc_stderr": 0.0030152942428909413
},
"harness|winogrande|5": {
"acc": 0.6527229676400947,
"acc_stderr": 0.013380909249751237
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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Falah/Alzheimer_MRI | Falah | 2023-07-04T10:03:44Z | 598 | 1 | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] | [
"image-classification"
] | 2023-07-04T09:24:50Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Mild_Demented
'1': Moderate_Demented
'2': Non_Demented
'3': Very_Mild_Demented
splits:
- name: train
num_bytes: 22560791.2
num_examples: 5120
- name: test
num_bytes: 5637447.08
num_examples: 1280
download_size: 28289848
dataset_size: 28198238.28
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- medical
pretty_name: Alzheimer_MRI Disease Classification Dataset
size_categories:
- 1K<n<10K
---
# Alzheimer_MRI Disease Classification Dataset
The Falah/Alzheimer_MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:
- '0': Mild_Demented
- '1': Moderate_Demented
- '2': Non_Demented
- '3': Very_Mild_Demented
## Dataset Information
- Train split:
- Name: train
- Number of bytes: 22,560,791.2
- Number of examples: 5,120
- Test split:
- Name: test
- Number of bytes: 5,637,447.08
- Number of examples: 1,280
- Download size: 28,289,848 bytes
- Dataset size: 28,198,238.28 bytes
## Citation
If you use this dataset in your research or health medicine applications, we kindly request that you cite the following publication:
```
@dataset{alzheimer_mri_dataset,
author = {Falah.G.Salieh},
title = {Alzheimer MRI Dataset},
year = {2023},
publisher = {Hugging Face},
version = {1.0},
url = {https://huggingface.co/datasets/Falah/Alzheimer_MRI}
}
```
## Usage Example
Here's an example of how to load the dataset using the Hugging Face library:
```python
from datasets import load_dataset
# Load the Falah/Alzheimer_MRI dataset
dataset = load_dataset('Falah/Alzheimer_MRI', split='train')
# Print the number of examples and the first few samples
print("Number of examples:", len(dataset))
print("Sample data:")
for example in dataset[:5]:
print(example)
``` | [
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bertin-project/mc4-es-sampled | bertin-project | 2023-03-16T08:56:10Z | 597 | 3 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"size_categories:10M<n<100M",
"size_categories:100M<n<1B",
"source_datasets:mc4",
"source_datasets:bertin-project/mc4-sampling",
"language:es",
"license:odc-by",
"arxiv:1910.10683",
"arxiv:2207.06814",
"region:us"
] | [
"text-generation",
"fill-mask"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- es
license:
- odc-by
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
- 10M<n<100M
- 100M<n<1B
source_datasets:
- mc4
- bertin-project/mc4-sampling
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
pretty_name: mC4-es-sampled
---
# Dataset Card for mC4-es-sampled
## Table of Contents
- [Dataset Card for mC4-es-sampled](#dataset-card-for-mc4-es-sampled)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/allenai/c4
- **Paper:** https://arxiv.org/abs/1910.10683
### Dataset Summary
This dataset is the result of applying perplexity sampling to the Spanish portion of mC4 using [`mc4-sampling`](https://huggingface.co/datasets/bertin-project/mc4-sampling/). Please, refer to [BERTIN Project](https://huggingface.co/bertin-project/bertin-roberta-base-spanish).
You can load the mC4 Spanish sampled like this:
```python
from datasets import load_dataset
for config in ("random", "stepwise", "gaussian"):
mc4es = load_dataset(
"bertin-project/mc4-es-sampled",
config,
split="train",
streaming=True
).shuffle(buffer_size=1000)
for sample in mc4es:
print(config, sample)
break
```
Alternatively, you can bypass the `datasets` library and quickly download (\~1.5hrs, depending on connection) a specific config in the same order used to pre-train BERTIN models in a massive (\~200GB) JSON-lines files:
```python
import io
import gzip
import json
import sys
import requests
from tqdm import tqdm
_DATA_URL_TRAIN = "https://huggingface.co/datasets/bertin-project/mc4-es-sampled/resolve/main/mc4-es-train-50M-{config}-shard-{index:04d}-of-{n_shards:04d}.json.gz"
def main(config="stepwise"):
data_urls = [
_DATA_URL_TRAIN.format(
config=config,
index=index + 1,
n_shards=1024,
)
for index in range(1024)
]
with open(f"mc4-es-train-50M-{config}.jsonl", "w") as f:
for dara_url in tqdm(data_urls):
response = requests.get(dara_url)
bio = io.BytesIO(response.content)
with gzip.open(bio, "rt", encoding="utf8") as g:
for line in g:
json_line = json.loads(line.strip())
f.write(json.dumps(json_line) + "\
")
if __name__ == "__main__":
main(sys.argv[1])
```
### Supported Tasks and Leaderboards
mC4-es-sampled is mainly intended for reproducibility purposes of the BERTIN Project and to pretrain language models and word representations on medium budgets.
### Languages
The dataset only supports the Spanish language.
## Dataset Structure
### Data Instances
An example form the `Gaussian` config:
```python
{'timestamp': '2018-10-20T06:20:53Z', 'text': 'Ortho HyaluroTop 200 aporta el colágeno y ácido hialurónico que, con la edad, se producen en menor cantidad. La vitamina C promueve la producción de colágeno para mantener la piel sana y protege a las células contra los radicales libres causados ??por la contaminación ambiental y los rayos UV.', 'url': 'https://www.farmaciagaleno.com/orthonat-hyalurotop-200-30-capsulas'}
```
### Data Fields
The data have several fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp as a string
### Data Splits
The resulting mC4 subsets for Spanish are reported in this table:
| config | train |
|:---------|:--------|
| stepwise | 50M |
| random | 50M |
| gaussian | 50M |
The split `validation` is exactly the same as the original `mc4` dataset.
## Dataset Creation
### Curation Rationale
This dataset was built from the original [`mc4`](https://huggingface.co/datasets/mc4) by applying perplexity-sampling via [`mc4-sampling`](https://huggingface.co/datasets/bertin-project/mc4-sampling) for Spanish.
## Additional Information
### Dataset Curators
Original data by [Common Crawl](https://commoncrawl.org/).
### Licensing Information
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
To cite this dataset ([arXiv](https://arxiv.org/abs/2207.06814)):
```bibtex
@article{BERTIN,
author = {Javier De la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury},
title = {{BERTIN}: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling},
journal = {Procesamiento del Lenguaje Natural},
volume = {68},
number = {0},
year = {2022},
keywords = {},
abstract = {The pre-training of large language models usually requires massive amounts of resources, both in terms of computation and data. Frequently used web sources such as Common Crawl might contain enough noise to make this pretraining sub-optimal. In this work, we experiment with different sampling methods from the Spanish version of mC4, and present a novel data-centric technique which we name perplexity sampling that enables the pre-training of language models in roughly half the amount of steps and using one fifth of the data. The resulting models are comparable to the current state-of-the-art, and even achieve better results for certain tasks. Our work is proof of the versatility of Transformers, and paves the way for small teams to train their models on a limited budget.},
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6403},
pages = {13--23}
}
```
If you use this dataset, we would love to hear about it! Reach out on twitter, GitHub, Discord, or shoot us an email.
To cite the original `mc4` dataset:
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
### Contributions
Dataset contributed by [@versae](https://github.com/versae) for BERTIN Project.
Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding the original mC4 dataset.
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mozilla-foundation/common_voice_8_0 | mozilla-foundation | 2023-07-29T16:00:11Z | 597 | 25 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | [
"automatic-speech-recognition"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
ab:
- 10K<n<100K
ar:
- 100K<n<1M
as:
- n<1K
az:
- n<1K
ba:
- 100K<n<1M
bas:
- 1K<n<10K
be:
- 100K<n<1M
bg:
- 1K<n<10K
br:
- 10K<n<100K
ca:
- 100K<n<1M
ckb:
- 10K<n<100K
cnh:
- 1K<n<10K
cs:
- 10K<n<100K
cv:
- 10K<n<100K
cy:
- 100K<n<1M
da:
- 1K<n<10K
de:
- 100K<n<1M
dv:
- 10K<n<100K
el:
- 10K<n<100K
en:
- 1M<n<10M
eo:
- 1M<n<10M
es:
- 100K<n<1M
et:
- 10K<n<100K
eu:
- 100K<n<1M
fa:
- 100K<n<1M
fi:
- 10K<n<100K
fr:
- 100K<n<1M
fy-NL:
- 10K<n<100K
ga-IE:
- 1K<n<10K
gl:
- 10K<n<100K
gn:
- 1K<n<10K
ha:
- 1K<n<10K
hi:
- 10K<n<100K
hsb:
- 1K<n<10K
hu:
- 10K<n<100K
hy-AM:
- 1K<n<10K
ia:
- 10K<n<100K
id:
- 10K<n<100K
ig:
- n<1K
it:
- 100K<n<1M
ja:
- 10K<n<100K
ka:
- 1K<n<10K
kab:
- 100K<n<1M
kk:
- 1K<n<10K
kmr:
- 10K<n<100K
ky:
- 10K<n<100K
lg:
- 100K<n<1M
lt:
- 10K<n<100K
lv:
- 1K<n<10K
mdf:
- n<1K
mk:
- n<1K
ml:
- 1K<n<10K
mn:
- 10K<n<100K
mr:
- 1K<n<10K
mt:
- 10K<n<100K
myv:
- 1K<n<10K
nl:
- 10K<n<100K
nn-NO:
- n<1K
or:
- 1K<n<10K
pa-IN:
- 1K<n<10K
pl:
- 100K<n<1M
pt:
- 100K<n<1M
rm-sursilv:
- 1K<n<10K
rm-vallader:
- 1K<n<10K
ro:
- 10K<n<100K
ru:
- 100K<n<1M
rw:
- 1M<n<10M
sah:
- 1K<n<10K
sat:
- n<1K
sk:
- 10K<n<100K
sl:
- 10K<n<100K
sr:
- 1K<n<10K
sv-SE:
- 10K<n<100K
sw:
- 100K<n<1M
ta:
- 100K<n<1M
th:
- 100K<n<1M
tr:
- 10K<n<100K
tt:
- 10K<n<100K
ug:
- 10K<n<100K
uk:
- 10K<n<100K
ur:
- 1K<n<10K
uz:
- 100K<n<1M
vi:
- 10K<n<100K
vot:
- n<1K
zh-CN:
- 10K<n<100K
zh-HK:
- 100K<n<1M
zh-TW:
- 10K<n<100K
source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 8.0
language_bcp47:
- ab
- ar
- as
- az
- ba
- bas
- be
- bg
- br
- ca
- ckb
- cnh
- cs
- cv
- cy
- da
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy-NL
- ga-IE
- gl
- gn
- ha
- hi
- hsb
- hu
- hy-AM
- ia
- id
- ig
- it
- ja
- ka
- kab
- kk
- kmr
- ky
- lg
- lt
- lv
- mdf
- mk
- ml
- mn
- mr
- mt
- myv
- nl
- nn-NO
- or
- pa-IN
- pl
- pt
- rm-sursilv
- rm-vallader
- ro
- ru
- rw
- sah
- sat
- sk
- sl
- sr
- sv-SE
- sw
- ta
- th
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- vot
- zh-CN
- zh-HK
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 8.0
## 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://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:[email protected])
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 18243 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 14122 validated hours in 87 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Abkhaz, Arabic, Armenian, Assamese, Azerbaijani, Basaa, Bashkir, Basque, Belarusian, Breton, Bulgarian, Catalan, Central Kurdish, Chinese (China), Chinese (Hong Kong), Chinese (Taiwan), Chuvash, Czech, Danish, Dhivehi, Dutch, English, Erzya, Esperanto, Estonian, Finnish, French, Frisian, Galician, Georgian, German, Greek, Guarani, Hakha Chin, Hausa, Hindi, Hungarian, Igbo, Indonesian, Interlingua, Irish, Italian, Japanese, Kabyle, Kazakh, Kinyarwanda, Kurmanji Kurdish, Kyrgyz, Latvian, Lithuanian, Luganda, Macedonian, Malayalam, Maltese, Marathi, Moksha, Mongolian, Norwegian Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Romansh Sursilvan, Romansh Vallader, Russian, Sakha, Santali (Ol Chiki), Serbian, Slovak, Slovenian, Sorbian, Upper, Spanish, Swahili, Swedish, Tamil, Tatar, Thai, Turkish, Ukrainian, Urdu, Uyghur, Uzbek, Vietnamese, Votic, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
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doc2dial | null | 2022-11-18T19:58:53Z | 596 | 2 | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: doc2dial
pretty_name: doc2dial
dataset_info:
- config_name: dialogue_domain
features:
- name: dial_id
dtype: string
- name: doc_id
dtype: string
- name: domain
dtype: string
- name: turns
list:
- name: turn_id
dtype: int32
- name: role
dtype: string
- name: da
dtype: string
- name: references
list:
- name: sp_id
dtype: string
- name: label
dtype: string
- name: utterance
dtype: string
splits:
- name: train
num_bytes: 6924209
num_examples: 3474
- name: validation
num_bytes: 1315815
num_examples: 661
download_size: 5879543
dataset_size: 8240024
- config_name: document_domain
features:
- name: domain
dtype: string
- name: doc_id
dtype: string
- name: title
dtype: string
- name: doc_text
dtype: string
- name: spans
list:
- name: id_sp
dtype: string
- name: tag
dtype: string
- name: start_sp
dtype: int32
- name: end_sp
dtype: int32
- name: text_sp
dtype: string
- name: title
dtype: string
- name: parent_titles
dtype: string
- name: id_sec
dtype: string
- name: start_sec
dtype: int32
- name: text_sec
dtype: string
- name: end_sec
dtype: int32
- name: doc_html_ts
dtype: string
- name: doc_html_raw
dtype: string
splits:
- name: train
num_bytes: 204874908
num_examples: 3416
download_size: 5879543
dataset_size: 204874908
- config_name: doc2dial_rc
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: domain
dtype: string
splits:
- name: validation
num_bytes: 22705288
num_examples: 3972
- name: train
num_bytes: 114778994
num_examples: 20431
download_size: 5879543
dataset_size: 137484282
---
# Dataset Card for doc2dial
## 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://doc2dial.github.io
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.652.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Doc2dial is dataset of goal-oriented dialogues that are grounded in the associated documents. It includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets this dataset covers a variety of dialogue scenes in information-seeking conversations.
### Supported Tasks and Leaderboards
> Supported Task: [Shared Task](https://doc2dial.github.io/workshop2021/shared.html) hosted by DialDoc21 at ACL.
> Leaderboard: [LINK](https://eval.ai/web/challenges/challenge-page/793)
### Languages
English
## Dataset Structure
### Data Instances
Sample data instance for `dialogue_domain` :
```
{
"dial_id": "9f44c1539efe6f7e79b02eb1b413aa43",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0",
"domain": "dmv",
"turns": [
{
"da": "query_condition",
"references": [
{
"sp_id": "4",
"label": "precondition"
}
],
"role": "user",
"turn_id": 1,
"utterance": "Hello, I forgot o update my address, can you help me with that?"
