datasetId
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| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
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| likes
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sequence | task_categories
sequence | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
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|---|---|---|---|---|---|---|---|---|---|
rusheeliyer/german-courts | rusheeliyer | 2023-11-14T15:21:58Z | 579 | 0 | [
"region:us"
] | null | 2023-11-01T10:46:49Z | ---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
configs:
- config_name: bundesfinanzhof
data_files:
- split: train
path: data/Bundesfinanzhof_train.csv
- split: test
path: data/Bundesfinanzhof_test.csv
- split: validation
path: data/Bundesfinanzhof_val.csv
- config_name: bundesgerichtshof
data_files:
- split: train
path: data/Bundesgerichtshof_train.csv
- split: test
path: data/Bundesgerichtshof_test.csv
- split: validation
path: data/Bundesgerichtshof_val.csv
- config_name: bundesarbeitsgericht
data_files:
- split: train
path: data/Bundesarbeitsgericht_train.csv
- split: test
path: data/Bundesarbeitsgericht_test.csv
- split: validation
path: data/Bundesarbeitsgericht_val.csv
- config_name: bundessozialgericht
data_files:
- split: train
path: data/Bundessozialgericht_train.csv
- split: test
path: data/Bundessozialgericht_test.csv
- split: validation
path: data/Bundessozialgericht_val.csv
- config_name: bundesverwaltungsgericht
data_files:
- split: train
path: data/Bundesverwaltungsgericht_train.csv
- split: test
path: data/Bundesverwaltungsgericht_test.csv
- split: validation
path: data/Bundesverwaltungsgericht_val.csv
- config_name: bundesverfassungsgericht
data_files:
- split: train
path: data/Bundesverfassungsgericht_train.csv
- split: test
path: data/Bundesverfassungsgericht_test.csv
- split: validation
path: data/Bundesverfassungsgericht_val.csv
---
# Dataset Card for Dataset Name
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## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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### Direct Use
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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[More Information Needed] | [
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argilla/oasst_response_comparison | argilla | 2023-11-29T11:23:51Z | 578 | 0 | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-06-30T07:54:14Z | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for oasst_response_comparison
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.cfg`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/oasst_response_comparison")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/oasst_response_comparison")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| prompt | Prompt | TextField | True | True |
| reply_1 | Reply 1 | TextField | True | True |
| reply_2 | Reply 2 | TextField | True | True |
| reply_3 | Reply 3 | TextField | True | True |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| reply-ranking | Order replies based on your preference | RankingQuestion | True | Order the replies from 1 to 3 based on your preference.
Ties are possible. | N/A |
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"prompt": "Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.",
"reply_1": "\"Monopsony\" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\n\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens \u0026 Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\n\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\n\nReferences:\nBivens, J., \u0026 Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.",
"reply_2": "Monopsony is a market structure in which there is a single buyer in a market. In the context of labor markets, a monopsony occurs when there is only one employer in a particular industry or geographic area, giving that employer significant power over the wages and working conditions of workers. This results in a situation where the employer has the ability to suppress wages and reduce the bargaining power of workers.\n\nResearch has shown that the existence of monopsony power in labor markets can have negative effects on workers, such as lower wages, reduced benefits, and limited job opportunities. For example, a study by Autor, Dorn, and Hanson (2013) found evidence of monopsony power in certain U.S. labor markets, resulting in lower wages and reduced job opportunities for workers. Similarly, another study by Bharadwaj, Black, and Kolesnikova (2019) found that the presence of a single dominant employer in a local labor market was associated with lower wages for workers.",
"reply_3": "Monopsony refers to a market structure where there is only one buyer of a good or service. In the context of labor markets, a monopsony occurs when there is only one employer in a particular geographic area or industry, giving them significant power to set wages and working conditions. Research has shown that the existence of monopsonies can lead to lower wages and reduced job opportunities for workers. For example, a study by the National Bureau of Economic Research found that in industries with high levels of concentration, workers earn lower wages and are less likely to receive benefits such as health insurance."
},
"metadata": null,
"responses": [
{
"status": "submitted",
"user_id": "3e760b76-e19a-480a-b436-a85812b98843",
"values": {
"reply-ranking": {
"value": [
{
"rank": 3,
"value": "reply_1"
},
{
"rank": 3,
"value": "reply_2"
},
{
"rank": 1,
"value": "reply_3"
}
]
}
}
}
]
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": null,
"prompt": "Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.",
"reply-ranking": {
"status": [
"submitted"
],
"user_id": [
"3e760b76-e19a-480a-b436-a85812b98843"
],
"value": [
{
"rank": [
3,
3,
1
],
"value": [
"reply_1",
"reply_2",
"reply_3"
]
}
]
},
"reply_1": "\"Monopsony\" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\n\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens \u0026 Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\n\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\n\nReferences:\nBivens, J., \u0026 Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.",
"reply_2": "Monopsony is a market structure in which there is a single buyer in a market. In the context of labor markets, a monopsony occurs when there is only one employer in a particular industry or geographic area, giving that employer significant power over the wages and working conditions of workers. This results in a situation where the employer has the ability to suppress wages and reduce the bargaining power of workers.\n\nResearch has shown that the existence of monopsony power in labor markets can have negative effects on workers, such as lower wages, reduced benefits, and limited job opportunities. For example, a study by Autor, Dorn, and Hanson (2013) found evidence of monopsony power in certain U.S. labor markets, resulting in lower wages and reduced job opportunities for workers. Similarly, another study by Bharadwaj, Black, and Kolesnikova (2019) found that the presence of a single dominant employer in a local labor market was associated with lower wages for workers.",
"reply_3": "Monopsony refers to a market structure where there is only one buyer of a good or service. In the context of labor markets, a monopsony occurs when there is only one employer in a particular geographic area or industry, giving them significant power to set wages and working conditions. Research has shown that the existence of monopsonies can lead to lower wages and reduced job opportunities for workers. For example, a study by the National Bureau of Economic Research found that in industries with high levels of concentration, workers earn lower wages and are less likely to receive benefits such as health insurance."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **prompt** is of type `TextField`.
* **reply_1** is of type `TextField`.
* **reply_2** is of type `TextField`.
* **reply_3** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
* **reply-ranking** is of type `RankingQuestion`, and description "Order the replies from 1 to 3 based on your preference.
Ties are possible.".
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
For each promt, order the replies in a ranking based on how clear and helpful you find each reply. Ties are allowed. If you prefer not to give an answer, click Discard and move on to the next record.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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tasksource/folio | tasksource | 2023-05-31T13:40:30Z | 577 | 5 | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"language:en",
"license:cc",
"arxiv:2209.00840",
"region:us"
] | [
"text-classification"
] | 2023-02-21T08:15:17Z | ---
license: cc
task_categories:
- text-classification
language:
- en
task_ids:
- natural-language-inference
- multi-input-text-classification
---
https://github.com/Yale-LILY/FOLIO
```
@article{han2022folio,
title={FOLIO: Natural Language Reasoning with First-Order Logic},
author = {Han, Simeng and Schoelkopf, Hailey and Zhao, Yilun and Qi, Zhenting and Riddell, Martin and Benson, Luke and Sun, Lucy and Zubova, Ekaterina and Qiao, Yujie and Burtell, Matthew and Peng, David and Fan, Jonathan and Liu, Yixin and Wong, Brian and Sailor, Malcolm and Ni, Ansong and Nan, Linyong and Kasai, Jungo and Yu, Tao and Zhang, Rui and Joty, Shafiq and Fabbri, Alexander R. and Kryscinski, Wojciech and Lin, Xi Victoria and Xiong, Caiming and Radev, Dragomir},
journal={arXiv preprint arXiv:2209.00840},
url = {https://arxiv.org/abs/2209.00840},
year={2022}
}
``` | [
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lamini/lamini_docs_evaluation | lamini | 2023-07-24T03:08:13Z | 577 | 0 | [
"region:us"
] | null | 2023-07-24T03:08:09Z | ---
dataset_info:
features:
- name: predicted_answer
dtype: string
- name: target_answer
dtype: string
splits:
- name: train
num_bytes: 744520
num_examples: 139
download_size: 86086
dataset_size: 744520
---
# Dataset Card for "lamini_docs_evaluation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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conv_ai_2 | null | 2022-11-03T16:31:09Z | 576 | 29 | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"evaluating-dialogue-systems",
"arxiv:1902.00098",
"region:us"
] | [
"conversational",
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: convai2
pretty_name: Conversational Intelligence Challenge 2
tags:
- evaluating-dialogue-systems
dataset_info:
features:
- name: id
dtype: string
- name: dialog_id
dtype: string
- name: dialog
list:
- name: id
dtype: int32
- name: sender
dtype: string
- name: text
dtype: string
- name: sender_class
dtype: string
- name: bot_profile
sequence:
list: string
- name: user_profile
sequence:
list: string
- name: eval_score
dtype: int32
- name: profile_match
dtype: int32
config_name: conv_ai_2
splits:
- name: train
num_bytes: 8403805
num_examples: 3495
download_size: 6636788
dataset_size: 8403805
---
# Dataset Card for conv_ai_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://github.com/DeepPavlov/convai/tree/master/2018
- **Repository:** https://github.com/DeepPavlov/convai/tree/master/2018
- **Paper:** https://arxiv.org/abs/1902.00098
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
ConvAI is a dataset of human-to-bot conversations labeled for quality. This data can be used to train a metric for evaluating dialogue systems. Moreover, it can be used in the development of chatbots themselves: it contains information on the quality of utterances and entire dialogues, that can guide a dialogue system in search of better answers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```
{
"dialog_id": "0x648cc5b7",
"dialog": [
{
"id": 0,
"sender": "participant2",
"text": "Hi! How is your day? \ud83d\ude09",
"sender_class": "Bot"
},
{
"id": 1,
"sender": "participant1",
"text": "Hi! Great!",
"sender_class": "Human"
},
{
"id": 2,
"sender": "participant2",
"text": "I am good thanks for asking are you currently in high school?",
"sender_class": "Bot"
}
],
"bot_profile": [
"my current goal is to run a k.",
"when i grow up i want to be a physical therapist.",
"i'm currently in high school.",
"i make straight as in school.",
"i won homecoming queen this year."
],
"user_profile": [
"my favorite color is red.",
"i enjoy listening to classical music.",
"i'm a christian.",
"i can drive a tractor."
],
"eval_score": 4,
"profile_match": 1
}
```
### Data Fields
- dialog_id : specifies the unique ID for the dialogs.
- dialog : Array of dialogs.
- bot_profile : Bot annotated response that will be used for evaluation.
- user_profile : user annoted response that will be used for evaluation.
- eval_score : (`1`,` 2`,` 3`,` 4`,` 5`) how does an user like a conversation. The missing values are replaced with` -1`
- profile_match : (`0`,` 1`) an user is given by two profile descriptions (4 sentences each), one of them is the one given to the bot it had been talking to, the other one is random; the user needs to choose one of them.The missing values are replaced with` -1`
### 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
@article{DBLP:journals/corr/abs-1902-00098,
author = {Emily Dinan and
Varvara Logacheva and
Valentin Malykh and
Alexander H. Miller and
Kurt Shuster and
Jack Urbanek and
Douwe Kiela and
Arthur Szlam and
Iulian Serban and
Ryan Lowe and
Shrimai Prabhumoye and
Alan W. Black and
Alexander I. Rudnicky and
Jason Williams and
Joelle Pineau and
Mikhail S. Burtsev and
Jason Weston},
title = {The Second Conversational Intelligence Challenge (ConvAI2)},
journal = {CoRR},
volume = {abs/1902.00098},
year = {2019},
url = {http://arxiv.org/abs/1902.00098},
archivePrefix = {arXiv},
eprint = {1902.00098},
timestamp = {Wed, 07 Oct 2020 11:09:41 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1902-00098.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | [
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discofuse | null | 2023-04-05T10:04:50Z | 575 | 4 | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"sentence-fusion",
"arxiv:1902.10526",
"region:us"
] | [
"text2text-generation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: DiscoFuse
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: discofuse
tags:
- sentence-fusion
dataset_info:
- config_name: discofuse-sport
features:
- name: connective_string
dtype: string
- name: discourse_type
dtype: string
- name: coherent_second_sentence
dtype: string
- name: has_coref_type_pronoun
dtype: float32
- name: incoherent_first_sentence
dtype: string
- name: incoherent_second_sentence
dtype: string
- name: has_coref_type_nominal
dtype: float32
- name: coherent_first_sentence
dtype: string
splits:
- name: train
num_bytes: 14736279993
num_examples: 43291020
- name: test
num_bytes: 151656323
num_examples: 445521
- name: validation
num_bytes: 150207737
num_examples: 440902
download_size: 4326637746
dataset_size: 15038144053
- config_name: discofuse-wikipedia
features:
- name: connective_string
dtype: string
- name: discourse_type
dtype: string
- name: coherent_second_sentence
dtype: string
- name: has_coref_type_pronoun
dtype: float32
- name: incoherent_first_sentence
dtype: string
- name: incoherent_second_sentence
dtype: string
- name: has_coref_type_nominal
dtype: float32
- name: coherent_first_sentence
dtype: string
splits:
- name: train
num_bytes: 6377924196
num_examples: 16310585
- name: test
num_bytes: 64008158
num_examples: 163657
- name: validation
num_bytes: 65682035
num_examples: 168081
download_size: 1717422334
dataset_size: 6507614389
---
# Dataset Card for "discofuse"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/google-research-datasets/discofuse
- **Paper:** [DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion](https://arxiv.org/abs/1902.10526)
- **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:** 6.04 GB
- **Size of the generated dataset:** 21.55 GB
- **Total amount of disk used:** 27.59 GB
### Dataset Summary
DiscoFuse is a large scale dataset for discourse-based sentence fusion.
### 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
#### discofuse-sport
- **Size of downloaded dataset files:** 4.33 GB
- **Size of the generated dataset:** 15.04 GB
- **Total amount of disk used:** 19.36 GB
An example of 'train' looks as follows.
```
{
"coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .",
"connective_string": "finally ,",
"discourse_type": "PAIR_CONN",
"has_coref_type_nominal": 0.0,
"has_coref_type_pronoun": 0.0,
"incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ."
}
```
#### discofuse-wikipedia
- **Size of downloaded dataset files:** 1.72 GB
- **Size of the generated dataset:** 6.51 GB
- **Total amount of disk used:** 8.23 GB
An example of 'validation' looks as follows.