},
{
"da": "response_solution",
"references": [
{
"sp_id": "6",
"label": "solution"
},
{
"sp_id": "7",
"label": "solution"
},
{
"sp_id": "4",
"label": "references"
}
],
"role": "agent",
"turn_id": 2,
"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles."
},
{
"da": "query_solution",
"references": [
{
"sp_id": "56",
"label": "solution"
},
{
"sp_id": "48",
"label": "references"
}
],
"role": "user",
"turn_id": 3,
"utterance": "Can I do my DMV transactions online?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "56",
"label": "solution"
},
{
"sp_id": "48",
"label": "references"
}
],
"role": "agent",
"turn_id": 4,
"utterance": "Yes, you can sign up for MyDMV for all the online transactions needed."
},
{
"da": "query_condition",
"references": [
{
"sp_id": "48",
"label": "precondition"
}
],
"role": "user",
"turn_id": 5,
"utterance": "Thanks, and in case I forget to bring all of the documentation needed to the DMV office, what can I do?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "49",
"label": "solution"
},
{
"sp_id": "50",
"label": "solution"
},
{
"sp_id": "52",
"label": "solution"
},
{
"sp_id": "48",
"label": "references"
}
],
"role": "agent",
"turn_id": 6,
"utterance": "This happens often with our customers so that's why our website and MyDMV are so useful for our customers. Just check if you can make your transaction online so you don't have to go to the DMV Office."
},
{
"da": "query_solution",
"references": [
{
"sp_id": "6",
"label": "solution"
},
{
"sp_id": "7",
"label": "solution"
},
{
"sp_id": "4",
"label": "references"
}
],
"role": "user",
"turn_id": 7,
"utterance": "Ok, and can you tell me again where should I report my new address?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "6",
"label": "solution"
},
{
"sp_id": "7",
"label": "solution"
},
{
"sp_id": "4",
"label": "references"
}
],
"role": "agent",
"turn_id": 8,
"utterance": "Sure. Any change of address must be reported to the DMV, that's for the address associated with your license and any of your vehicles."
},
{
"da": "query_condition",
"references": [
{
"sp_id": "40",
"label": "precondition"
}
],
"role": "user",
"turn_id": 9,
"utterance": "Can you tell me more about Traffic points and their cost?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "41",
"label": "solution"
},
{
"sp_id": "43",
"label": "solution"
},
{
"sp_id": "40",
"label": "references"
}
],
"role": "agent",
"turn_id": 10,
"utterance": "Traffic points is the system used by DMV to track dangerous drivers. The cost of the traffic points is independent of the DRA, so you get a separate charge based on the total points you accumulate."
}
]
}
```
Sample data instance for `document_domain` :
```
{
"doc_id": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1_0",
"domain": "ssa",
"doc_html_raw": "<main class=\"content\" id=\"content\" role=\"main\">\n\n<section>\n\n<div>\n<h2>\nBenefits Planner: Retirement\n</h2>\n</div>\n</section>\n\n\n<section>\n\n<div>\n\n<div>\n\n\n</div>\n\n<article>\n<section>\n\n<h3>Online Calculator (WEP Version)</h3>\n<p>The calculator shown below allows you to estimate your Social Security benefit.\nHowever, for the most accurate estimates, <a>use the Detailed Calculator</a>.</p>\n<p>You need to enter all your past earnings\n, which are shown on your <a>online </a>.</p>\n\n<p>Please Note:</p>\n<ul class=\"browser-default\">\n<li>The Online Calculator is updated periodically<span>*</span> with new benefit increases and other benefit amounts. Therefore, it is likely that your benefit estimates in the future will differ from those calculated today.</li>\n<li>The Online Calculator works on PCs and Macs with Javascript enabled.</li>\n<li>Some browsers may not allow you to print the table below. </li>\n</ul>\n<p></p>\n\n<div>\nThe Online Calculator temporarily stores information on your local computer while your browser is open. To protect your personal information, you should close your browser after you have finished your estimate.\n</div>\n<p></p>\n\n<div>\n<p>Note: If your birthday is on January 1st, we figure your benefit as if your birthday was in the previous year.</p>\n<p>If you qualify for benefits as a Survivor, your <a>full retirement age for survivors benefits</a> may be different.</p></div>\n\n<div>\n</div></section></article></div></section></main>",
"doc_html_ts": "<main><section><div><h2 sent_id=\"1\" text_id=\"1\">Benefits Planner: Retirement</h2></div></section><section><div><article><section><h3 sent_id=\"2\" text_id=\"2\">Online Calculator (WEP Version)</h3><div tag_id=\"1\"><u sent_id=\"3\" tag_id=\"1\"><u sent_id=\"3\" tag_id=\"1\" text_id=\"3\">The calculator shown below allows you to estimate your Social Security benefit .</u></u><u sent_id=\"4\" tag_id=\"1\"><u sent_id=\"4\" tag_id=\"1\" text_id=\"4\">However ,</u><u sent_id=\"4\" tag_id=\"1\" text_id=\"5\">for the most accurate estimates ,</u><u sent_id=\"4\" tag_id=\"1\" text_id=\"6\">use the Detailed Calculator .</u></u></div><div tag_id=\"2\"><u sent_id=\"5\" tag_id=\"2\"><u sent_id=\"5\" tag_id=\"2\" text_id=\"7\">You need to enter all your past earnings , which are shown on your online .</u></u></div><div tag_id=\"3\"><u sent_id=\"6\" tag_id=\"3\"><u sent_id=\"6\" tag_id=\"3\" text_id=\"8\">Please Note:</u></u></div><ul class=\"browser-default\" tag_id=\"3\"><li tag_id=\"3\"><div tag_id=\"3\"><u sent_id=\"9\" tag_id=\"3\"><u sent_id=\"9\" tag_id=\"3\" text_id=\"9\">The Online Calculator is updated periodically * with new benefit increases and other benefit amounts .</u></u><u sent_id=\"10\" tag_id=\"3\"><u sent_id=\"10\" tag_id=\"3\" text_id=\"10\">Therefore ,</u><u sent_id=\"10\" tag_id=\"3\" text_id=\"11\">it is likely that your benefit estimates in the future will differ from those calculated today .</u></u></div></li><li tag_id=\"3\"><u sent_id=\"11\" tag_id=\"3\"><u sent_id=\"11\" tag_id=\"3\" text_id=\"12\">The Online Calculator works on PCs and Macs with Javascript enabled .</u></u></li><li tag_id=\"3\"><u sent_id=\"12\" tag_id=\"3\"><u sent_id=\"12\" tag_id=\"3\" text_id=\"13\">Some browsers may not allow you to print the table below .</u></u></li></ul><div>The Online Calculator temporarily stores information on your local computer while your browser is open. To protect your personal information, you should close your browser after you have finished your estimate.</div><div><div tag_id=\"4\"><u sent_id=\"13\" tag_id=\"4\"><u sent_id=\"13\" tag_id=\"4\" text_id=\"14\">Note:</u></u><u sent_id=\"14\" tag_id=\"4\"><u sent_id=\"14\" tag_id=\"4\" text_id=\"15\">If your birthday is on January 1st ,</u><u sent_id=\"14\" tag_id=\"4\" text_id=\"16\">we figure your benefit as if your birthday was in the previous year .</u></u></div><div tag_id=\"5\"><u sent_id=\"15\" tag_id=\"5\"><u sent_id=\"15\" tag_id=\"5\" text_id=\"17\">If you qualify for benefits as a Survivor ,</u><u sent_id=\"15\" tag_id=\"5\" text_id=\"18\">your full retirement age for survivors benefits may be different .</u></u></div></div></section></article></div></section></main>",
"doc_text": "\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \nThe calculator shown below allows you to estimate your Social Security benefit. However , for the most accurate estimates , use the Detailed Calculator. You need to enter all your past earnings, which are shown on your online. Please Note: The Online Calculator is updated periodically * with new benefit increases and other benefit amounts. Therefore , it is likely that your benefit estimates in the future will differ from those calculated today. The Online Calculator works on PCs and Macs with Javascript enabled. Some browsers may not allow you to print the table below. Note: If your birthday is on January 1st , we figure your benefit as if your birthday was in the previous year. If you qualify for benefits as a Survivor , your full retirement age for survivors benefits may be different. ",
"title": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1",
"spans": [
{
"end_sec": 32,
"end_sp": 32,
"id_sec": "t_0",
"id_sp": "1",
"parent_titles": "[]",
"start_sec": 0,
"start_sp": 0,
"tag": "h2",
"text_sec": "\n\nBenefits Planner: Retirement \n",
"text_sp": "\n\nBenefits Planner: Retirement \n",
"title": "Benefits Planner: Retirement"
},
{
"end_sec": 67,
"end_sp": 67,
"id_sec": "t_1",
"id_sp": "2",
"parent_titles": "[{'id_sp': '1', 'text': 'Benefits Planner: Retirement', 'level': 'h2'}]",
"start_sec": 32,
"start_sp": 32,
"tag": "h3",
"text_sec": "\n\nOnline Calculator (WEP Version) \n",
"text_sp": "\n\nOnline Calculator (WEP Version) \n",
"title": "Online Calculator (WEP Version)"
},
{
"end_sec": 220,
"end_sp": 147,
"id_sec": "1",
"id_sp": "3",
"parent_titles": "[]",
"start_sec": 67,
"start_sp": 67,
"tag": "u",
"text_sec": "The calculator shown below allows you to estimate your Social Security benefit. However , for the most accurate estimates , use the Detailed Calculator. ",
"text_sp": "The calculator shown below allows you to estimate your Social Security benefit. ",
"title": "Online Calculator (WEP Version)"
}
]
}
```
Sample data instance for `doc2dial_rc` :
```
{
"id": "78f72b08b43791a4a70363fe62b8de08_1",
"is_impossible": false,
"question": "Hello, I want to know about the retirement plan.",
"answers": {
"answer_start": [
0
],
"text": [
"\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \n"
]
},
"context": "\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \nThe calculator shown below allows you to estimate your Social Security benefit. However , for the most accurate estimates , use the Detailed Calculator. You need to enter all your past earnings, which are shown on your online. Please Note: The Online Calculator is updated periodically * with new benefit increases and other benefit amounts. Therefore , it is likely that your benefit estimates in the future will differ from those calculated today. The Online Calculator works on PCs and Macs with Javascript enabled. Some browsers may not allow you to print the table below. Note: If your birthday is on January 1st , we figure your benefit as if your birthday was in the previous year. If you qualify for benefits as a Survivor , your full retirement age for survivors benefits may be different. ",
"title": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1_0",
"domain": "ssa"
}
```
### Data Fields
For `document_domain`,
- `doc_id`: the ID of a document;
- `title`: the title of the document;
- `domain`: the domain of the document;
- `doc_text`: the text content of the document (without HTML markups);
- `doc_html_ts`: the document content with HTML markups and the annotated spans that are indicated by `text_id` attribute, which corresponds to `id_sp`.
- `doc_html_raw`: the document content with HTML markups and without span annotations.
- `spans`: key-value pairs of all spans in the document, with `id_sp` as key. Each span includes the following,
- `id_sp`: the id of a span as noted by `text_id` in `doc_html_ts`;
- `start_sp`/ `end_sp`: the start/end position of the text span in `doc_text`;
- `text_sp`: the text content of the span.
- `id_sec`: the id of the (sub)section (e.g. `<p>`) or title (`<h2>`) that contains the span.
- `start_sec` / `end_sec`: the start/end position of the (sub)section in `doc_text`.
- `text_sec`: the text of the (sub)section.
- `title`: the title of the (sub)section.
- `parent_titles`: the parent titles of the `title`.
For `dialogue_domain`:
- `dial_id`: the ID of a dialogue;
- `doc_id`: the ID of the associated document;
- `domain`: domain of the document;
- `turns`: a list of dialogue turns. Each turn includes,
- `turn_id`: the time order of the turn;
- `role`: either "agent" or "user";
- `da`: dialogue act;
- `references`: the grounding span (`id_sp`) in the associated document. If a turn is an irrelevant turn, i.e., `da` ends with "ood", `reference` is empty. **Note** that spans with labels "*precondition*"/"*solution*" are the actual grounding spans. Spans with label "*reference*" are the related titles or contextual reference, which is used for the purpose of describing a dialogue scene better to crowd contributors.
- `utterance`: the human-generated utterance based on the dialogue scene.
For `doc2dial_rc`, this conforms to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) data format. For how to load Doc2Dial data for reading comprehension task, please refer [here](https://github.com/doc2dial/sharedtask-dialdoc2021).
- `id`: the ID of a QA instance;
- `question`: user query;
- `answers`: the answers that are grounded in the associated document;
- `answer_start`: the start position of the grounding span in the associated document (`context`);
- `text`: the text content of the grounding span;
- `title`: the title of the associated document;
- `domain`: the domain of the associated document;
- `context`: the text content of the associated document (without HTML markups).
### Data Splits
Training & dev split for dialogue domain
Training split only for document domain
## 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
Song Feng, Hui Wan, Chulaka Gunasekara, Siva Sankalp Patel,Sachindra Joshi. Luis A. Lastras
### Licensing Information
Creative Commons Attribution 3.0 Unported
### Citation Information
@inproceedings{feng-etal-2020-doc2dial,
title = "doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset",
author = "Feng, Song and Wan, Hui and Gunasekara, Chulaka and Patel, Siva and Joshi, Sachindra and Lastras, Luis",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.652",
}
### Contributions
Thanks to [@songfeng](https://github.com/songfeng), [@KMFODA](https://github.com/KMFODA) for adding this dataset. | [
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hope_edi | null | 2023-06-01T14:59:49Z | 595 | 1 | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:ml",
"language:ta",
"license:cc-by-4.0",
"hope-speech-classification",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- ml
- ta
license:
- cc-by-4.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: hopeedi
pretty_name: 'HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality,
Diversity, and Inclusion'
tags:
- hope-speech-classification
dataset_info:
- config_name: english
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Hope_speech
'1': Non_hope_speech
'2': not-English
splits:
- name: train
num_bytes: 2306656
num_examples: 22762
- name: validation
num_bytes: 288663
num_examples: 2843
download_size: 2739901
dataset_size: 2595319
- config_name: tamil
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Hope_speech
'1': Non_hope_speech
'2': not-Tamil
splits:
- name: train
num_bytes: 1531013
num_examples: 16160
- name: validation
num_bytes: 197378
num_examples: 2018
download_size: 1795767
dataset_size: 1728391
- config_name: malayalam
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Hope_speech
'1': Non_hope_speech
'2': not-malayalam
splits:
- name: train
num_bytes: 1492031
num_examples: 8564
- name: validation
num_bytes: 180713
num_examples: 1070
download_size: 1721534
dataset_size: 1672744
config_names:
- english
- malayalam
- tamil
---
# 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:** [Hope Speech Detection for Equality, Diversity, and Inclusion-EACL 2021](https://competitions.codalab.org/competitions/27653#learn_the_details)
- **Repository:** [HopeEDI data repository](https://competitions.codalab.org/competitions/27653#participate-get_data)
- **Paper:** [HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion](https://www.aclweb.org/anthology/2020.peoples-1.5/)
- **Leaderboard:** [Rank list](https://competitions.codalab.org/competitions/27653#results)
- **Point of Contact:** [Bharathi Raja Chakravarthi](mailto:[email protected])
### Dataset Summary
A Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting.