```
{
"coherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"coherent_second_sentence": "Finally , an HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner .",
"connective_string": "finally ,",
"discourse_type": "PAIR_CONN",
"has_coref_type_nominal": 0.0,
"has_coref_type_pronoun": 0.0,
"incoherent_first_sentence": "Four LPr and three LC2000r HP Netservers handle customer management and web server functions .",
"incoherent_second_sentence": "An HP Netserver LT6000r hosts i2 Demand Planner and i2 Collaboration Planner ."
}
```
### Data Fields
The data fields are the same among all splits.
#### discofuse-sport
- `connective_string`: a `string` feature.
- `discourse_type`: a `string` feature.
- `coherent_second_sentence`: a `string` feature.
- `has_coref_type_pronoun`: a `float32` feature.
- `incoherent_first_sentence`: a `string` feature.
- `incoherent_second_sentence`: a `string` feature.
- `has_coref_type_nominal`: a `float32` feature.
- `coherent_first_sentence`: a `string` feature.
#### discofuse-wikipedia
- `connective_string`: a `string` feature.
- `discourse_type`: a `string` feature.
- `coherent_second_sentence`: a `string` feature.
- `has_coref_type_pronoun`: a `float32` feature.
- `incoherent_first_sentence`: a `string` feature.
- `incoherent_second_sentence`: a `string` feature.
- `has_coref_type_nominal`: a `float32` feature.
- `coherent_first_sentence`: a `string` feature.
### Data Splits
| name | train |validation| test |
|-------------------|-------:|---------:|-----:|
|discofuse-sport |43291020| 440902|445521|
|discofuse-wikipedia|16310585| 168081|163657|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The data is licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@InProceedings{GevaEtAl2019,
title = {DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion},
author = {Geva, Mor and Malmi, Eric and Szpektor, Idan and Berant, Jonathan},
booktitle = {Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
note = {arXiv preprint arXiv:1902.10526},
year = {2019}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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oscar-corpus/OSCAR-2201 | oscar-corpus | 2023-05-30T07:48:15Z | 574 | 77 | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:original",
"language:af",
"language:sq",
"language:am",
"language:ar",
"language:an",
"language:hy",
"language:as",
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"language:eu",
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"language:br",
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"language:my",
"language:ca",
"language:ceb",
"language:ckb",
"language:ce",
"language:zh",
"language:cv",
"language:kw",
"language:hr",
"language:cs",
"language:da",
"language:diq",
"language:dv",
"language:nl",
"language:mhr",
"language:arz",
"language:en",
"language:eo",
"language:et",
"language:tl",
"language:fi",
"language:fr",
"language:gl",
"language:ka",
"language:de",
"language:gom",
"language:el",
"language:gn",
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"license:cc0-1.0",
"arxiv:2010.14571",
"arxiv:2201.06642",
"arxiv:2103.12028",
"region:us"
] | [
"fill-mask",
"text-generation"
] | 2022-03-14T23:09:14Z | ---
pretty_name: OSCAR
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- sq
- am
- ar
- an
- hy
- as
- ast
- av
- az
- bn
- ba
- eu
- be
- bh
- bpy
- bs
- br
- bg
- my
- ca
- ceb
- ckb
- ce
- zh
- cv
- kw
- hr
- cs
- da
- diq
- dv
- nl
- mhr
- arz
- en
- eo
- et
- tl
- fi
- fr
- gl
- ka
- de
- gom
- el
- gn
- gu
- he
- hi
- hu
- is
- io
- ilo
- id
- ia
- ga
- it
- ja
- jv
- xal
- kn
- krc
- kk
- km
- kv
- ko
- ku
- ky
- lo
- la
- lv
- lez
- li
- lt
- jbo
- lmo
- nds
- dsb
- lb
- mk
- mai
- mg
- ms
- ml
- mt
- mr
- mzn
- min
- xmf
- mn
- nah
- ne
- new
- no
- nn
- oc
- or
- os
- ps
- fa
- pms
- pl
- pt
- pa
- qu
- ro
- bxr
- ru
- sah
- sa
- gd
- sr
- sh
- scn
- sd
- si
- sk
- sl
- so
- azb
- es
- su
- sw
- sv
- tg
- ta
- tt
- te
- th
- bo
- als
- tr
- tk
- uk
- eml
- hsb
- ur
- ug
- uz
- vi
- vo
- wa
- war
- cy
- fy
- mrj
- pnb
- wuu
- yi
- yo
- mul
license:
- cc0-1.0
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
---
# Dataset Card for "oscar"
## 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://oscar-corpus.com](https://oscar-corpus.com)
- **Repository:** [github.com/oscar-corpus/corpus](https://github.com/oscar-corpus/corpus)
- **Paper:** [Towards a Cleaner Document-Oriented Multilingual Crawled Corpus](https://oscar-corpus.com/publication/2022/arxiv/towards/)
- **Point of Contact:** [Contact](https://oscar-corpus.com/#contact)
### Dataset Summary
OSCAR or **O**pen **S**uper-large **C**rawled **A**ggregated co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [ungoliant](https://github.com/oscar-corpus/ungoliant) architecture. Data is distributed by language in both original and deduplicated form.
**We are aware of the virus warnings issue. See discussion [here](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201/discussions/12) for more info!**
### Usage
```py
from datasets import load_dataset
dataset = load_dataset("oscar-corpus/OSCAR-2201",
use_auth_token=True, # required
language="ar",
streaming=True, # optional
split="train") # optional, but the dataset only has a train split
for d in dataset:
print(d) # prints documents
```
### Supported Tasks and Leaderboards
OSCAR is mainly intended to pretrain language models and word representations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 151 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
### Issues
OSCAR 22.01 may have quality issues on low size subcorpora, as it has been the case before.
Note that since the documents are identified as a whole, it is expected to have lines in other languages in a given language subcorpus.
As an example, it is known and expected that the German subcorpus contains documents holding lines identified as Swiss German / Alemannic.
**If you encounter something that is unexpected, please file an issue here: https://github.com/oscar-corpus/corpus/issues.**
|Language code|Language|Issues|
|-------------|--------|------|
| | | |
## Dataset Structure
We show detailed information for all the configurations of the dataset.
### Data Instances
TODO
### Data Fields
* `id`: a `int64` feature.
* `content`: `string` Newline-separated content
* `warc_headers`: WARC Headers
* `warc_headers.content-length`: `int64` Content length (in bytes) **before** cleaning
* `warc_headers.content-type`: `string` MIME type
* `warc_headers.warc-block-digest`:`string` Algorithm name and calculated value of a digest applied to the full block of the record
* `warc_headers.warc-date`: `string` Crawl date (YYYY-MM-DDThh:mm:ssZ)
* `warc_headers.warc-identified-content-language`: `string` Comma-separated list of language identifications done by CommonCrawl (uses CLD3)
* `warc_headers.warc-record-id`: `string` Record ID
* `warc_headers.warc-refers-to`: `string` Record-ID of a single record for which the present record holds additional content
* `warc_headers.warc-target-uri`: `string` URI from where the content has been fetched
* `warc_headers.warc-type`: `string` Type of the WARC Record
* `metadata`: Metadata
* `metadata.identification.label`: `string` Language identification of the document
* `metadata.identification.prob`: `float` Confidence of the identification
* `metadata.annotation`: `[string]` Annnotations of the document. `null` if none present. (Is `None` if using `datasets`)
* `metadata.sentence_identifications`: `[string]` List of line identifications. `null`/`None` can be present for lines that failed the identification step.
* `meta.offset`: `int64` line offset where the related text begins. Should be used with `meta.nb_sentences` when reading the source files rather than using iterators to get related data.
* `text`: `string` content
See the [WARC Format standard](https://iipc.github.io/warc-specifications/specifications/warc-format/warc-1.1/#warc-type-mandatory) for more details on the `warc_headers` fields, and our [website](https://oscar-corpus.com/post/oscar-v22-01/) for more details about the format in general.
### Data Splits
<details>
<summary>Click to expand the number of samples per configuration</summary>
</details>
## Table
| lang | size | docs | words |
|:----------------------------|:----------|:------------|:----------------|
| _Multilingual_ | 12.1 GB | 1,210,685 | 936,187,711 |
| Afrikaans | 47.0 MB | 12,393 | 6,227,310 |
| Albanian | 3.0 GB | 437,287 | 326,325,149 |
| Alemannic / Swiss German | 363.6 kB | 139 | 37,381 |
| Amharic | 461.0 MB | 37,513 | 30,481,153 |
| Arabic | 84.2 GB | 8,718,929 | 6,103,711,887 |
| Aragonese | 10.6 kB | 12 | 51 |
| Armenian | 4.7 GB | 379,267 | 268,031,270 |
| Assamese | 221.2 MB | 17,084 | 11,109,557 |
| Asturian | 73.6 kB | 77 | 3,919 |
| Avaric | 18.6 kB | 14 | 582 |
| Azerbaijani | 3.5 GB | 491,847 | 291,927,692 |
| Bangla | 15.1 GB | 1,171,501 | 751,877,226 |
| Bashkir | 95.5 MB | 11,198 | 5,418,474 |
| Basque | 1.1 GB | 233,658 | 97,092,942 |
| Belarusian | 1.8 GB | 180,046 | 107,227,860 |
| Bihari languages | 24.2 kB | 27 | 569 |
| Bishnupriya | 2.0 MB | 271 | 98,419 |
| Bosnian | 10.3 kB | 10 | 422 |
| Breton | 33.7 MB | 16,119 | 3,111,619 |
| Bulgarian | 35.1 GB | 2,887,115 | 2,405,981,285 |
| Burmese | 1.9 GB | 158,733 | 44,835,970 |
| Catalan | 13.9 GB | 2,627,307 | 1,508,919,864 |
| Cebuano | 44.6 MB | 5,742 | 5,253,785 |
| Central Kurdish | 716.4 MB | 84,950 | 43,913,025 |
| Chechen | 14.0 MB | 4,086 | 798,766 |
| Chinese | 900.9 GB | 56,524,518 | 23,149,203,886 |
| Chuvash | 41.8 MB | 4,750 | 2,465,782 |
| Cornish | 1.4 kB | 2 | 55 |
| Croatian | 11.2 MB | 11,462 | 505,369 |
| Czech | 58.6 GB | 10,381,916 | 5,452,724,456 |
| Danish | 12.6 GB | 2,265,479 | 1,454,439,292 |
| Dimli (individual language) | 706 Bytes | 1 | 19 |
| Divehi | 217.2 MB | 24,067 | 10,112,205 |
| Dutch | 114.0 GB | 20,206,532 | 12,329,127,151 |
| Eastern Mari | 11.3 MB | 1,612 | 641,525 |
| Egyptian Arabic | 2.8 MB | 1,256 | 176,096 |
| English | 3.2 TB | 431,992,659 | 377,376,402,775 |
| Esperanto | 558.3 MB | 111,932 | 58,416,628 |
| Estonian | 9.2 GB | 1,362,524 | 820,975,443 |
| Filipino | 646.5 MB | 70,394 | 81,881,278 |
| Finnish | 37.8 GB | 4,948,961 | 2,900,615,928 |
| French | 382.2 GB | 52,037,098 | 41,713,990,658 |
| Galician | 255.2 MB | 88,803 | 27,051,212 |
| Georgian | 7.1 GB | 488,588 | 281,430,479 |
| German | 496.7 GB | 70,075,424 | 46,826,676,844 |
| Goan Konkani | 787.2 kB | 46 | 38,831 |
| Greek | 78.3 GB | 6,738,546 | 5,031,242,803 |
| Guarani | 9.0 kB | 10 | 374 |
| Gujarati | 4.8 GB | 136,467 | 301,170,777 |
| Hebrew | 30.3 GB | 3,132,396 | 2,249,377,984 |
| Hindi | 23.3 GB | 1,529,907 | 1,534,799,198 |
| Hungarian | 53.9 GB | 6,866,062 | 4,598,787,907 |
| Icelandic | 2.0 GB | 396,183 | 210,365,124 |
| Ido | 77.3 kB | 105 | 2,690 |
| Iloko | 97.9 kB | 75 | 8,592 |
| Indonesian | 17.4 GB | 2,244,622 | 1,984,195,207 |
| Interlingua | 40.2 kB | 6 | 10,125 |
| Irish | 45.6 MB | 12,233 | 4,877,850 |
| Italian | 229.3 GB | 28,502,092 | 24,294,684,830 |
| Japanese | 258.7 GB | 36,328,931 | 5,592,948,356 |
| Javanese | 152.7 kB | 70 | 10,441 |
| Kalmyk | 9.3 kB | 9 | 250 |
| Kannada | 2.6 GB | 150,850 | 108,450,571 |
| Karachay-Balkar | 119.6 kB | 91 | 4,089 |
| Kazakh | 2.9 GB | 261,085 | 157,267,307 |
| Khmer | 1.9 GB | 121,910 | 30,564,131 |
| Komi | 119.9 kB | 127 | 3,335 |
| Korean | 51.8 GB | 5,881,481 | 3,854,968,649 |
| Kurdish | 150.3 MB | 29,906 | 17,390,759 |
| Kyrgyz | 518.6 MB | 62,244 | 28,028,986 |
| Lao | 337.1 MB | 28,914 | 6,682,982 |
| Latin | 4.1 MB | 4,397 | 187,446 |
| Latvian | 8.2 GB | 1,032,987 | 707,361,898 |