### Supported Tasks and Leaderboards
To identify hope speech in the comments/posts in social media.
### Languages
English, Tamil and Malayalam
## Dataset Structure
### Data Instances
An example from the English dataset looks as follows:
| text | label |
| :------ | :----- |
| all lives matter .without that we never have peace so to me forever all lives matter. | Hope_speech |
| I think it's cool that you give people a voice to speak out with here on this channel. | Hope_speech |
An example from the Tamil dataset looks as follows:
| text | label |
| :------ | :----- |
| Idha solla ivalo naala | Non_hope_speech |
| இன்று தேசிய பெண் குழந்தைகள் தினம்.. பெண் குழந்தைகளை போற்றுவோம்..அவர்களை பாதுகாப்போம்... | Hope_speech |
An example from the Malayalam dataset looks as follows:
| text | label |
| :------ | :----- |
| ഇത്രെയും കഷ്ടപ്പെട്ട് വളർത്തിയ ആ അമ്മയുടെ മുഖം കണ്ടപ്പോൾ കണ്ണ് നിറഞ്ഞു പോയി | Hope_speech |
| snehikunavar aanayalum pennayalum onnichu jeevikatte..aareyum compel cheythitallalooo..parasparamulla ishtathodeyalle...avarum jeevikatte..🥰🥰 | Hope_speech |
### Data Fields
English
- `text`: English comment.
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-English"
Tamil
- `text`: Tamil-English code mixed comment.
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-Tamil"
Malayalam
- `text`: Malayalam-English code mixed comment.
- `label`: list of the possible values: "Hope_speech", "Non_hope_speech", "not-malayalam"
### Data Splits
| | train | validation |
| ----- |------:|-----------:|
| English | 22762 | 2843 |
| Tamil | 16160 | 2018 |
| Malayalam | 8564 | 1070 |
## Dataset Creation
### Curation Rationale
Hope is considered significant for the well-being, recuperation and restoration of human life by health professionals.
Hate speech or offensive language detection dataset is not available for code-mixed Tamil and code-mixed Malayalam, and it does not take into account LGBTIQ, women in STEM and other minorities. Thus, we cannot use existing hate speech or offensive language detection datasets to detect hope or non-hope for EDI of minorities.
### Source Data
#### Initial Data Collection and Normalization
For English, we collected data on recent topics of EDI, including women in STEM, LGBTIQ issues, COVID-19, Black Lives Matters, United Kingdom (UK) versus China, United States of America (USA) versus China and Australia versus China from YouTube video comments. The data was collected from videos of people from English-speaking countries, such as Australia, Canada, the Republic of Ireland, United Kingdom, the United States of America and New Zealand.
For Tamil and Malayalam, we collected data from India on the recent topics regarding LGBTIQ issues, COVID-19, women in STEM, the Indo-China war and Dravidian affairs.
#### Who are the source language producers?
Youtube users
### Annotations
#### Annotation process
We created Google forms to collect annotations from annotators. Each form contained a maximum of 100 comments, and each page contained a maximum of 10 comments to maintain the quality of annotation. We collected information on the gender, educational background and the medium of schooling of the annotator to know the diversity of the annotator and avoid bias. We educated annotators by providing them with YouTube videos on EDI. A minimum of three annotators annotated each form.
#### Who are the annotators?
For English language comments, annotators were from Australia, the Republic of Ireland, the United Kingdom and the United States of America. For Tamil, we were able to get annotations from both people from the state of Tamil Nadu of India and from Sri Lanka. Most of the annotators were graduate or post-graduate students.
### Personal and Sensitive Information
Social media data is highly sensitive, and even more so when it is related to the minority population, such as the LGBTIQ community or women. We have taken full consideration to minimise the risk associated with individual identity in the data by removing personal information from dataset, such as names but not celebrity names. However, to study EDI, we needed to keep information relating to the following characteristics; racial, gender, sexual orientation, ethnic origin and philosophical beliefs. Annotators were only shown anonymised posts and agreed to make no attempts to contact the comment creator. The dataset will only be made available for research purpose to the researcher who agree to follow ethical
guidelines
## 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 4.0 International Licence](http://creativecommons.org/licenses/by/4.0/.)
### Citation Information
```
@inproceedings{chakravarthi-2020-hopeedi,
title = "{H}ope{EDI}: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion",
author = "Chakravarthi, Bharathi Raja",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.peoples-1.5",
pages = "41--53",
abstract = "Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff{'}s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.",
}
```
### Contributions
Thanks to [@jamespaultg](https://github.com/jamespaultg) for adding this dataset. | [
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KevinGeng/PAL_dataset | KevinGeng | 2023-04-17T07:37:53Z | 593 | 0 | [
"region:us"
] | null | 2023-04-17T07:37:26Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: base
num_bytes: 18526857.0
num_examples: 160
- name: base_extra
num_bytes: 58877425.0
num_examples: 299
download_size: 77158588
dataset_size: 77404282.0
---
# Dataset Card for "PAL_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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doqa | null | 2023-04-05T10:04:58Z | 592 | 0 | [
"language:en",
"arxiv:2005.01328",
"region:us"
] | null | 2022-03-02T23:29:22Z | ---
language:
- en
paperswithcode_id: doqa
pretty_name: DoQA
dataset_info:
- config_name: cooking
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: followup
dtype: string
- name: yesno
dtype: string
- name: orig_answer
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: test
num_bytes: 2969064
num_examples: 1797
- name: validation
num_bytes: 1461613
num_examples: 911
- name: train
num_bytes: 6881681
num_examples: 4612
download_size: 4197671
dataset_size: 11312358
- config_name: movies
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: followup
dtype: string
- name: yesno
dtype: string
- name: orig_answer
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: test
num_bytes: 3166075
num_examples: 1884
download_size: 4197671
dataset_size: 3166075
- config_name: travel
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: followup
dtype: string
- name: yesno
dtype: string
- name: orig_answer
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: test
num_bytes: 3216374
num_examples: 1713
download_size: 4197671
dataset_size: 3216374
---
# Dataset Card for "doqa"
## 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/RevanthRameshkumar/CRD3](https://github.com/RevanthRameshkumar/CRD3)
- **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:** 12.59 MB
- **Size of the generated dataset:** 17.70 MB
- **Total amount of disk used:** 30.28 MB
### Dataset Summary
DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often
referred to as internal “knowledge bases”.
These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack
Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual
reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable
questions and some relevant dialogue acts.
DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs.
### 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
#### cooking
- **Size of downloaded dataset files:** 4.19 MB
- **Size of the generated dataset:** 11.31 MB
- **Total amount of disk used:** 15.51 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
```
#### movies
- **Size of downloaded dataset files:** 4.19 MB
- **Size of the generated dataset:** 3.17 MB
- **Total amount of disk used:** 7.36 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
```
#### travel
- **Size of downloaded dataset files:** 4.19 MB
- **Size of the generated dataset:** 3.22 MB
- **Total amount of disk used:** 7.41 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
```
### Data Fields
The data fields are the same among all splits.
#### cooking
- `title`: a `string` feature.
- `background`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `id`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `followup`: a `string` feature.
- `yesno`: a `string` feature.
- `orig_answer`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### movies
- `title`: a `string` feature.
- `background`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `id`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `followup`: a `string` feature.
- `yesno`: a `string` feature.
- `orig_answer`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### travel
- `title`: a `string` feature.
- `background`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `id`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `followup`: a `string` feature.
- `yesno`: a `string` feature.
- `orig_answer`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
#### cooking
| |train|validation|test|
|-------|----:|---------:|---:|
|cooking| 4612| 911|1797|
#### movies
| |test|
|------|---:|
|movies|1884|
#### travel
| |test|
|------|---:|
|travel|1713|
## 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
```
@misc{campos2020doqa,
title={DoQA -- Accessing Domain-Specific FAQs via Conversational QA},
author={Jon Ander Campos and Arantxa Otegi and Aitor Soroa and Jan Deriu and Mark Cieliebak and Eneko Agirre},
year={2020},
eprint={2005.01328},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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allenai/ultrafeedback_binarized_cleaned | allenai | 2023-12-01T18:42:09Z | 591 | 11 | [
"license:mit",
"region:us"
] | null | 2023-11-29T18:03:33Z | ---
license: mit
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
- split: train_gen
path: data/train_gen-*
- split: test_gen
path: data/test_gen-*
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: source
dtype: string
splits:
- name: train_sft
num_bytes: 393926052.7984401
num_examples: 60829
- name: test_sft
num_bytes: 6230841.363636363
num_examples: 985
- name: train_gen
num_bytes: 314344767.49216783
num_examples: 60829
- name: test_gen
num_bytes: 4982506.090909091
num_examples: 985
- name: train_prefs
num_bytes: 393926052.7984401
num_examples: 60829
- name: test_prefs
num_bytes: 12672623.615773508
num_examples: 1964
download_size: 629736515
dataset_size: 1126082844.1593668
---
# Dataset Card for "ultrafeedback_binarized_cleaned"
**Update 1/12/2023**: I've removed examples identified as faulty by Argilla - see [their awesome work](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) for more details.
This is a version of the [UltraFeedback binarized dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) but with TruthfulQA prompts removed and source annotations added (so you can filter out samples from different sources yourself if you want!).
Please see the [binarized dataset card for more information](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), or the [original UltraFeedback dataset card](https://huggingface.co/datasets/openbmb/UltraFeedback). | [
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RIW/small-coco | RIW | 2023-02-06T01:06:04Z | 590 | 1 | [
"region:us"
] | null | 2023-02-06T00:57:39Z | ---
dataset_info:
features:
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dtype: image
- name: caption
dtype: string
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dtype: string
- name: key
dtype: string
- name: status
dtype: string
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dtype: 'null'
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splits:
- name: train
num_bytes: 1946738057.45
num_examples: 9890
- name: validation
num_bytes: 1953823510.0
num_examples: 9893
download_size: 1313384992
dataset_size: 3900561567.45
---
# Dataset Card for "small-coco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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europa_eac_tm | null | 2023-01-25T14:30:11Z | 589 | 2 | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:bg",
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"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"language:tr",
"license:cc-by-4.0",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids: []
pretty_name: Europa Education and Culture Translation Memory (EAC-TM)
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languages:
- en
- sk
- name: sentence_type
dtype:
class_label:
names:
'0': form_data
'1': sentence_data
splits:
- name: train
num_bytes: 306049
num_examples: 2972
download_size: 3521416
dataset_size: 306049
- config_name: en2sl
features:
- name: translation
dtype:
translation:
languages:
- en
- sl
- name: sentence_type
dtype:
class_label:
names:
'0': form_data
'1': sentence_data
splits:
- name: train
num_bytes: 577524
num_examples: 4644
download_size: 3521416
dataset_size: 577524
- config_name: en2sv
features:
- name: translation
dtype:
translation:
languages:
- en
- sv
- name: sentence_type
dtype:
class_label:
names:
'0': form_data
'1': sentence_data
splits:
- name: train
num_bytes: 304954
num_examples: 2909
download_size: 3521416
dataset_size: 304954
- config_name: en2tr
features:
- name: translation
dtype:
translation:
languages:
- en
- tr
- name: sentence_type
dtype:
class_label:
names:
'0': form_data
'1': sentence_data
splits:
- name: train
num_bytes: 328267
num_examples: 3198
download_size: 3521416
dataset_size: 328267
---
# Dataset Card for Europa Education and Culture Translation Memory (EAC-TM)
## 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://ec.europa.eu/jrc/en/language-technologies/eac-translation-memory](https://ec.europa.eu/jrc/en/language-technologies/eac-translation-memory)
- **Paper:** [https://link.springer.com/article/10.1007/s10579-014-9277-0](https://link.springer.com/article/10.1007/s10579-014-9277-0)
- **Point of Contact:** [[email protected]](mailto:[email protected])
### Dataset Summary
This dataset is a corpus of manually produced translations from english to up to 25 languages, released in 2012 by the European Union's Directorate General for Education and Culture (EAC).
To load a language pair that is not part of the config, just specify the language code as language pair. For example, if you want to translate Czech to Greek:
`dataset = load_dataset("europa_eac_tm", language_pair=("cs", "el"))`
### Supported Tasks and Leaderboards
- `text2text-generation`: the dataset can be used to train a model for `machine-translation`. Machine translation models are usually evaluated using metrics such as [BLEU](https://huggingface.co/metrics/bleu), [ROUGE](https://huggingface.co/metrics/rouge) or [SacreBLEU](https://huggingface.co/metrics/sacrebleu). You can use the [mBART](https://huggingface.co/facebook/mbart-large-cc25) model for this task. This task has active leaderboards which can be found at [https://paperswithcode.com/task/machine-translation](https://paperswithcode.com/task/machine-translation), which usually rank models based on [BLEU score](https://huggingface.co/metrics/bleu).
### Languages
The sentences in this dataset were originally written in English (source language is English) and then translated into the other languages. The sentences are extracted from electroniv forms: application and report forms for decentralised actions of EAC's Life-long Learning Programme (LLP) and the Youth in Action Programme. The contents in the electronic forms are technically split into two types: (a) the labels and contents of drop-down menus (referred to as 'Forms' Data) and (b) checkboxes (referred to as 'Reference Data').
The dataset contains traduction of English sentences or parts of sentences to Bulgarian, Czech, Danish, Dutch, Estonian, German, Greek, Finnish, French, Croatian, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish and Turkish.
Language codes:
- `bg`
- `cs`
- `da`
- `de`
- `el`
- `en`
- `es`
- `et`
- `fi`
- `fr`
- `hr`
- `hu`
- `is`
- `it`
- `lt`
- `lv`
- `mt`
- `nl`
- `no`
- `pl`
- `pt`
- `ro`
- `sk`
- `sl`
- `sv`
- `tr`
## Dataset Structure
### Data Instances
```
{
"translation": {
"en":"Sentence to translate",
"<target_language>": "Phrase à traduire",
},
"sentence_type": 0
}
```
### Data Fields
- `translation`: Mapping of sentences to translate (in English) and translated sentences.
- `sentence_type`: Integer value, 0 if the sentence is a 'form data' (extracted from the labels and contents of drop-down menus of the source electronic forms) or 1 if the sentence is a 'reference data' (extracted from the electronic forms checkboxes).
### Data Splits
The data is not splitted (only the `train` split is available).
## Dataset Creation
### Curation Rationale
The EAC-TM is relatively small compared to the JRC-Acquis and to DGT-TM, but it has the advantage that it focuses on a very different domain, namely that of education and culture. Also, it includes translation units for the languages Croatian (HR), Icelandic (IS), Norwegian (Bokmål, NB or Norwegian, NO) and Turkish (TR).
### Source Data
#### Initial Data Collection and Normalization
EAC-TM was built in the context of translating electronic forms: application and report forms for decentralised actions of EAC's Life-long Learning Programme (LLP) and the Youth in Action Programme. All documents and sentences were originally written in English (source language is English) and then translated into the other languages.
The contents in the electronic forms are technically split into two types: (a) the labels and contents of drop-down menus (referred to as 'Forms' Data) and (b) checkboxes (referred to as 'Reference Data'). Due to the different types of data, the two collections are kept separate. For example, labels can be 'Country', 'Please specify your home country' etc., while examples for reference data are 'Germany', 'Basic/general programmes', 'Education and Culture' etc.
The data consists of translations carried out between the end of the year 2008 and July 2012.
#### Who are the source language producers?
The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language.
### Annotations
#### Annotation process
Sentences were manually translated by humans.
#### Who are the annotators?
The texts were translated by staff of the National Agencies of the Lifelong Learning and Youth in Action programmes. They are typically professionals in the field of education/youth and EU programmes. They are thus not professional translators, but they are normally native speakers of the target language.
### 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
© European Union, 1995-2020
The Commission's reuse policy is implemented by the [Commission Decision of 12 December 2011 on the reuse of Commission documents](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32011D0833).
Unless otherwise indicated (e.g. in individual copyright notices), content owned by the EU on this website is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) licence](http://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed, provided appropriate credit is given and changes are indicated.
You may be required to clear additional rights if a specific content depicts identifiable private individuals or includes third-party works. To use or reproduce content that is not owned by the EU, you may need to seek permission directly from the rightholders. Software or documents covered by industrial property rights, such as patents, trade marks, registered designs, logos and names, are excluded from the Commission's reuse policy and are not licensed to you.
### Citation Information
```
@Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An overview of the European Union's highly multilingual parallel corpora},
journal={Language Resources and Evaluation},
year={2014},
month={Dec},
day={01},
volume={48},
number={4},
pages={679-707},
issn={1574-0218},
doi={10.1007/s10579-014-9277-0},
url={https://doi.org/10.1007/s10579-014-9277-0}
}
```
### Contributions
Thanks to [@SBrandeis](https://github.com/SBrandeis) for adding this dataset. | [
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allenai/scicite | allenai | 2023-01-25T14:43:39Z | 589 | 4 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1904.01608",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
paperswithcode_id: scicite
pretty_name: SciCite
dataset_info:
features:
- name: string
dtype: string
- name: sectionName
dtype: string
- name: label
dtype:
class_label:
names:
'0': method
'1': background
'2': result
- name: citingPaperId
dtype: string
- name: citedPaperId
dtype: string
- name: excerpt_index
dtype: int32
- name: isKeyCitation
dtype: bool
- name: label2
dtype:
class_label:
names:
'0': supportive
'1': not_supportive
'2': cant_determine
'3': none
- name: citeEnd
dtype: int64
- name: citeStart
dtype: int64
- name: source
dtype:
class_label:
names:
'0': properNoun
'1': andPhrase
'2': acronym
'3': etAlPhrase
'4': explicit
'5': acronymParen
'6': nan
- name: label_confidence
dtype: float32
- name: label2_confidence
dtype: float32
- name: id
dtype: string
splits:
- name: test
num_bytes: 870809
num_examples: 1859
- name: train
num_bytes: 3843904
num_examples: 8194
- name: validation
num_bytes: 430296
num_examples: 916
download_size: 23189911
dataset_size: 5145009
---
# Dataset Card for "scicite"
## 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:** https://github.com/allenai/scicite
- **Paper:** [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://arxiv.org/abs/1904.01608)
- **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:** 22.12 MB
- **Size of the generated dataset:** 4.91 MB
- **Total amount of disk used:** 27.02 MB
### Dataset Summary
This is a dataset for classifying citation intents in academic papers.
The main citation intent label for each Json object is specified with the label
key while the citation context is specified in with a context key. Example:
{
'string': 'In chacma baboons, male-infant relationships can be linked to both
formation of friendships and paternity success [30,31].'
'sectionName': 'Introduction',
'label': 'background',
'citingPaperId': '7a6b2d4b405439',
'citedPaperId': '9d1abadc55b5e0',
...
}
You may obtain the full information about the paper using the provided paper ids
with the Semantic Scholar API (https://api.semanticscholar.org/).
The labels are:
Method, Background, Result
### 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:** 22.12 MB
- **Size of the generated dataset:** 4.91 MB
- **Total amount of disk used:** 27.02 MB
An example of 'validation' looks as follows.
```
{
"citeEnd": 68,
"citeStart": 64,
"citedPaperId": "5e413c7872f5df231bf4a4f694504384560e98ca",
"citingPaperId": "8f1fbe460a901d994e9b81d69f77bfbe32719f4c",
"excerpt_index": 0,
"id": "8f1fbe460a901d994e9b81d69f77bfbe32719f4c>5e413c7872f5df231bf4a4f694504384560e98ca",
"isKeyCitation": false,
"label": 2,
"label2": 0,
"label2_confidence": 0.0,
"label_confidence": 0.0,
"sectionName": "Discussion",
"source": 4,
"string": "These results are in contrast with the findings of Santos et al.(16), who reported a significant association between low sedentary time and healthy CVF among Portuguese"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `string`: a `string` feature.
- `sectionName`: a `string` feature.
- `label`: a classification label, with possible values including `method` (0), `background` (1), `result` (2).
- `citingPaperId`: a `string` feature.
- `citedPaperId`: a `string` feature.
- `excerpt_index`: a `int32` feature.
- `isKeyCitation`: a `bool` feature.
- `label2`: a classification label, with possible values including `supportive` (0), `not_supportive` (1), `cant_determine` (2), `none` (3).
- `citeEnd`: a `int64` feature.
- `citeStart`: a `int64` feature.
- `source`: a classification label, with possible values including `properNoun` (0), `andPhrase` (1), `acronym` (2), `etAlPhrase` (3), `explicit` (4).
- `label_confidence`: a `float32` feature.
- `label2_confidence`: a `float32` feature.
- `id`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 8194| 916|1859|
## 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{cohan-etal-2019-structural,
title = "Structural Scaffolds for Citation Intent Classification in Scientific Publications",
author = "Cohan, Arman and
Ammar, Waleed and
van Zuylen, Madeleine and
Cady, Field",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1361",
doi = "10.18653/v1/N19-1361",
pages = "3586--3596",
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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dali-does/clevr-math | dali-does | 2022-10-31T11:28:31Z | 589 | 5 | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:clevr",
"language:en",
"license:cc-by-4.0",
"reasoning",
"neuro-symbolic",
"multimodal",
"arxiv:2208.05358",
"region:us"
] | [
"visual-question-answering"
] | 2022-08-06T12:09:39Z | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CLEVR-Math - Compositional language, visual, and mathematical reasoning
size_categories:
#- 100K<n<1M
source_datasets: [clevr]
tags:
- reasoning
- neuro-symbolic
- multimodal
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
---
# Dataset Card for CLEVR-Math
## 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)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [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:*https://github.com/dali-does/clevr-math*
- **Paper:*https://arxiv.org/abs/2208.05358*
- **Leaderboard:**
- **Point of Contact:*[email protected]*
### Dataset Summary
Dataset for compositional multimodal mathematical reasoning based on CLEVR.
#### Loading the data, preprocessing text with CLIP
```
from transformers import CLIPPreprocessor
from datasets import load_dataset, DownloadConfig
dl_config = DownloadConfig(resume_download=True,
num_proc=8,
force_download=True)
# Load 'general' instance of dataset
dataset = load_dataset('dali-does/clevr-math', download_config=dl_config)
# Load version with only multihop in test data
dataset_multihop = load_dataset('dali-does/clevr-math', 'multihop',
download_config=dl_config)
model_path = "openai/clip-vit-base-patch32"
extractor = CLIPProcessor.from_pretrained(model_path)
def transform_tokenize(e):
e['image'] = [image.convert('RGB') for image in e['image']]
return extractor(text=e['question'],
images=e['image'],
padding=True)
dataset = dataset.map(transform_tokenize,
batched=True,
num_proc=8,
padding='max_length')
dataset_subtraction = dataset.filter(lambda e:
e['template'].startswith('subtraction'), num_proc=4)
```
### Supported Tasks and Leaderboards
Leaderboard will be announced at a later date.
### Languages
The dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.
## Dataset Structure
### Data Instances
* `general` containing the default version with multihop questions in train and test
* `multihop` containing multihop questions only in test data to test generalisation of reasoning
### Data Fields
```
features = datasets.Features(
{
"template": datasets.Value("string"),
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"image": datasets.Image(),
"label": datasets.Value("int64")
}
)
```
### Data Splits
train/val/test
## Dataset Creation
Data is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.
## Considerations for Using the Data
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Adam Dahlgren Lindström - [email protected]
### Licensing Information
Licensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0).
### Citation Information
[More Information Needed]
```
@misc{https://doi.org/10.48550/arxiv.2208.05358,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren and Abraham, Savitha Sam},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
```
### Contributions
Thanks to [@dali-does](https://github.com/dali-does) for adding this dataset.
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ai4bharat/naamapadam | ai4bharat | 2023-05-24T17:09:03Z | 589 | 4 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:as",
"language:bn",
"language:gu",
"language:hi",
"language:kn",
"language:ml",
"language:mr",
"language:or",
"language:pa",
"language:ta",
"language:te",
"license:cc0-1.0",
"arxiv:2212.10168",
"region:us"
] | [
"token-classification"
] | 2023-01-19T03:17:10Z | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: naamapadam
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for naamapadam
## 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:** https://github.com/AI4Bharat/indicner
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Anoop Kunchukuttan
### Dataset Summary
Naamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.
### Supported Tasks and Leaderboards
**Tasks:** NER on Indian languages.
**Leaderboards:** Currently there is no Leaderboard for this dataset.
### Languages
- `Assamese (as)`
- `Bengali (bn)`
- `Gujarati (gu)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Marathi (mr)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`
## Dataset Structure
### Data Instances
{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],
'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],
}
### Data Fields
- `words`: Raw tokens in the dataset.
- `ner`: the NER tags for this dataset.
### Data Splits
(to be updated, see paper for correct numbers)
| Language | Train | Validation | Test |
|---:|---:|---:|---:|
| as | 10266 | 52 | 51 |
| bn | 961679 | 4859 | 607 |
| gu | 472845 | 2389 | 50 |
| hi | 985787 | 13460 | 437 |
| kn | 471763 | 2381 | 1019 |
| ml | 716652 | 3618 | 974 |
| mr | 455248 | 2300 | 1080 |
| or | 196793 | 993 | 994 |
| pa | 463534 | 2340 | 2342 |
| ta | 497882 | 2795 | 49 |
| te | 507741 | 2700 | 53 |
## Usage
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
```code
pip install datasets
```
To use the dataset, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('ai4bharat/naamapadam')
```
## Dataset Creation
We use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.
### Curation Rationale
naamapadam was built from [Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/). This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.
### Source Data
[Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/)
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
NER annotations were done following the CoNLL-2003 guidelines.
#### Who are the annotators?
The annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:
- Anil Mhaske
- Anoop Kunchukuttan
- Archana Mhaske
- Arnav Mhaske
- Gowtham Ramesh
- Harshit Kedia
- Nitin Kedia
- Rudramurthy V
- Sangeeta Rajagopal
- Sumanth Doddapaneni
- Vindhya DS
- Yash Madhani
- Kabir Ahuja
- Shallu Rani
- Armin Virk
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
<!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100" />
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100" href="http://creativecommons.org/publicdomain/zero/1.0/"/>
</a>
<br/> -->
**CC0 License Statement**
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/>
</a>
<br>
<br>
- We do not own any of the text from which this data has been extracted.
- We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0).
- To the extent possible under law, <a rel="dct:publisher" href="https://ai4bharat.iitm.ac.in/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Naamapadam</span> manually collected data and existing sources.
- This work is published from: India.
### Citation Information
If you are using the Naampadam corpus, please cite the following article:
```
@misc{mhaske2022naamapadam,
doi = {10.48550/ARXIV.2212.10168},
url = {https://arxiv.org/abs/2212.10168},
author = {Mhaske, Arnav and Kedia, Harshit and Doddapaneni, Sumanth and Khapra, Mitesh M. and Kumar, Pratyush and Murthy, Rudra and Kunchukuttan, Anoop},
title = {Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages}
publisher = {arXiv},
year = {2022},
}
```
<!-- Contributors -->
### Contributors
- Arnav Mhaske <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Harshit Kedia <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Sumanth Doddapaneni <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Mitesh M. Khapra <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Pratyush Kumar <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
- Rudra Murthy <sub> ([AI4Bharat](https://ai4bharat.org), [IBM](https://www.ibm.com))</sub>
- Anoop Kunchukuttan <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
This work is the outcome of a volunteer effort as part of the [AI4Bharat initiative](https://ai4bharat.iitm.ac.in).
<!-- Contact -->
### Contact
- Anoop Kunchukuttan ([[email protected]](mailto:[email protected]))
- Rudra Murthy V ([[email protected]](mailto:[email protected])) | [
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kyujinpy/KOpen-platypus | kyujinpy | 2023-11-01T20:18:07Z | 589 | 24 | [
"size_categories:10K<n<100K",
"language:en",
"language:ko",
"license:cc-by-4.0",
"arxiv:2308.07317",
"region:us"
] | null | 2023-08-21T14:59:26Z | ---
language:
- en
- ko
license: cc-by-4.0
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 34213211
num_examples: 24926
download_size: 16662523
dataset_size: 34213211
---
# KOpenPlatypus: Korean Translation dataset about Open-Platypus
## Korean Translation Method
I use [DeepL-pro-API](https://www.deepl.com/ko/pro/change-plan?cta=header-pro#single) and selenium.
It takes about 140h times.
+) 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭
## Korean Translation post-processing
And also, applying post-processing. See below lists. (*약 2000개 이상의 코드 관련 데이터를 수작업으로 수정함)
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. `고유명사`는 최대한 유지함
- 95% 이상의 번역 오류는 전부 고친 것으로 생각됨.