| Lezghian | 375.5 kB | 124 | 19,250 |
| Limburgish | 1.4 kB | 2 | 41 |
| Lithuanian | 20.0 GB | 2,303,070 | 1,712,802,056 |
| Lojban | 1.9 MB | 570 | 260,542 |
| Lombard | 2.6 kB | 2 | 225 |
| Low German | 9.0 MB | 1,938 | 1,012,561 |
| Lower Sorbian | 707 Bytes | 1 | 17 |
| Luxembourgish | 15.8 MB | 5,108 | 1,545,946 |
| Macedonian | 3.6 GB | 341,775 | 244,058,579 |
| Maithili | 21.6 kB | 23 | 483 |
| Malagasy | 57.3 MB | 3,028 | 7,279,056 |
| Malay | 5.3 MB | 5,228 | 217,818 |
| Malayalam | 4.1 GB | 250,972 | 137,831,247 |
| Maltese | 2.5 MB | 2,208 | 118,190 |
| Marathi | 3.3 GB | 250,376 | 160,179,233 |
| Mazanderani | 128.2 kB | 76 | 7,337 |
| Minangkabau | 6.0 MB | 585 | 614,613 |
| Mingrelian | 7.6 MB | 2,550 | 253,333 |
| Mongolian | 2.8 GB | 237,719 | 176,405,432 |
| Nahuatl languages | 8.7 kB | 12 | 179 |
| Nepali | 3.7 GB | 391,947 | 177,885,116 |
| Newari | 5.7 MB | 1,134 | 273,837 |
| Norwegian | 2.8 GB | 973,188 | 279,182,902 |
| Norwegian Nynorsk | 6.8 MB | 5,835 | 459,183 |
| Occitan | 2.1 MB | 373 | 31,061 |
| Odia | 487.9 MB | 52,942 | 23,755,902 |
| Ossetic | 13.9 MB | 3,560 | 800,430 |
| Pashto | 490.3 MB | 50,312 | 46,293,249 |
| Persian | 77.4 GB | 7,665,871 | 6,430,164,396 |
| Piedmontese | 1.7 MB | 698 | 188,270 |
| Polish | 139.0 GB | 19,301,137 | 12,584,498,906 |
| Portuguese | 170.3 GB | 23,735,707 | 18,441,864,893 |
| Punjabi | 1.1 GB | 68,094 | 70,068,604 |
| Quechua | 744 Bytes | 1 | 14 |
| Romanian | 49.2 GB | 4,624,764 | 5,261,803,995 |
| Russia Buriat | 32.9 kB | 39 | 785 |
| Russian | 1.1 TB | 76,060,844 | 62,811,122,663 |
| Sakha | 65.6 MB | 6,284 | 3,473,813 |
| Sanskrit | 136.0 MB | 4,472 | 5,671,369 |
| Scottish Gaelic | 137.7 kB | 136 | 7,769 |
| Serbian | 6.9 GB | 577,472 | 482,932,670 |
| Serbian (Latin) | 931.8 kB | 738 | 92,875 |
| Sicilian | 1.5 kB | 2 | 50 |
| Sindhi | 117.1 MB | 15,516 | 10,685,611 |
| Sinhala | 2.0 GB | 108,593 | 113,179,741 |
| Slovak | 16.5 GB | 2,409,555 | 1,619,121,944 |
| Slovenian | 1.2 GB | 351,894 | 118,400,246 |
| Somali | 2.1 kB | 3 | 109 |
| South Azerbaijani | 14.1 MB | 5,381 | 693,746 |
| Spanish | 381.9 GB | 51,386,247 | 42,829,835,316 |
| Sundanese | 5.0 MB | 263 | 547,145 |
| Swahili | 1.3 MB | 462 | 123,050 |
| Swedish | 48.0 GB | 7,541,278 | 5,078,331,128 |
| Tajik | 870.9 MB | 46,366 | 56,627,727 |
| Tamil | 11.4 GB | 556,772 | 452,343,748 |
| Tatar | 915.3 MB | 76,398 | 51,875,265 |
| Telugu | 3.4 GB | 249,756 | 137,752,065 |
| Thai | 66.1 GB | 5,030,254 | 1,626,779,846 |
| Tibetan | 234.5 MB | 18,683 | 2,286,269 |
| Turkish | 75.1 GB | 10,826,031 | 6,421,221,358 |
| Turkmen | 4.4 MB | 2,485 | 276,632 |
| Ukrainian | 48.8 GB | 4,558,214 | 2,879,585,992 |
| Emiliano-Romagnolo[eml] | 901 Bytes | 1 | 53 |
| Upper Sorbian | 132.8 kB | 110 | 8,825 |
| Urdu | 3.4 GB | 336,994 | 332,816,354 |
| Uyghur | 201.9 MB | 18,556 | 11,240,889 |
| Uzbek | 19.9 MB | 9,526 | 1,370,842 |
| Vietnamese | 98.9 GB | 9,587,233 | 12,283,185,482 |
| Volapük | 825.9 kB | 661 | 57,039 |
| Walloon | 105.7 kB | 138 | 4,386 |
| Waray | 7.6 MB | 933 | 830,872 |
| Welsh | 409.3 MB | 90,378 | 49,488,495 |
| Western Frisian | 75.3 MB | 21,946 | 6,357,929 |
| Western Mari | 743.5 kB | 155 | 43,916 |
| Western Panjabi | 46.7 MB | 6,790 | 4,060,419 |
| Wu Chinese | 137.2 kB | 88 | 3,056 |
| Yiddish | 232.5 MB | 23,418 | 15,809,780 |
| Yoruba | 24.7 kB | 26 | 1,042 |
## Dataset Creation
### Curation Rationale
OSCAR was constructed using [`Ungoliant`](https://github.com/oscar-corpus/ungoliant), a new pipeline derived from [goclassy](https://github.com/oscar-corpus/goclassy), itself being derived from [fastText's one](https://github.com/facebookresearch/fastText).
The pipeline works on documents rather than lines.
`Ungoliant` is implemented in the [Rust programming language](https://rust-lang.org), and uses [rayon](https://github.com/rayon-rs/rayon) as its data parallelism strategy.
Threading is done at shard, record and sentence level, making the whole generation process much more efficient.