- 약 144h 정도 번역 작업을 진행함. (72h/72h; Translation/Post-processing)
## Introdcution
This dataset is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. It is comprised of the following datasets, which were filtered using keyword search and then Sentence Transformers to remove questions with a similarity above 80%:
| Dataset Name | License Type |
|--------------------------------------------------------------|--------------|
| [PRM800K](https://github.com/openai/prm800k) | MIT |
| [ScienceQA](https://github.com/lupantech/ScienceQA) | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| [SciBench](https://github.com/mandyyyyii/scibench) | MIT |
| [ReClor](https://whyu.me/reclor/) | Non-commercial |
| [TheoremQA](https://huggingface.co/datasets/wenhu/TheoremQA) | MIT |
| [`nuprl/leetcode-solutions-python-testgen-gpt4`](https://huggingface.co/datasets/nuprl/leetcode-solutions-python-testgen-gpt4/viewer/nuprl--leetcode-solutions-python-testgen-gpt4/train?p=1) | None listed |
| [`jondurbin/airoboros-gpt4-1.4.1`](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) | other |
| [`TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k`](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k/viewer/TigerResearch--tigerbot-kaggle-leetcodesolutions-en-2k/train?p=2) | apache-2.0 |
| [openbookQA](https://huggingface.co/datasets/openbookqa/viewer/additional/train?row=35) | apache-2.0 |
| [ARB](https://arb.duckai.org) | MIT |
| [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | apache-2.0 |
## Data Contamination Check
We've removed approximately 200 questions that appear in the Hugging Face benchmark test sets. Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information.
## Model Info
Please see models at [`garage-bAInd`](https://huggingface.co/garage-bAInd).
## Training and filtering code
Please see the [Platypus GitHub repo](https://github.com/arielnlee/Platypus).
## Citations
```bibtex
@article{platypus2023,
title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
booktitle={arXiv preprint arxiv:2308.07317},
year={2023}
}
```
```bibtex
@article{lightman2023lets,
title={Let's Verify Step by Step},
author={Lightman, Hunter and Kosaraju, Vineet and Burda, Yura and Edwards, Harri and Baker, Bowen and Lee, Teddy and Leike, Jan and Schulman, John and Sutskever, Ilya and Cobbe, Karl},
journal={preprint arXiv:2305.20050},
year={2023}
}
```
```bibtex
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
```
```bibtex
@misc{wang2023scibench,
title={SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models},
author={Xiaoxuan Wang and Ziniu Hu and Pan Lu and Yanqiao Zhu and Jieyu Zhang and Satyen Subramaniam and Arjun R. Loomba and Shichang Zhang and Yizhou Sun and Wei Wang},
year={2023},
arXiv eprint 2307.10635
}
```
```bibtex
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
```
```bibtex
@article{chen2023theoremqa,
title={TheoremQA: A Theorem-driven Question Answering dataset},
author={Chen, Wenhu and Ming Yin, Max Ku, Elaine Wan, Xueguang Ma, Jianyu Xu, Tony Xia, Xinyi Wang, Pan Lu},
journal={preprint arXiv:2305.12524},
year={2023}
}
```
```bibtex
@inproceedings{OpenBookQA2018,
title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},
booktitle={EMNLP},
year={2018}
}
```
```bibtex
@misc{sawada2023arb,
title={ARB: Advanced Reasoning Benchmark for Large Language Models},
author={Tomohiro Sawada and Daniel Paleka and Alexander Havrilla and Pranav Tadepalli and Paula Vidas and Alexander Kranias and John J. Nay and Kshitij Gupta and Aran Komatsuzaki},
arXiv eprint 2307.13692,
year={2023}
}
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webis/tldr-17 | webis | 2023-06-05T12:48:30Z | 588 | 26 | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"reddit-posts-summarization",
"region:us"
] | [
"summarization"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: webis-tldr-17-corpus
pretty_name: Reddit Webis-TLDR-17
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
col_mapping:
content: text
summary: target
metrics:
- type: rouge
name: Rouge
tags:
- reddit-posts-summarization
dataset_info:
features:
- name: author
dtype: string
- name: body
dtype: string
- name: normalizedBody
dtype: string
- name: subreddit
dtype: string
- name: subreddit_id
dtype: string
- name: id
dtype: string
- name: content
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 18940542951
num_examples: 3848330
download_size: 3141854161
dataset_size: 18940542951
---
# Dataset Card for Reddit Webis-TLDR-17
## 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://webis.de/data/webis-tldr-17.html](https://webis.de/data/webis-tldr-17.html)
- **Repository:** [https://github.com/webis-de/webis-tldr-17-corpus](https://github.com/webis-de/webis-tldr-17-corpus)
- **Paper:** [https://aclanthology.org/W17-4508]
- **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:** 3.14 GB
- **Size of the generated dataset:** 18.94 GB
- **Total amount of disk used:** 22.08 GB
### Dataset Summary
This corpus contains preprocessed posts from the Reddit dataset (Webis-TLDR-17).
The dataset consists of 3,848,330 posts with an average length of 270 words for content,
and 28 words for the summary.
Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
Content is used as document and summary is used as summary.
### Supported Tasks and Leaderboards
Summarization (abstractive)
Known ROUGE scores achieved for the Webis-TLDR-17:
| Model | ROUGE-1 | ROUGE-2 | ROUGE-L | Paper/Source |
|-------|-------|-------|-------|------:|
| Transformer + Copy (Gehrmann et al., 2019) | 22 | 6 | 17 | Generating Summaries with Finetuned Language Models |
| Unified VAE + PGN (Choi et al., 2019) | 19 | 4 | 15 | VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization |
(Source: https://github.com/sebastianruder/NLP-progress/blob/master/english/summarization.md)
### Languages
English
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 3.14 GB
- **Size of the generated dataset:** 18.94 GB
- **Total amount of disk used:** 22.08 GB
An example of 'train' looks as follows.
```
{
"author": "me",
"body": "<>",
"content": "input document.",
"id": "1",
"normalizedBody": "",
"subreddit": "machinelearning",
"subreddit_id": "2",
"summary": "output summary."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `author`: a `string` feature.
- `body`: a `string` feature.
- `normalizedBody`: a `string` feature.
- `subreddit`: a `string` feature.
- `subreddit_id`: a `string` feature.
- `id`: a `string` feature.
- `content`: a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name | train |
|-------|------:|
|default|3848330|
This corpus does not contain a separate test set. Thus it is up to the users to divide the corpus into appropriate training, validation and test sets.
## Dataset Creation
### Curation Rationale
In the scope of the task of absractive summarization the creators of the Webis-TLDR-17 propose mining social media for author-provided summaries and taking advantage of the common practice of appending a "TL;DR" to long posts. A large Reddit crawl was used to yield the Webis-TLDR-17 corpus. This dataset intends to complement the existing summarization corpora primarily from the news genre.
### Source Data
Reddit subreddits posts (submissions & comments) containing "TL;DR" from 2006 to 2016. Multiple subreddits are included.
#### Initial Data Collection and Normalization
Initial data: a set of 286 million submissions and 1.6 billion comments posted to Reddit between 2006 and 2016.
Then a five-step pipeline of consecutive filtering steps was applied.
#### Who are the source language producers?
The contents of the dataset are produced by human authors, bot-generated content was eliminated by filtering out all bot accounts with the help of an extensive list provided by the Reddit community, as well as manual inspection of cases where the user name contained the substring "bot."
### 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
This dataset has been created to serve as a source of large-scale summarization training data. It is primarily geared towards the automatic abstractive summarization task, that can be considered one of the most challenging variants of automatic summarization. It also aims to tackle the lack of genre diversity in the summarization datasets (most are news-related).
### 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
Reddit users write TL;DRs with various intentions, such as providing a “true” summary, asking questions or for help, or forming judgments and conclusions. As noted in the paper introducing the dataset, although the first kind of TL;DR posts are most important for training summarization models, yet, the latter allow for various alternative summarization-related tasks.
Although filtering was performed abusive language maybe still be present.
## Additional Information
### Dataset Curators
Michael Völske, Martin Potthast, Shahbaz Syed, Benno Stein
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{volske-etal-2017-tl,
title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
author = {V{"o}lske, Michael and
Potthast, Martin and
Syed, Shahbaz and
Stein, Benno},
booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W17-4508",
doi = "10.18653/v1/W17-4508",
pages = "59--63",
abstract = "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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GEM/mlsum | GEM | 2022-10-24T15:30:21Z | 588 | 2 | [
"task_categories:summarization",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:de",
"language:es",
"license:other",
"region:us"
] | [
"summarization"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- de
- es
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: mlsum
---
# Dataset Card for GEM/mlsum
## Dataset Description
- **Homepage:** N/A
- **Repository:** https://gitlab.lip6.fr/scialom/mlsum_data/-/tree/master/MLSUM
- **Paper:** https://aclanthology.org/2020.emnlp-main.647/
- **Leaderboard:** N/A
- **Point of Contact:** Thomas Scialom
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/mlsum).
### Dataset Summary
MLSum is a multilingual summarization dataset crawled from different news websites. The GEM version supports the German and Spanish subset alongside specifically collected challenge sets for COVID-related articles to test out-of-domain generalization.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/mlsum')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/mlsum).
#### website
N/A
#### paper
[ACL Anthology](https://aclanthology.org/2020.emnlp-main.647/)
#### authors
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano
## 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 -->
[Gitlab](https://gitlab.lip6.fr/scialom/mlsum_data/-/tree/master/MLSUM)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.emnlp-main.647/)
#### 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{scialom-etal-2020-mlsum,
title = "{MLSUM}: The Multilingual Summarization Corpus",
author = "Scialom, Thomas and
Dray, Paul-Alexis and
Lamprier, Sylvain and
Piwowarski, Benjamin and
Staiano, Jacopo",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.647",
doi = "10.18653/v1/2020.emnlp-main.647",
pages = "8051--8067",
abstract = "We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages {--} namely, French, German, Spanish, Russian, Turkish. Together with English news articles from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.",
}
```
#### 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 -->
Thomas Scialom
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
{thomas,paul-alexis,jacopo}@recital.ai, {sylvain.lamprier,benjamin.piwowarski}@lip6.fr
#### 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 -->
yes
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
There is only one dialect per language, Hochdeutsch for German and Castilian Spanish for Spanish.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`German`, `Spanish, Castilian`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
The German articles are crawled from Süddeutsche Zeitung and the Spanish ones from El Pais.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
other: Other license
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The intended use of this dataset is to augment existing datasets for English news summarization with additional languages.
#### Add. License Info
<!-- info: What is the 'other' license of the dataset? -->
<!-- scope: periscope -->
Restricted to non-commercial research purposes.
#### 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 -->
The speaker is required to produce a high quality summary of news articles in the same language as the input article.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`other`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
CNRS, Sorbonne Université, reciTAL
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Funding information is not specified.
#### 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 Pedro Henrique Martins (Instituto de Telecomunicações) and Sebastian Gehrmann (Google Research) extended and updated it to the v2 format. The COVID challenge set was created by Laura Perez-Beltrachini (University of Edinburgh). Data cleaning was done by Juan Diego Rodriguez (UT Austin).
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The data fields are:
- `text`: the source article (`string`).
- `summary`: the output summary (`string`).
- `topic`: the topic of the article (`string`).
- `url`: the article's url (`string`).
- `title`: the article's title (`string`).
- `date`: the article's date (`string`).
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The structure follows previously released datasets. The `topic` and `title` fields were added to enable additional tasks like title generation and topic detection.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
They are human written highlights or summaries scraped from the same website.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'date': '00/01/2010',
'gem_id': 'mlsum_de-train-2',
'gem_parent_id': 'mlsum_de-train-2',
'references': [],
'target': 'Oskar Lafontaine gibt den Parteivorsitz der Linken ab - und seine Kollegen streiten, wer ihn beerben soll. sueddeutsche.de stellt die derzeit aussichtsreichsten Anwärter für Führungsaufgaben vor. Mit Vote.',
'text': 'Wenn an diesem Montag die Landesvorsitzenden der Linken über die Nachfolger der derzeitigen Chefs Lothar Bisky und Oskar Lafontaine sowie des Bundesgeschäftsführers Dietmar Bartsch beraten, geht es nicht nur darum, wer die Partei führen soll. Es geht auch um die künftige Ausrichtung und Stärke einer Partei, die vor allem von Lafontaine zusammengehalten worden war. Ihm war es schließlich vor fünf Jahren gelungen, aus der ostdeutschen PDS und der westedeutschen WASG eine Partei zu formen. Eine Partei allerdings, die zerrissen ist in Ost und West, in Regierungswillige und ewige Oppositionelle, in Realos und Ideologen, in gemäßigte und radikale Linke. Wir stellen mögliche Kandidaten vor. Stimmen Sie ab: Wen halten Sie für geeignet und wen für unfähig? Kampf um Lafontaines Erbe: Gregor Gysi Sollte überhaupt jemand die Partei alleine führen, wie es sich viele Ostdeutsche wünschen, käme dafür wohl nur der 62-jährige Gregor Gysi in Betracht. Er ist nach Lafontaine einer der bekanntesten Politiker der Linken und derzeit Fraktionsvorsitzender der Partei im Bundestag. Allerdings ist der ehemalige PDS-Vorsitzende und Rechtsanwalt nach drei Herzinfarkten gesundheitlich angeschlagen. Wahrscheinlich wäre deshalb, dass er die zerstrittene Partei nur übergangsweise führt. Doch noch ist nicht klar, ob eine Person allein die Partei führen soll oder eine Doppelspitze. Viele Linke wünschen sich ein Duo aus einem westdeutschen und einem ostdeutschen Politiker, Mann und Frau. Foto: Getty Images',
'title': 'Personaldebatte bei der Linken - Wer kommt nach Lafontaine?',
'topic': 'politik',
'url': 'https://www.sueddeutsche.de/politik/personaldebatte-bei-der-linken-wer-kommt-nach-lafontaine-1.70041'
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The statistics of the original dataset are:
| | Dataset | Train | Validation | Test | Mean article length | Mean summary length |
| :--- | :----: | :---: | :---: | :---: | :---: | :---: |
| German | 242,982 | 220,887 |11,394 |10,701 |570.6 (words) | 30.36 (words) |
| Spanish | 290,645 | 266,367 |10,358 |13,920 |800.5 (words) |20.71 (words) |
The statistics of the cleaned version of the dataset are:
| | Dataset | Train | Validation | Test |
| :--- | :----: | :---: | :---: | :---: |
| German | 242,835 | 220,887 |11,392 |10,695 |
| Spanish | 283,228 |259,886 |9,977 |13,365 |
The COVID challenge sets have 5058 (de) and 1938 (es) examples.
#### 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 training set contains data from 2010 to 2018. Data from 2019 (~10% of the dataset) is used for validation (up to May) and testing(May-December 2019).
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
Some topics are less represented within the dataset (e.g., Financial news in German and Television in Spanish).
## 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 -->
As the first large-scale multilingual summarization dataset, it enables evaluation of summarization models beyond English.
#### 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 -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
In our configuration, the dataset is fully non-English.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Content Selection, Content Planning, Realization
### 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 removed`, `data points added`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
The modifications done to the original dataset are the following:
- Selection of 2 languages (Spanish and German) out of the dataset 5 languages due to copyright restrictions.