Filtering will be explained in a future blog post at our [website](https://oscar-corpus.com)
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR 22.01, the **November/December 2021** snapshot was used. It is composed by 64 000 compressed text files containing documents and their headers.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
The corpus was put together by [Julien Abadji](https://ujj.space), [Pedro Ortiz Suarez](https://portizs.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@ARTICLE{2022arXiv220106642A,
author = {{Abadji}, Julien and {Ortiz Suarez}, Pedro and {Romary}, Laurent and {Sagot}, Beno{\^\i}t},
title = "{Towards a Cleaner Document-Oriented Multilingual Crawled Corpus}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language},
year = 2022,
month = jan,
eid = {arXiv:2201.06642},
pages = {arXiv:2201.06642},
archivePrefix = {arXiv},
eprint = {2201.06642},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220106642A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{AbadjiOrtizSuarezRomaryetal.2021,
author = {Julien Abadji and Pedro Javier Ortiz Su{\'a}rez and Laurent Romary and Beno{\^i}t Sagot},
title = {Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9) 2021. Limerick, 12 July 2021 (Online-Event)},
editor = {Harald L{\"u}ngen and Marc Kupietz and Piotr Bański and Adrien Barbaresi and Simon Clematide and Ines Pisetta},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-10468},
url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-104688},
pages = {1 -- 9},
year = {2021},
abstract = {Since the introduction of large language models in Natural Language Processing, large raw corpora have played a crucial role in Computational Linguistics. However, most of these large raw corpora are either available only for English or not available to the general public due to copyright issues. Nevertheless, there are some examples of freely available multilingual corpora for training Deep Learning NLP models, such as the OSCAR and Paracrawl corpora. However, they have quality issues, especially for low-resource languages. Moreover, recreating or updating these corpora is very complex. In this work, we try to reproduce and improve the goclassy pipeline used to create the OSCAR corpus. We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data. Also, unlike OSCAR, the metadata information is at the document level. We release our pipeline under an open source license and publish the corpus under a research-only license.},
language = {en}
}
@ARTICLE{caswell-etal-2021-quality,
author = {{Caswell}, Isaac and {Kreutzer}, Julia and {Wang}, Lisa and {Wahab}, Ahsan and {van Esch}, Daan and {Ulzii-Orshikh}, Nasanbayar and {Tapo}, Allahsera and {Subramani}, Nishant and {Sokolov}, Artem and {Sikasote}, Claytone and {Setyawan}, Monang and {Sarin}, Supheakmungkol and {Samb}, Sokhar and {Sagot}, Beno{\^\i}t and {Rivera}, Clara and {Rios}, Annette and {Papadimitriou}, Isabel and {Osei}, Salomey and {Ortiz Su{\'a}rez}, Pedro Javier and {Orife}, Iroro and {Ogueji}, Kelechi and {Niyongabo}, Rubungo Andre and {Nguyen}, Toan Q. and {M{\"u}ller}, Mathias and {M{\"u}ller}, Andr{\'e} and {Hassan Muhammad}, Shamsuddeen and {Muhammad}, Nanda and {Mnyakeni}, Ayanda and {Mirzakhalov}, Jamshidbek and {Matangira}, Tapiwanashe and {Leong}, Colin and {Lawson}, Nze and {Kudugunta}, Sneha and {Jernite}, Yacine and {Jenny}, Mathias and {Firat}, Orhan and {Dossou}, Bonaventure F.~P. and {Dlamini}, Sakhile and {de Silva}, Nisansa and {{\c{C}}abuk Ball{\i}}, Sakine and {Biderman}, Stella and {Battisti}, Alessia and {Baruwa}, Ahmed and {Bapna}, Ankur and {Baljekar}, Pallavi and {Abebe Azime}, Israel and {Awokoya}, Ayodele and {Ataman}, Duygu and {Ahia}, Orevaoghene and {Ahia}, Oghenefego and {Agrawal}, Sweta and {Adeyemi}, Mofetoluwa},
title = "{Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence},
year = 2021,
month = mar,
eid = {arXiv:2103.12028},
pages = {arXiv:2103.12028},
archivePrefix = {arXiv},
eprint = {2103.12028},
primaryClass = {cs.CL},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210312028C},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
### Contributions
Thanks to [@pjox](https://github.com/pjox), [@Uinelj](https://github.com/Uinelj) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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Ela279/langchain_docs | Ela279 | 2023-05-26T09:42:36Z | 574 | 0 | [
"license:mit",
"region:us"
] | null | 2023-05-26T09:30:25Z | ---
license: mit
---
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scb_mt_enth_2020 | null | 2022-11-18T21:43:37Z | 573 | 3 | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:found",
"annotations_creators:machine-generated",
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"language:en",
"language:th",
"license:cc-by-sa-4.0",
"arxiv:2007.03541",
"arxiv:1909.05858",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
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language_creators:
- expert-generated
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language:
- en
- th
license:
- cc-by-sa-4.0
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: scb-mt-en-th-2020
pretty_name: ScbMtEnth2020
dataset_info:
- config_name: enth
features:
- name: translation
dtype:
translation:
languages:
- en
- th
- name: subdataset
dtype: string
splits:
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num_bytes: 390411946
num_examples: 801402
- name: validation
num_bytes: 54167280
num_examples: 100173
- name: test
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num_examples: 100177
download_size: 138415559
dataset_size: 498362016
- config_name: then
features:
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dtype:
translation:
languages:
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- name: subdataset
dtype: string
splits:
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num_bytes: 390411946
num_examples: 801402
- name: validation
num_bytes: 54167280
num_examples: 100173
- name: test
num_bytes: 53782790
num_examples: 100177
download_size: 138415559
dataset_size: 498362016
---
# Dataset Card for `scb_mt_enth_2020`
## 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://airesearch.in.th/
- **Repository:** https://github.com/vistec-AI/thai2nmt
- **Paper:** https://arxiv.org/abs/2007.03541
- **Leaderboard:**
- **Point of Contact:** https://airesearch.in.th/
### Dataset Summary
scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
The primary objective of our work is to build a large-scale English-Thai dataset for machine translation.
We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources,
namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents.
Methodology for gathering data, building parallel texts and removing noisy sentence pairs are presented in a reproducible manner.
We train machine translation models based on this dataset. Our models' performance are comparable to that of
Google Translation API (as of May 2020) for Thai-English and outperform Google when the Open Parallel Corpus (OPUS) is
included in the training data for both Thai-English and English-Thai translation.
The dataset, pre-trained models, and source code to reproduce our work are available for public use.
### Supported Tasks and Leaderboards
machine translation
### Languages
English, Thai
## Dataset Structure
### Data Instances
```
{'subdataset': 'aqdf', 'translation': {'en': 'FAR LEFT: Indonesian National Police Chief Tito Karnavian, from left, Philippine National Police Chief Ronald Dela Rosa and Royal Malaysian Police Inspector General Khalid Abu Bakar link arms before the Trilateral Security Meeting in Pasay city, southeast of Manila, Philippines, in June 2017. [THE ASSOCIATED PRESS]', 'th': '(ซ้ายสุด) นายติโต คาร์นาเวียน ผู้บัญชาการตํารวจแห่งชาติอินโดนีเซีย (จากซ้าย) นายโรนัลด์ เดลา โรซา ผู้บัญชาการตํารวจแห่งชาติฟิลิปปินส์ และนายคาลิด อาบู บาการ์ ผู้บัญชาการตํารวจแห่งชาติมาเลเซีย ไขว้แขนกันก่อนเริ่มการประชุมความมั่นคงไตรภาคีในเมืองปาเซย์ ซึ่งอยู่ทางตะวันออกเฉียงใต้ของกรุงมะนิลา ประเทศฟิลิปปินส์ ในเดือนมิถุนายน พ.ศ. 2560 ดิแอสโซซิเอทเต็ด เพรส'}}
{'subdataset': 'thai_websites', 'translation': {'en': "*Applicants from certain countries may be required to pay a visa issuance fee after their application is approved. The Department of State's website has more information about visa issuance fees and can help you determine if an issuance fee applies to your nationality.", 'th': 'ประเภทวีซ่า รวมถึงค่าธรรมเนียม และข้อกําหนดในการสัมภาษณ์วีซ่า จะขึ้นอยู่กับชนิดของหนังสือเดินทาง และจุดประสงค์ในการเดินทางของท่าน โปรดดูตารางด้านล่างก่อนการสมัครวีซ่า'}}
{'subdataset': 'nus_sms', 'translation': {'en': 'Yup... Okay. Cya tmr... So long nvr write already... Dunno whether tmr can come up with 500 words', 'th': 'ใช่...ได้ แล้วเจอกันพรุ่งนี้... นานแล้วไม่เคยเขียน... ไม่รู้ว่าพรุ่งนี้จะทําได้ถึง500คําไหมเลย'}}
```
### Data Fields
- `subdataset`: subdataset from which the sentence pair comes from
- `translation`:
- `en`: English sentences (original source)
- `th`: Thai sentences (originally target for translation)
### Data Splits
```
Split ratio (train, valid, test) : (0.8, 0.1, 0.1)
Number of paris (train, valid, test): 801,402 | 100,173 | 100,177
# Train
generated_reviews_yn: 218,637 ( 27.28% )
task_master_1: 185,671 ( 23.17% )
generated_reviews_translator: 105,561 ( 13.17% )
thai_websites: 93,518 ( 11.67% )
paracrawl: 46,802 ( 5.84% )
nus_sms: 34,495 ( 4.30% )
mozilla_common_voice: 2,451 ( 4.05% )
wikipedia: 26,163 ( 3.26% cd)
generated_reviews_crowd: 19,769 ( 2.47% )
assorted_government: 19,712 ( 2.46% )
aqdf: 10,466 ( 1.31% )
msr_paraphrase: 8,157 ( 1.02% )
# Valid
generated_reviews_yn: 30,786 ( 30.73% )
task_master_1: 18,531 ( 18.50% )
generated_reviews_translator: 13,884 ( 13.86% )
thai_websites: 13,381 ( 13.36% )
paracrawl: 6,618 ( 6.61% )
nus_sms: 4,628 ( 4.62% )
wikipedia: 3,796 ( 3.79% )
assorted_government: 2,842 ( 2.83% )
generated_reviews_crowd: 2,409 ( 2.40% )
aqdf: 1,518 ( 1.52% )
msr_paraphrase: 1,107 ( 1.11% )
mozilla_common_voice: 673 ( 0.67% )
# Test
generated_reviews_yn: 30,785 ( 30.73% )
task_master_1: 18,531 ( 18.50% )
generated_reviews_translator: 13,885 ( 13.86% )
thai_websites: 13,381 ( 13.36% )
paracrawl: 6,619 ( 6.61% )
nus_sms: 4,627 ( 4.62% )
wikipedia: 3,797 ( 3.79% )
assorted_government: 2,844 ( 2.83% )
generated_reviews_crowd: 2,409 ( 2.40% )
aqdf: 1,519 ( 1.52% )
msr_paraphrase: 1,107 ( 1.11% )
mozilla_common_voice : 673 ( 0.67% )
```
## Dataset Creation
### Curation Rationale
[AIResearch](https://airesearch.in.th/), funded by [VISTEC](https://www.vistec.ac.th/) and [depa](https://www.depa.or.th/th/home), curated this dataset as part of public NLP infrastructure. The center releases the dataset and baseline models under CC-BY-SA 4.0.
### Source Data
#### Initial Data Collection and Normalization
The sentence pairs are curated from news, Wikipedia articles, SMS messages, task-based dialogs, webcrawled data and government documents. Sentence pairs are generated by:
- Professional translators
- Crowdsourced translators
- Google Translate API and human annotators (accepted or rejected)
- Sentence alignment with [multilingual universal sentence encoder](https://tfhub.dev/google/universal-sentence-encoder-multilingual/3); the author created [CRFCut](https://github.com/vistec-AI/crfcut) to segment Thai sentences to be abel to align with their English counterparts (sentence segmented by [NLTK](https://www.nltk.org/))
For detailed explanation of dataset curation, see https://arxiv.org/pdf/2007.03541.pdf
### Annotations
#### Sources and Annotation process
- generated_reviews_yn: generated by [CTRL](https://arxiv.org/abs/1909.05858), translated to Thai by Google Translate API and annotated as accepted or rejected by human annotators (we do not include rejected sentence pairs)
- task_master_1: [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/) translated by professional translators hired by [AIResearch](https://airesearch.in.th/)
- generated_reviews_translator: professional translators hired by [AIResearch](https://airesearch.in.th/)
- thai_websites: webcrawling from top 500 websites in Thailand; respective content creators; the authors only did sentence alignment
- paracrawl: replicating Paracrawl's methodology for webcrawling; respective content creators; the authors only did sentence alignment
- nus_sms: [The National University of Singapore SMS Corpus](https://scholarbank.nus.edu.sg/handle/10635/137343) translated by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
- wikipedia: Thai Wikipedia; respective content creators; the authors only did sentence alignment
- assorted_government: Government document in PDFs from various government websites; respective content creators; the authors only did sentence alignment
- generated_reviews_crowd: generated by [CTRL](https://arxiv.org/abs/1909.05858), translated to Thai by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
- aqdf: Bilingual news from [Asia Pacific Defense Forum](https://ipdefenseforum.com/); respective content creators; the authors only did sentence alignment
- msr_paraphrase: [Microsoft Research Paraphrase Corpus](https://www.microsoft.com/en-us/download/details.aspx?id=52398) translated to Thai by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
- mozilla_common_voice: English version of [Mozilla Common Voice](https://commonvoice.mozilla.org/) translated to Thai by crowdsourced translators hired by [AIResearch](https://airesearch.in.th/)
### Personal and Sensitive Information
There are risks of personal information to be included in the webcrawled data namely `paracrawl` and `thai_websites`.
## Considerations for Using the Data
### Social Impact of Dataset
- The first and currently largest English-Thai machine translation dataset that is strictly cleaned and deduplicated, compare to other sources such as Paracrawl.
### Discussion of Biases
- Gender-based ending honorifics in Thai (ครับ/ค่ะ) might not be balanced due to more female translators than male for `task_master_1`
### Other Known Limitations
#### Segment Alignment between Languages With and Without Boundaries
Unlike English, there is no segment boundary marking in Thai. One segment in Thai may or may not cover all
the content of an English segment. Currently, we mitigate this problem by grouping Thai segments together before
computing the text similarity scores. We then choose the combination with the highest text similarity score. It can be
said that adequacy is the main issue in building this dataset.
Quality of Translation from Crawled Websites
Some websites use machine translation models such as Google Translate to localize their content. As a result, Thai
segments retrieved from web crawling might face issues of fluency since we do not use human annotators to perform
quality control.
#### Quality Control of Crowdsourced Translators
When we use a crowdsourcing platform to translate the content, we can not fully control the quality of the translation.
To combat this, we filter out low-quality segments by using a text similarity threshold, based on cosine similarity of
universal sentence encoder vectors. Moreover, some crowdsourced translators might copy and paste source segments to
a translation engine and take the results as answers to the platform. To further improve, we can apply techniques such
as described in [Zaidan, 2012] to control the quality and avoid fraud on the platform.
#### Domain Dependence of Machine Tranlsation Models
We test domain dependence of machine translation models by comparing models trained and tested on the same dataset,
using 80/10/10 train-validation-test split, and models trained on one dataset and tested on the other.