- Removal of duplicate articles.
- Manually removal of article-summary pairs for which the summary is not related to the article.
- Removal of article-summary pairs written in a different language (detected using the [langdetect](https://pypi.org/project/langdetect/) library).
#### 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 -->
For both selected languages (German and Spanish), we compiled time-shifted test data in the form of new articles for the second semester of 2020 with Covid19-related keywords. We collected articles from the same German and Spanish outlets as the original MLSUM datasets (El Pais and Süddeutsche Zeitung). We used the scripts provided for the re-creation of the [MLSUM datasets](https://github.com/recitalAI/MLSUM). The new challenge test set for German contains 5058 instances and the Spanish one contains 1938.
We additionally sample 500 training and validation points as additional challenge sets to measure overfitting.
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
Generalization to unseen topics.
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Content Selection, Content Planning, Realization
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`METEOR`, `ROUGE`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Novelty: Number of generated n-grams not included in the source articles.
#### 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 -->
ROUGE and METEOR both measure n-gram overlap with a focus on recall and are standard summarization metrics. Novelty is often reported alongside them to characterize how much a model diverges from its inputs.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
The GEM benchmark results (https://gem-benchmark.com/results) report a wide range of metrics include lexical overlap metrics but also semantic ones like BLEURT and BERT-Score.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The rationale was to create a multilingual news summarization dataset that mirrors the format of popular English datasets like XSum or CNN/DM.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The speaker is required to produce a high quality summary of news articles in the same language as the input article.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
www.lemonde.fr
www.sueddeutsche.de
www.elpais.com
www.mk.ru
www.internethaber.com
### 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 -->
`Multiple websites`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The language producers are professional journalists.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
4/5 of the original languages report their topics (except Turkish) and the distributions differ between sources. The dominant topics in German are Politik, Sport, Wirtschaft (economy). The dominant topics in Spanish are actualidad (current news) and opinion. French and Russian are different as well but we omit these languages in the GEM version.
#### 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 -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
In the original dataset, only one filter was applied: all the articles shorter than 50 words or summaries shorter than 10 words are discarded.
The GEM version additionally applies langID filter to ensure that articles are in the correct language.
### 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 remains with the original data creators and the usage permission is restricted to non-commercial uses.
### 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 -->
`sensitive information`, `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 -->
no
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] |
cambridgeltl/vsr_zeroshot | cambridgeltl | 2023-03-22T17:27:58Z | 588 | 1 | [
"task_categories:text-classification",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"multimodal",
"vision-and-language",
"arxiv:2205.00363",
"region:us"
] | [
"text-classification",
"question-answering"
] | 2023-03-22T16:42:17Z | ---
license: cc-by-4.0
task_categories:
- text-classification
- question-answering
language:
- en
tags:
- multimodal
- vision-and-language
pretty_name: VSR (zeroshot)
size_categories:
- 1K<n<10K
---
# VSR: Visual Spatial Reasoning
This is the **zero-shot set** of **VSR**: *Visual Spatial Reasoning* (TACL 2023) [[paper]](https://arxiv.org/abs/2205.00363).
### Usage
```python
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_zeroshot", data_files=data_files)
```
Note that the image files still need to be downloaded separately. See [`data/`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data) for details.
Go to our [github repo](https://github.com/cambridgeltl/visual-spatial-reasoning) for more introductions.
### Citation
If you find VSR useful:
```bibtex
@article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={Transactions of the Association for Computational Linguistics},
year={2023},
}
```
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open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.2 | open-llm-leaderboard | 2023-10-28T11:30:38Z | 588 | 1 | [
"region:us"
] | null | 2023-10-10T19:14:44Z | ---
pretty_name: Evaluation run of ICBU-NPU/FashionGPT-70B-V1.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ICBU-NPU/FashionGPT-70B-V1.2](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-28T11:30:26.266910](https://huggingface.co/datasets/open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.2/blob/main/results_2023-10-28T11-30-26.266910.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.007235738255033557,\n\
\ \"em_stderr\": 0.00086796885701786,\n \"f1\": 0.08897126677852359,\n\
\ \"f1_stderr\": 0.0016572567969813893,\n \"acc\": 0.5329529019437246,\n\
\ \"acc_stderr\": 0.011217384303167696\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.007235738255033557,\n \"em_stderr\": 0.00086796885701786,\n\
\ \"f1\": 0.08897126677852359,\n \"f1_stderr\": 0.0016572567969813893\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2403335860500379,\n \
\ \"acc_stderr\": 0.01176958070383695\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8255722178374112,\n \"acc_stderr\": 0.010665187902498444\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|arc:challenge|25_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_28T11_30_26.266910
path:
- '**/details_harness|drop|3_2023-10-28T11-30-26.266910.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-28T11-30-26.266910.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_28T11_30_26.266910
path:
- '**/details_harness|gsm8k|5_2023-10-28T11-30-26.266910.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-28T11-30-26.266910.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T19_14_20.366315
path:
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- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_5
data_files:
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- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-14-20.366315.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T19-14-20.366315.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T19-14-20.366315.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_28T11_30_26.266910
path:
- '**/details_harness|winogrande|5_2023-10-28T11-30-26.266910.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-28T11-30-26.266910.parquet'
- config_name: results
data_files:
- split: 2023_10_10T19_14_20.366315
path:
- results_2023-10-10T19-14-20.366315.parquet
- split: 2023_10_28T11_30_26.266910
path:
- results_2023-10-28T11-30-26.266910.parquet
- split: latest
path:
- results_2023-10-28T11-30-26.266910.parquet
---
# Dataset Card for Evaluation run of ICBU-NPU/FashionGPT-70B-V1.2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ICBU-NPU/FashionGPT-70B-V1.2](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T11:30:26.266910](https://huggingface.co/datasets/open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.2/blob/main/results_2023-10-28T11-30-26.266910.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.007235738255033557,
"em_stderr": 0.00086796885701786,
"f1": 0.08897126677852359,
"f1_stderr": 0.0016572567969813893,
"acc": 0.5329529019437246,
"acc_stderr": 0.011217384303167696
},
"harness|drop|3": {
"em": 0.007235738255033557,
"em_stderr": 0.00086796885701786,
"f1": 0.08897126677852359,
"f1_stderr": 0.0016572567969813893
},
"harness|gsm8k|5": {
"acc": 0.2403335860500379,
"acc_stderr": 0.01176958070383695
},
"harness|winogrande|5": {
"acc": 0.8255722178374112,
"acc_stderr": 0.010665187902498444
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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dane | null | 2023-01-25T14:29:05Z | 587 | 3 | [
"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:1K<n<10K",
"source_datasets:extended|other-Danish-Universal-Dependencies-treebank",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] | [
"token-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-Danish-Universal-Dependencies-treebank
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: dane
pretty_name: DaNE
dataset_info:
features:
- name: sent_id
dtype: string
- name: text
dtype: string
- name: tok_ids
sequence: int64
- name: tokens
sequence: string
- name: lemmas
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': NUM
'1': CCONJ
'2': PRON
'3': VERB
'4': INTJ
'5': AUX
'6': ADJ
'7': PROPN
'8': PART
'9': ADV
'10': PUNCT
'11': ADP
'12': NOUN
'13': X
'14': DET
'15': SYM
'16': SCONJ
- name: morph_tags
sequence: string
- name: dep_ids
sequence: int64
- name: dep_labels
sequence:
class_label:
names:
'0': parataxis
'1': mark
'2': nummod
'3': discourse
'4': compound:prt
'5': reparandum
'6': vocative
'7': list
'8': obj
'9': dep
'10': det
'11': obl:loc
'12': flat
'13': iobj
'14': cop
'15': expl
'16': obl
'17': conj
'18': nmod
'19': root
'20': acl:relcl
'21': goeswith
'22': appos
'23': fixed
'24': obl:tmod
'25': xcomp
'26': advmod
'27': nmod:poss
'28': aux
'29': ccomp
'30': amod
'31': cc
'32': advcl
'33': nsubj
'34': punct
'35': case
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 7311212
num_examples: 4383
- name: test
num_bytes: 909699
num_examples: 565
- name: validation
num_bytes: 940413
num_examples: 564
download_size: 1209710
dataset_size: 9161324
---
# Dataset Card for DaNE
## 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:** [DaNE homepage](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane)
- **Repository:** [Github](https://github.com/alexandrainst/danlp)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.lrec-1.565)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Danish Dependency Treebank (DaNE) is a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme.
The Danish UD treebank (Johannsen et al., 2015, UD-DDT) is a conversion of the Danish Dependency Treebank (Buch-Kromann et al. 2003) based on texts from Parole (Britt, 1998). UD-DDT has annotations for dependency parsing and part-of-speech (POS) tagging. The dataset was annotated with Named Entities for PER, ORG, and LOC by the Alexandra Institute in the DaNE dataset (Hvingelby et al. 2020).
### Supported Tasks and Leaderboards
Parts-of-speech tagging, dependency parsing and named entitity recognition.
### Languages
Danish
## Dataset Structure
### Data Instances
This is an example in the "train" split:
```python
{
'sent_id': 'train-v2-0\n',
'lemmas': ['på', 'fredag', 'have', 'SiD', 'invitere', 'til', 'reception', 'i', 'SID-hus', 'i', 'anledning', 'af', 'at', 'formand', 'Kjeld', 'Christensen', 'gå', 'ind', 'i', 'den', 'glad', 'tresser', '.'],
'dep_labels': [35, 16, 28, 33, 19, 35, 16, 35, 18, 35, 18, 1, 1, 33, 22, 12, 32, 11, 35, 10, 30, 16, 34],
'ner_tags': [0, 0, 0, 3, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0],
'morph_tags': ['AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'Definite=Ind|Number=Sing|Tense=Past|VerbForm=Part', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', 'Definite=Def|Gender=Neut|Number=Sing', 'AdpType=Prep', 'Definite=Ind|Gender=Com|Number=Sing', 'AdpType=Prep', '_', 'Definite=Def|Gender=Com|Number=Sing', '_', '_', 'Mood=Ind|Tense=Pres|VerbForm=Fin|Voice=Act', '_', 'AdpType=Prep', 'Number=Plur|PronType=Dem', 'Degree=Pos|Number=Plur', 'Definite=Ind|Gender=Com|Number=Plur', '_'],
'dep_ids': [2, 5, 5, 5, 0, 7, 5, 9, 7, 11, 7, 17, 17, 17, 14, 15, 11, 17, 22, 22, 22, 18, 5],
'pos_tags': [11, 12, 5, 7, 3, 11, 12, 11, 12, 11, 12, 11, 16, 12, 7, 7, 3, 9, 11, 14, 6, 12, 10],
'text': 'På fredag har SID inviteret til reception i SID-huset i anledning af at formanden Kjeld Christensen går ind i de glade tressere.\n',
'tokens': ['På', 'fredag', 'har', 'SID', 'inviteret', 'til', 'reception', 'i', 'SID-huset', 'i', 'anledning', 'af', 'at', 'formanden', 'Kjeld', 'Christensen', 'går', 'ind', 'i', 'de', 'glade', 'tressere', '.'],
'tok_ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
}
```
### Data Fields
Data Fields:
- q_id: a string question identifier for each example, corresponding to its ID in the Pushshift.io Reddit submission dumps.
- subreddit: One of explainlikeimfive, askscience, or AskHistorians, indicating which subreddit the question came from
- title: title of the question, with URLs extracted and replaced by URL_n tokens
- title_urls: list of the extracted URLs, the nth element of the list was replaced by URL_n
- sent_id: a string identifier for each example
- text: a string, the original sentence (not tokenized)
- tok_ids: a list of ids (int), one for each token
- tokens: a list of strings, the tokens
- lemmas: a list of strings, the lemmas of the tokens
- pos_tags: a list of strings, the part-of-speech tags of the tokens
- morph_tags: a list of strings, the morphological tags of the tokens
- dep_ids: a list of ids (int), the id of the head of the incoming dependency for each token
- dep_labels: a list of strings, the dependency labels
- ner_tags: a list of strings, the named entity tags (BIO format)
### Data Splits
| | train | validation | test |
|-------------|-------:|-----------:|-------:|
| # sentences | 4383 | 564 | 565 |
| # tokens | 80 378 | 10 322 | 10 023 |
## 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]
### Citation Information
```
@inproceedings{hvingelby-etal-2020-dane,
title = "{D}a{NE}: A Named Entity Resource for {D}anish",
author = "Hvingelby, Rasmus and
Pauli, Amalie Brogaard and
Barrett, Maria and
Rosted, Christina and
Lidegaard, Lasse Malm and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.565",
pages = "4597--4604",
abstract = "We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokm{\aa}l (Norwegian) training set compared to only using DaNE. However, the highest performance isachieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@ophelielacroix](https://github.com/ophelielacroix), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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hitorilabs/iris | hitorilabs | 2023-09-07T19:42:41Z | 587 | 0 | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"license:cc0-1.0",
"region:us"
] | [
"tabular-classification"
] | 2023-08-24T21:40:28Z | ---
license: cc0-1.0
size_categories:
- n<1K
task_categories:
- tabular-classification
dataset_info:
features:
- name: petal_length
dtype: float32
- name: petal_width
dtype: float32
- name: sepal_length
dtype: float32
- name: sepal_width
dtype: float32
- name: species
dtype:
class_label:
names:
'0': Iris-setosa
'1': Iris-versicolor
'2': Iris-virginica
splits:
- name: train
num_bytes: 3600
num_examples: 150
download_size: 3835
dataset_size: 3600
configs:
- config_name: default
data_files: data/train-*
---
# Note
The Iris dataset is one of the most popular datasets used for demonstrating simple classification models. This dataset was copied and transformed from `scikit-learn/iris` to be more native to huggingface.
Some changes were made to the dataset to save the user from extra lines of data transformation code, notably:
- removed `id` column
- `species` column is casted to ClassLabel (supports `ClassLabel.int2str()` and `ClassLabel.str2int()`)
- cast feature columns from `float64` down to `float32`
- rename feature names to snake-case
## Iris Species Dataset
The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.
It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.
The dataset is taken from [UCI Machine Learning Repository's Kaggle](https://www.kaggle.com/datasets/uciml/iris).
The following description is taken from UCI Machine Learning Repository.
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Predicted attribute: class of iris plant.
This is an exceedingly simple domain.
This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.
Features in this dataset are the following:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-setosa
- Iris-versicolour
- Iris-virginica | [
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Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647_random | Multimodal-Fatima | 2023-02-04T02:05:09Z | 586 | 0 | [
"region:us"
] | null | 2023-02-04T01:27:48Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_1_bs_16
num_bytes: 86815989.125
num_examples: 5647
- name: fewshot_3_bs_16
num_bytes: 90734149.125
num_examples: 5647
download_size: 169653770
dataset_size: 177550138.25
---
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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lingjoor/databricks-dolly-15k-context-3k-rag | lingjoor | 2023-10-13T09:08:52Z | 586 | 1 | [
"task_categories:table-question-answering",
"license:cc-by-sa-3.0",
"region:us"
] | [
"table-question-answering"
] | 2023-10-13T09:02:30Z | ---
license: cc-by-sa-3.0
task_categories:
- table-question-answering
--- | [
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patrickvonplaten/restore_punctuation_tiny_num_beams_1 | patrickvonplaten | 2022-12-28T21:13:10Z | 585 | 0 | [
"speechbox_punc",
"region:us"
] | null | 2022-12-28T21:09:30Z | ---
tags:
- speechbox_punc
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open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1 | open-llm-leaderboard | 2023-10-29T15:25:02Z | 585 | 0 | [
"region:us"
] | null | 2023-09-19T01:12:41Z | ---
pretty_name: Evaluation run of ICBU-NPU/FashionGPT-70B-V1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ICBU-NPU/FashionGPT-70B-V1](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-29T15:24:49.736716](https://huggingface.co/datasets/open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1/blob/main/results_2023-10-29T15-24-49.736716.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0019924496644295304,\n\
\ \"em_stderr\": 0.0004566676462666993,\n \"f1\": 0.07125104865771813,\n\
\ \"f1_stderr\": 0.0014102826102321945,\n \"acc\": 0.5589478168926856,\n\
\ \"acc_stderr\": 0.011387742640607\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0019924496644295304,\n \"em_stderr\": 0.0004566676462666993,\n\
\ \"f1\": 0.07125104865771813,\n \"f1_stderr\": 0.0014102826102321945\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2812736921910538,\n \
\ \"acc_stderr\": 0.012384789310940239\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8366219415943172,\n \"acc_stderr\": 0.010390695970273764\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|arc:challenge|25_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_29T15_24_49.736716
path:
- '**/details_harness|drop|3_2023-10-29T15-24-49.736716.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-29T15-24-49.736716.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_29T15_24_49.736716
path:
- '**/details_harness|gsm8k|5_2023-10-29T15-24-49.736716.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-29T15-24-49.736716.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hellaswag|10_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-12-17.792946.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-19T01-12-17.792946.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-19T01-12-17.792946.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_29T15_24_49.736716
path:
- '**/details_harness|winogrande|5_2023-10-29T15-24-49.736716.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-29T15-24-49.736716.parquet'
- config_name: results
data_files:
- split: 2023_09_19T01_12_17.792946
path:
- results_2023-09-19T01-12-17.792946.parquet
- split: 2023_10_29T15_24_49.736716
path:
- results_2023-10-29T15-24-49.736716.parquet
- split: latest
path:
- results_2023-10-29T15-24-49.736716.parquet
---
# Dataset Card for Evaluation run of ICBU-NPU/FashionGPT-70B-V1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ICBU-NPU/FashionGPT-70B-V1](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T15:24:49.736716](https://huggingface.co/datasets/open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1/blob/main/results_2023-10-29T15-24-49.736716.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0019924496644295304,
"em_stderr": 0.0004566676462666993,
"f1": 0.07125104865771813,
"f1_stderr": 0.0014102826102321945,
"acc": 0.5589478168926856,
"acc_stderr": 0.011387742640607
},
"harness|drop|3": {
"em": 0.0019924496644295304,
"em_stderr": 0.0004566676462666993,
"f1": 0.07125104865771813,
"f1_stderr": 0.0014102826102321945
},
"harness|gsm8k|5": {
"acc": 0.2812736921910538,
"acc_stderr": 0.012384789310940239
},
"harness|winogrande|5": {
"acc": 0.8366219415943172,
"acc_stderr": 0.010390695970273764
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
-0.4396580755710602,
-0.6758861541748047,
0.12209320813417435,
0.2767820656299591,
-0.2664850652217865,
0.1779296100139618,
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Cartinoe5930/KoRAE_filtered_12k | Cartinoe5930 | 2023-11-09T13:40:09Z | 585 | 1 | [
"arxiv:2307.08701",
"region:us"
] | null | 2023-11-07T02:19:29Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: review
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 35802556
num_examples: 12473
download_size: 18374150
dataset_size: 35802556
---
# KoRAE Dataset
<p align="center"><img src="https://cdn-uploads.huggingface.co/production/uploads/63e087b6a98d931aa90c1b9c/VVHlw268vUEdRAzUtjDhE.png", width=256, height=256></p>
We used filtered high-quality Korean dataset for finetuning of KoRAE.
First of all, we gathered Korean data and make the mixture of them.
Then we filtered high-quality data from the combination of data through filtering method that introduced from [AlpaGasus](https://arxiv.org/abs/2307.08701).
The overview of data processing procedure is as follws:
1. Collect various Korean dataset from HuggingFace Hub.
2. Rate the data quality using `gpt-3.5-turbo`.
3. Process the rated data and filter the high-scored data.
Let's go deeper into data processing!
### 1. Korean dataset mixture
We investigated several sources to collect high-quality Korean data, and among them, we collected data from the various sources.
As a result, we were able to create a new dataset containing 64K pieces of data.
The specific configuration of the dataset is as follows:
|Dataset|# Nums|
|---|---|
|**[OpenOrca-ko](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO)**|21.6k|
|**[KOpen-Platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus)**|24.9k|
|**[KoCoT_2000](https://huggingface.co/datasets/kyujinpy/KoCoT_2000)**|2.1k|
|**[databricks-dolly-15k-ko](https://huggingface.co/datasets/nlpai-lab/databricks-dolly-15k-ko)**|15k|
|**Total**|63.7k|
You can check the original KoRAE dataset here: [KoRAE_original](https://huggingface.co/datasets/Cartinoe5930/KoRAE_original)
### 2. Rating
We utilized ChatGPT(gpt-3.5-turbo) as rater to rate the quality of dataset.
We considered whether to use the prompt for the evaluation in Korean or English, but we thought it would be undesirable to give evaluations in different languages, so we conducted the evaluation using the Korean prompt.
The overall data rating method was reffered to [AlpaGasus](https://arxiv.org/abs/2307.08701)
### 3. Processing & Filtering
We postprocessed rated dataset after the rating.
The main postprocessing procedure are as follows:
- Wrong score extraction correction
- Incorrect format dataset exclusion
You can check the postprocessed KoRAE dataset here: [KoRAE_rated_filtered](https://huggingface.co/datasets/Cartinoe5930/KoRAE_rated_filtered)
After the all postprocessing, we analysed the score distribution of rated dataset.
As shown in the following figure, it was confirmed that 8-point data was the most.
This confirms that KoRAE dataset consisted of high-quality data from the beginning.
However, We filtered data only with a score of 8.5 or higher and used it to finetune KoRAE for better performance.
As a result, we were able to filter the dataset 64k to 12k!
This 'KoRAE_filtered_12k' is the result of all previous processes.
## Github Repository
For the more specific information, please check the following [Repository](https://github.com/gauss5930/KoRAE)
## Citation
Thanks to [@kyujinpy](https://huggingface.co/kyujinpy) and [@nlp-ai](https://huggingface.co/nlpai-lab) for providing Korean datasets.
- [KO-Platypus](https://github.com/Marker-Inc-Korea/KO-Platypus)
- [Korean-OpenOrca](https://github.com/Marker-Inc-Korea/Korean-OpenOrca)
```
@inproceedings{lee2023kullm,
title={KULLM: Learning to Construct Korean Instruction-following Large Language Models},
author={Lee, SeungJun and Lee, Taemin and Lee, Jeongwoo and Jang, Yoona and Lim, Heuiseok},
booktitle={Annual Conference on Human and Language Technology},
pages={196--202},
year={2023},
organization={Human and Language Technology}
}
```
```
@misc{chen2023alpagasus,
title={AlpaGasus: Training A Better Alpaca with Fewer Data},
author={Lichang Chen and Shiyang Li and Jun Yan and Hai Wang and Kalpa Gunaratna and Vikas Yadav and Zheng Tang and Vijay Srinivasan and Tianyi Zhou and Heng Huang and Hongxia Jin},
year={2023},
eprint={2307.08701},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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air_dialogue | null | 2022-11-03T16:31:11Z | 584 | 6 | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-generation",
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | [
"conversational",
"text-generation",
"fill-mask"
] | 2022-03-02T23:29:22Z | ---
pretty_name: AirDialogue
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- conversational
- text-generation
- fill-mask
task_ids:
- dialogue-generation
- dialogue-modeling
- language-modeling
- masked-language-modeling
paperswithcode_id: null
dataset_info:
- config_name: air_dialogue_data
features:
- name: action
struct:
- name: status
dtype: string
- name: name
dtype: string
- name: flight
sequence: int32
- name: intent
struct:
- name: return_month
dtype: string
- name: return_day
dtype: string
- name: max_price
dtype: int32
- name: departure_airport
dtype: string
- name: max_connections
dtype: int32
- name: departure_day
dtype: string
- name: goal
dtype: string
- name: departure_month
dtype: string
- name: name
dtype: string
- name: return_airport
dtype: string
- name: timestamps
sequence: int64
- name: dialogue
sequence: string
- name: expected_action
struct:
- name: status
dtype: string
- name: name
dtype: string
- name: flight
sequence: int32
- name: search_info
list:
- name: button_name
dtype: string
- name: field_name
dtype: string
- name: field_value
dtype: string
- name: timestmamp
dtype: int64
- name: correct_sample
dtype: bool_
splits:
- name: train
num_bytes: 353721137
num_examples: 321459
- name: validation
num_bytes: 44442238
num_examples: 40363
download_size: 272898923
dataset_size: 398163375
- config_name: air_dialogue_kb
features:
- name: kb
list:
- name: airline
dtype: string
- name: class
dtype: string
- name: departure_airport
dtype: string
- name: departure_day
dtype: string
- name: departure_month
dtype: string
- name: departure_time_num
dtype: int32
- name: flight_number
dtype: int32
- name: num_connections
dtype: int32
- name: price
dtype: int32
- name: return_airport
dtype: string
- name: return_day
dtype: string
- name: return_month
dtype: string
- name: return_time_num
dtype: int32
- name: reservation
dtype: int32
splits:
- name: train
num_bytes: 782592158
num_examples: 321459
- name: validation
num_bytes: 98269789
num_examples: 40363
download_size: 272898923
dataset_size: 880861947
---
# Dataset Card for air_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:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
- **Repository:** https://github.com/google/airdialogue
- **Paper:** https://www.aclweb.org/anthology/D18-1419/
- **Leaderboard:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
- **Point of Contact:** [AirDialogue-Google](mailto:[email protected])
[Aakash Gupta](mailto:[email protected])
### Dataset Summary
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
### Supported Tasks and Leaderboards
We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores
The inference competition & leaderboard can be found here:
https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
### Languages
The text in the dataset is in English. The BCP 47 code is `en`
## Dataset Structure
### Data Instances
The data is provided in two set of files. The first one has the dialogues (`air_dialogue_data`) and the knowledge-base (`air_dialogue_kb`)
BuilderConfig: `air_dialogue_data`
```
{"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true}
```
BuilderConfig: `air_dialogue_kb`
```
{"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0}
```
### Data Fields
BuilderConfig: `air_dialogue_data`:
Provides for customer context, dialogue states and environment
key name | Description |
|---|---|
|'search_action' | search action performed by customer |
|'action' | Action taken by the agent |
|'intent' | Intents from the conversation |
|'timestamps' | Timestamp for each of the dialogues |
|'dialogue' | Dialogue recorded between agent & customer |
|'expected_action' | Expected action from agent (human-annotated)|
|'correct_sample' | whether action performed by agent was same as expected_action |
BuilderConfig: `air_dialogue_kb`:
Provides for the Agent Context _ca_ = (_db_, _r_ )
key name | Description |
|---|---|
|'kb' | Available flights in the database |
|'reservation' | whether customer has an existing reservation|
### Data Splits
Data is split into Train/Dev & Test in the ration of 80%, 10% and 10%
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
No personal and sensitive information is stored
## 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
[AirDialogue team](mailto:[email protected])
For issues regarding HuggingFace Dataset Hub implementation [Aakash Gupta](mailto:[email protected])
### Licensing Information
cc-by-nc-4.0
### Citation Information
@inproceedings{wei-etal-2018-airdialogue,
title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
author = "Wei, Wei and
Le, Quoc and
Dai, Andrew and
Li, Jia",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1419",
doi = "10.18653/v1/D18-1419",
pages = "3844--3854",
abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.",
}
### Contributions
Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset. | [
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europa_ecdc_tm | null | 2022-11-03T16:31:26Z | 584 | 1 | [
"task_categories:translation",
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"language:sv",
"license:cc-by-sa-4.0",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
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multilinguality:
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size_categories:
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paperswithcode_id: null
pretty_name: EuropaEcdcTm
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num_examples: 2527
download_size: 4286636
dataset_size: 546349
---
# 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:** [https://ec.europa.eu/jrc/en/language-technologies/ecdc-translation-memory](https://ec.europa.eu/jrc/en/language-technologies/ecdc-translation-memory)
- **Paper:** [https://link.springer.com/article/10.1007/s10579-014-9277-0](https://link.springer.com/article/10.1007/s10579-014-9277-0)
- **Point of Contact:** [Ralf Steinberger](mailto:[email protected])
### Dataset Summary
In October 2012, the European Union (EU) agency 'European Centre for Disease Prevention and Control' (ECDC) released a translation memory (TM), i.e. a collection of sentences and their professionally produced translations, in twenty-five languages.
ECDC-TM covers 25 languages: the 23 official languages of the EU plus Norwegian (Norsk) and Icelandic. ECDC-TM was created by translating from English into the following 24 languages: Bulgarian, Czech, Danish, Dutch, English, Estonian, Gaelige (Irish), German, Greek, Finnish, French, Hungarian, Icelandic, Italian, Latvian, Lithuanian, Maltese, Norwegian (NOrsk), Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish and Swedish.
All documents and sentences were originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg.
To load a language pair that is not part of the config, just specify the language code as language pair. For example, if you want to translate Czech to Greek:
`dataset = load_dataset("europa_ecdc_tm", language_pair=("cs", "el"))`
### Supported Tasks and Leaderboards
- `text2text-generation`: the dataset can be used to train a model for `machine-translation`. Machine translation models are usually evaluated using metrics such as [BLEU](https://huggingface.co/metrics/bleu), [ROUGE](https://huggingface.co/metrics/rouge) or [SacreBLEU](https://huggingface.co/metrics/sacrebleu). You can use the [mBART](https://huggingface.co/facebook/mbart-large-cc25) model for this task. This task has active leaderboards which can be found at [https://paperswithcode.com/task/machine-translation](https://paperswithcode.com/task/machine-translation), which usually rank models based on [BLEU score](https://huggingface.co/metrics/bleu).
### Languages
All documents and sentences were originally written in English (`en`). They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg.
Translations are available in these languages: `en`, `bg`, `cs`, `da`, `de`, `el`, `en`, `es`, `et`, `fi`, `fr`, `ga`, `hu`, `is`, `it`, `lt`, `lv`, `mt`, `nl`, `no`, `pl`, `pt`, `ro`, `sk`, `sl`, `sv`.
## Dataset Structure
### Data Instances
```
{
"translation": {
"<source_language>":"Sentence to translate",
"<target_language>": "Translated sentence",
},
}
```
### Data Fields
- `translation`: a multilingual `string` variable, with possible languages including `en`, `bg`, `cs`, `da`, `de`, `el`, `en`, `es`, `et`, `fi`, `fr`, `ga`, `hu`, `is`, `it`, `lt`, `lv`, `mt`, `nl`, `no`, `pl`, `pt`, `ro`, `sk`, `sl`, `sv`.
### Data Splits
The data is not splitted (only the `train` split is available).
## Dataset Creation
### Curation Rationale
The ECDC-TM is relatively small compared to the JRC-Acquis and to DGT-TM, but it has the advantage that it focuses on a very different domain, namely that of public health. Also, it includes translation units for the languages Irish (Gaelige, GA), Norwegian (Norsk, NO) and Icelandic (IS).
### Source Data
#### Initial Data Collection and Normalization
ECDC-TM was built on the basis of the website of the European Centre for Disease Prevention and Control (ECDC). The major part of the documents talks about health-related topics (anthrax, botulism, cholera, dengue fever, hepatitis, etc.), but some of the web pages also describe the organisation ECDC (e.g. its organisation, job opportunities) and its activities (e.g. epidemic intelligence, surveillance).
#### Who are the source language producers?
All documents and sentences were originally written in English, by the ECDC website content producers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
All documents and sentences were thus originally written in English. They were then translated into the other languages by professional translators from the Translation Centre CdT in Luxembourg.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
Contains translations of sentences in the public healthcare domain, including technical terms (disease and treatment names for example).
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Copyright © EU / ECDC, 2020
#### Copyright
The Work (as defined below) is provided under the terms of this Licence (or later versions of
this Licence published by the European Commission). The work is protected by copyright
and/or other applicable law. Any use of the work other than as authorised under this
Licence or copyright law is prohibited.
The terms provided herein conform to the reuse policy established by the Commission's
Reuse Decision (2011/833/EU).
By exercising any rights to the work provided here, you accept and agree to be bound by the
terms of this Licence. The Owner (as defined below) grants You the rights conferred by this
Licence in consideration of your acceptance of such terms and conditions.
#### Definitions
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Commission and the European Centre for Disease Prevention and Control, which are the
original licensors and/or control the copyright and any other intellectual and industrial
property rights related to the Work.
The ‘Work’ is the information and/or data offered to You under this Licence, according to
the ‘Copyright Notice’:
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acquiring rights under this Licence.
‘Use’ means any act which is restricted by copyright or database rights, whether in the
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#### Rights
You are herewith granted a worldwide, royalty‐free, perpetual, non‐exclusive Licence to Use
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a) Unmodified distributions must retain the above Copyright Notice;
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#### No warranty
Each Work is provided ‘as is’ without, to the full extent permitted by law, representations,
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Except in the cases of wilful misconduct or damages directly caused to natural persons, the
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### Citation Information
```
@Article{Steinberger2014,
author={Steinberger, Ralf
and Ebrahim, Mohamed
and Poulis, Alexandros
and Carrasco-Benitez, Manuel
and Schl{\"u}ter, Patrick
and Przybyszewski, Marek
and Gilbro, Signe},
title={An overview of the European Union's highly multilingual parallel corpora},
journal={Language Resources and Evaluation},
year={2014},
month={Dec},
day={01},
volume={48},
number={4},
pages={679-707},
issn={1574-0218},
doi={10.1007/s10579-014-9277-0},
url={https://doi.org/10.1007/s10579-014-9277-0}
}
```
### Contributions
Thanks to [@SBrandeis](https://github.com/SBrandeis) for adding this dataset. | [
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squad_v1_pt | null | 2023-04-05T13:40:41Z | 583 | 7 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:mit",
"arxiv:1606.05250",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- pt
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: SquadV1Pt
dataset_info:
features:
- name: id
dtype: string
- name: title
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: 85323237
num_examples: 87599
- name: validation
num_bytes: 11265474
num_examples: 10570
download_size: 39532595
dataset_size: 96588711
---
# Dataset Card for "squad_v1_pt"
## 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/nunorc/squad-v1.1-pt](https://github.com/nunorc/squad-v1.1-pt)
- **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:** 39.53 MB
- **Size of the generated dataset:** 96.72 MB
- **Total amount of disk used:** 136.25 MB
### Dataset Summary
Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API.
### 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:** 39.53 MB
- **Size of the generated dataset:** 96.72 MB
- **Total amount of disk used:** 136.25 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [0],
"text": ["Saint Bernadette Soubirous"]
},
"context": "\"Arquitetonicamente, a escola tem um caráter católico. No topo da cúpula de ouro do edifício principal é uma estátua de ouro da ...",
"id": "5733be284776f41900661182",
"question": "A quem a Virgem Maria supostamente apareceu em 1858 em Lourdes, na França?",
"title": "University_of_Notre_Dame"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `title`: 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 | validation |
| ------- | ----: | ---------: |
| default | 87599 | 10570 |
## 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
```
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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nickrosh/Evol-Instruct-Code-80k-v1 | nickrosh | 2023-07-11T02:05:26Z | 582 | 110 | [
"license:cc-by-nc-sa-4.0",
"arxiv:2306.08568",
"region:us"
] | null | 2023-07-08T04:31:37Z | ---
license: cc-by-nc-sa-4.0
---
Open Source Implementation of Evol-Instruct-Code as described in the [WizardCoder Paper](https://arxiv.org/pdf/2306.08568.pdf).
Code for the intruction generation can be found on Github as [Evol-Teacher](https://github.com/nickrosh/evol-teacher).
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gilkeyio/librispeech-alignments | gilkeyio | 2023-11-22T16:03:34Z | 582 | 0 | [
"task_categories:automatic-speech-recognition",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"arxiv:1512.02595",
"region:us"
] | [
"automatic-speech-recognition"
] | 2023-11-22T04:15:29Z | ---
dataset_info:
features:
- name: sex
dtype: string
- name: subset
dtype: string
- name: id
dtype: string
- name: audio
dtype: audio
- name: transcript
dtype: string
- name: words
list:
- name: end
dtype: float64
- name: start
dtype: float64
- name: word
dtype: string
- name: phonemes
list:
- name: end
dtype: float64
- name: phoneme
dtype: string
- name: start
dtype: float64
splits:
- name: dev_clean
num_bytes: 365310608.879
num_examples: 2703
- name: dev_other
num_bytes: 341143993.784
num_examples: 2864
- name: test_clean
num_bytes: 377535532.98
num_examples: 2620
- name: test_other
num_bytes: 351207892.569557
num_examples: 2938
- name: train_clean_100
num_bytes: 6694747231.610863
num_examples: 28538
- name: train_clean_360
num_bytes: 24163659711.787865
num_examples: 104008
- name: train_other_500
num_bytes: 32945085271.89443
num_examples: 148645
download_size: 62101682957
dataset_size: 65238690243.50571
configs:
- config_name: default
data_files:
- split: dev_clean
path: data/dev_clean-*
- split: dev_other
path: data/dev_other-*
- split: test_clean
path: data/test_clean-*
- split: test_other
path: data/test_other-*
- split: train_clean_100
path: data/train_clean_100-*
- split: train_clean_360
path: data/train_clean_360-*
- split: train_other_500
path: data/train_other_500-*
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
pretty_name: Librispeech Alignments
size_categories:
- 100K<n<1M
---
# Dataset Card for Librispeech Alignments
Librispeech with alignments generated by the [Montreal Forced Aligner](https://montreal-forced-aligner.readthedocs.io/en/latest/). The original alignments in TextGrid format can be found [here](https://zenodo.org/records/2619474)
## Dataset Details
### Dataset Description
Librispeech is a corpus of read English speech, designed for training and evaluating automatic speech recognition (ASR) systems. The dataset contains 1000 hours of 16kHz read English speech derived from audiobooks.
The Montreal Forced Aligner (MFA) was used to generate word and phoneme level alignments for the Librispeech dataset.
- **Curated by:** Vassil Panayotov, Guoguo Chen, Daniel Povey, Sanjeev Khudanpur (for Librispeech)
- **Funded by:** DARPA LORELEI
- **Shared by:** Loren Lugosch (for Alignments)
- **Language(s) (NLP):** English
- **License:** Creative Commons Attribution 4.0 International License
### Dataset Sources
- **Repository:** https://www.openslr.org/12
- **Paper:** https://arxiv.org/abs/1512.02595
- **Alignments:** https://zenodo.org/record/2619474
## Uses
### Direct Use
The Librispeech dataset can be used to train and evaluate ASR systems. The alignments allow for forced alignment techniques.
### Out-of-Scope Use
The dataset only contains read speech, so may not perform as well on spontaneous conversational speech.
## Dataset Structure
The dataset contains 1000 hours of segmented read English speech from audiobooks. There are three train subsets: 100 hours (train-clean-100), 360 hours (train-clean-360) and 500 hours (train-other-500).
The alignments connect the audio to the reference text transcripts on word and phoneme level.
### Data Fields
- sex: M for male, F for female
- subset: dev_clean, dev_other, test_clean, test_other, train_clean_100, train_clean_360, train_other_500
- id: unique id of the data sample. (speaker id)-(chapter-id)-(utterance-id)
- audio: the audio, 16kHz
- transcript: the spoken text of the dataset, normalized and lowercased
- words: a list of words with fields:
- word: the text of the word
- start: the start time in seconds
- end: the end time in seconds
- phonemes: a list of phonemes with fields:
- phoneme: the phoneme spoken
- start: the start time in seconds
- end: the end time in seconds
## Dataset Creation
### Curation Rationale
Librispeech was created to further speech recognition research and to benchmark progress in the field.
### Source Data
#### Data Collection and Processing
The audio and reference texts were sourced from read English audiobooks in the LibriVox project. The data was segmented, filtered and prepared for speech recognition.
#### Who are the source data producers?
The audiobooks are read by volunteers for the LibriVox project. Information about the readers is available in the LibriVox catalog.
### Annotations
#### Annotation process
The Montreal Forced Aligner was used to create word and phoneme level alignments between the audio and reference texts. The aligner is based on Kaldi.
In the process of formatting this into a HuggingFace dataset, words with empty text and phonemes with empty text, silence tokens, or spacing tokens were removed
#### Who are the annotators?
The alignments were generated automatically by the Montreal Forced Aligner and shared by Loren Lugosch. The TextGrid files were parsed and integrated into this dataset by Kim Gilkey.
#### Personal and Sensitive Information
The data contains read speech and transcripts. No personal or sensitive information expected.
## Bias, Risks, and Limitations
The dataset contains only read speech from published books, not natural conversational speech. Performance on other tasks may be reduced.
### Recommendations
Users should understand that the alignments may contain errors and account for this in applications. For example, be wary of <UNK> tokens.
## Citation
**Librispeech:**
```
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={ICASSP},
year={2015},
organization={IEEE}
}
```
**Librispeech Alignments:**
```
Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio, "Speech Model Pre-training for End-to-End Spoken Language Understanding", Interspeech 2019.
```
**Montreal Forced Aligner:**
```
Michael McAuliffe, Michaela Socolof, Sarah Mihuc, Michael Wagner, and Morgan Sonderegger. "Montreal Forced Aligner: trainable text-speech alignment using Kaldi", Interspeech 2017.
``` | [
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AdapterOcean/Open_Platypus_standardized_cluster_2_alpaca | AdapterOcean | 2023-10-23T20:49:41Z | 580 | 0 | [
"region:us"
] | null | 2023-10-22T23:16:46Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 5719356
num_examples: 5147
download_size: 0
dataset_size: 5719356
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Open_Platypus_standardized_cluster_2_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
IconicAI/janet-24oct | IconicAI | 2023-11-17T15:21:15Z | 580 | 0 | [
"task_categories:conversational",
"region:us"
] | [
"conversational"
] | 2023-11-02T19:33:22Z | ---
task_categories:
- conversational
---
---
tags:
- gpt-4
- janet
---
# Description
39 Multi-Turn conversations generated by GPT-4.
Each conversation represents a distinct dialogue between two people -
a sniper and a central operator called Janet.
The context of each conversation is the same - the sniper is perched on a rooftop,
on a mission to eliminate a dangerous person that's hiding in the crowd below.
Janet knows how that person looks like and is talking the sniper through the process
of identifying and eliminating the target.
# Schema
```
{
"id": string
"conversation": [{
"from": string, # system - system-prompt, assistant - LLM, user - human
"value": string, # dialogue line
}]
}
```
# Implementation details
Original format in `.txt`, Axolotl-compatible ShareGPT version is `.sharegpt.jsonl`.
# Citation
```
@misc{Janet24Oct,
title = {Janet24Oct: A Dataset of Multi-Turn Conversations for Conversational AI},
author = {Kieran Donaldson and Mikel Bober-Izar and Piotr Trochim},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/datasets/IconicAI/janet-24oct}},
}
``` | [
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C-MTEB/CMedQAv2-reranking | C-MTEB | 2023-07-28T07:17:06Z | 579 | 0 | [
"region:us"
] | null | 2023-07-28T07:16:41Z | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: test
num_bytes: 30417770
num_examples: 1000
download_size: 19720976
dataset_size: 30417770
---
# Dataset Card for "CMedQAv2-reranking"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
yentinglin/TC-Eval | yentinglin | 2023-12-01T02:38:36Z | 579 | 3 | [
"task_categories:question-answering",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:zh",
"arxiv:2311.17487",
"region:us"
] | [
"question-answering",
"text-classification"
] | 2023-10-24T15:58:14Z | ---
task_categories:
- question-answering
- text-classification
language:
- zh
pretty_name: TMLU
size_categories:
- 1K<n<10K
configs:
- config_name: FGC
data_files:
- split: test
path: "fgc.jsonl"
- config_name: DRCD
data_files:
- split: test
path: "drcd.jsonl"
- config_name: TMMLU
data_files:
- split: test
path: "tmmlu.jsonl"
- config_name: XSUM_TC
data_files:
- split: test
path: "xsum.jsonl"
- config_name: TTQA
data_files:
- split: test
path: TTQA_mc.jsonl
- config_name: TCIC
data_files:
- split: test
path: TCIC_1.2.0.json
- config_name: PenguinsInTable_TC
data_files:
- split: test
path: PenguinsInTable_TC.jsonl
- config_name: IMDB_TC
data_files:
- split: test
path: imdb.jsonl
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
## Performance
Data source: https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval
# Citation for Taiwan LLM
```
@misc{lin2023taiwan,
title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model},
author={Yen-Ting Lin and Yun-Nung Chen},
year={2023},
eprint={2311.17487},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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