## Additional Information
### Dataset Curators
[AIResearch](https://airesearch.in.th/), funded by [VISTEC](https://www.vistec.ac.th/) and [depa](https://www.depa.or.th/th/home)
### Licensing Information
CC-BY-SA 4.0
### Citation Information
```
@article{lowphansirikul2020scb,
title={scb-mt-en-th-2020: A Large English-Thai Parallel Corpus},
author={Lowphansirikul, Lalita and Polpanumas, Charin and Rutherford, Attapol T and Nutanong, Sarana},
journal={arXiv preprint arXiv:2007.03541},
year={2020}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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snow_simplified_japanese_corpus | null | 2022-11-03T16:31:17Z | 572 | 15 | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:other",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:ja",
"license:cc-by-4.0",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
- other
language_creators:
- found
language:
- en
- ja
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: SNOW T15 and T23 (simplified Japanese corpus)
dataset_info:
- config_name: snow_t15
features:
- name: ID
dtype: string
- name: original_ja
dtype: string
- name: simplified_ja
dtype: string
- name: original_en
dtype: string
splits:
- name: train
num_bytes: 7218115
num_examples: 50000
download_size: 3634132
dataset_size: 7218115
- config_name: snow_t23
features:
- name: ID
dtype: string
- name: original_ja
dtype: string
- name: simplified_ja
dtype: string
- name: original_en
dtype: string
- name: proper_noun
dtype: string
splits:
- name: train
num_bytes: 6704695
num_examples: 34300
download_size: 3641507
dataset_size: 6704695
---
# Dataset Card for SNOW T15 and T23 (simplified Japanese 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:** [SNOW T15](http://www.jnlp.org/SNOW/T15), [SNOW T23](http://www.jnlp.org/SNOW/T23)
- **Repository:** [N/A]
- **Paper:** ["Simplified Corpus with Core Vocabulary"](https://www.aclweb.org/anthology/L18-1185), ["やさしい⽇本語対訳コーパスの構築"](https://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/B5-1.pdf), ["Crowdsourced Corpus of Sentence Simplification with Core Vocabulary"](https://www.aclweb.org/anthology/L18-1072)
- **Leaderboard:** [N/A]
- **Point of Contact:** Check the homepage.
### Dataset Summary
- **SNOW T15:**
The simplified corpus for the Japanese language. The corpus has 50,000 manually simplified and aligned sentences.
This corpus contains the original sentences, simplified sentences and English translation of the original sentences.
It can be used for automatic text simplification as well as translating simple Japanese into English and vice-versa.
The core vocabulary is restricted to 2,000 words where it is selected by accounting for several factors such as meaning preservation, variation, simplicity and the UniDic word segmentation criterion.
For details, refer to the explanation page of Japanese simplification (http://www.jnlp.org/research/Japanese_simplification).
The original texts are from "small_parallel_enja: 50k En/Ja Parallel Corpus for Testing SMT Methods", which is a bilingual corpus for machine translation.
- **SNOW T23:**
An expansion corpus of 35,000 sentences rewritten in easy Japanese (simple Japanese vocabulary) based on SNOW T15.
The original texts are from "Tanaka Corpus" (http://www.edrdg.org/wiki/index.php/Tanaka_Corpus).
### Supported Tasks and Leaderboards
It can be used for automatic text simplification in Japanese as well as translating simple Japanese into English and vice-versa.
### Languages
Japanese, simplified Japanese, and English.
## Dataset Structure
### Data Instances
SNOW T15 is xlsx file with ID, "#日本語(原文)" (Japanese (original)), "#やさしい日本語" (simplified Japanese), "#英語(原文)" (English (original)).
SNOW T23 is xlsx file with ID, "#日本語(原文)" (Japanese (original)), "#やさしい日本語" (simplified Japanese), "#英語(原文)" (English (original)), and "#固有名詞" (proper noun).
### Data Fields
- `ID`: sentence ID.
- `original_ja`: original Japanese sentence.
- `simplified_ja`: simplified Japanese sentence.
- `original_en`: original English sentence.
- `proper_noun`: (included only in SNOW T23) Proper nowus that the workers has extracted as proper nouns. The authors instructed workers not to rewrite proper nouns, leaving the determination of proper nouns to the workers.
### Data Splits
The data is not split.
## Dataset Creation
### Curation Rationale
A dataset on the study of automatic conversion to simplified Japanese (Japanese simplification).
### Source Data
#### Initial Data Collection and Normalization
- **SNOW T15:**
The original texts are from "small_parallel_enja: 50k En/Ja Parallel Corpus for Testing SMT Methods", which is a bilingual corpus for machine translation.
- **SNOW T23:**
The original texts are from "Tanaka Corpus" (http://www.edrdg.org/wiki/index.php/Tanaka_Corpus).
#### Who are the source language producers?
[N/A]
### Annotations
#### Annotation process
- **SNOW T15:**
Five students in the laboratory rewrote the original Japanese sentences to simplified Japanese all by hand.
The core vocabulary is restricted to 2,000 words where it is selected by accounting for several factors such as meaning preservation, variation, simplicity and the UniDic word segmentation criterion.
- **SNOW T23:**
Seven people, gathered through crowdsourcing, rewrote all the sentences manually.
Each worker rewrote 5,000 sentences, of which 100 sentences were rewritten to be common among the workers.
The average length of the sentences was kept as close to the same as possible so that the amount of work was not varied among the workers.
#### Who are the annotators?
Five students for SNOW T15, seven crowd workers for SNOW T23.
### 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
The datasets are part of SNOW, Japanese language resources/tools created by Natural Language Processing Laboratory, Nagaoka University of Technology, Japan.
### Licensing Information
CC BY 4.0
### Citation Information
```
@inproceedings{maruyama-yamamoto-2018-simplified,
title = "Simplified Corpus with Core Vocabulary",
author = "Maruyama, Takumi and
Yamamoto, Kazuhide",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://www.aclweb.org/anthology/L18-1185",
}
@inproceedings{yamamoto-2017-simplified-japanese,
title = "やさしい⽇本語対訳コーパスの構築",
author = "⼭本 和英 and
丸⼭ 拓海 and
⾓張 ⻯晴 and
稲岡 夢⼈ and
⼩川 耀⼀朗 and
勝⽥ 哲弘 and
髙橋 寛治",
booktitle = "言語処理学会第23回年次大会",
month = 3月,
year = "2017",
address = "茨城, 日本",
publisher = "言語処理学会",
url = "https://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/B5-1.pdf",
}
@inproceedings{katsuta-yamamoto-2018-crowdsourced,
title = "Crowdsourced Corpus of Sentence Simplification with Core Vocabulary",
author = "Katsuta, Akihiro and
Yamamoto, Kazuhide",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://www.aclweb.org/anthology/L18-1072",
}
```
### Contributions
Thanks to [@forest1988](https://github.com/forest1988), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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pasinit/xlwic | pasinit | 2022-10-25T09:54:22Z | 572 | 4 | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:bg",
"language:zh",
"language:hr",
"language:da",
"language:nl",
"language:et",
"language:fa",
"language:ja",
"language:ko",
"language:it",
"language:fr",
"language:de",
"license:cc-by-nc-4.0",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
extended:
- original
language_creators:
- found
language:
- en
- bg
- zh
- hr
- da
- nl
- et
- fa
- ja
- ko
- it
- fr
- de
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
---
# XL-WiC
Huggingface dataset for the XL-WiC paper [https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf](https://www.aclweb.org/anthology/2020.emnlp-main.584.pdf).
Please refer to the official [website](https://pilehvar.github.io/xlwic/) for more information.
## Configurations
When loading one of the XL-WSD datasets one has to specify the training language and the target language (on which dev and test will be performed).
Please refer to [Languages](#languages) section to see in which languages training data is available.
For example, we can load the dataset having English as training language and Italian as target language as follows:
```python
from datasets import load_dataset
dataset = load_dataset('pasinit/xlwic', 'en_it')
```
## Languages
**Training data**
- en (English)
- fr (French)
- de (German)
- it (Italian)
**Dev & Test data**
- fr (French)
- de (German)
- it (Italian)
- bg (Bulgarian)
- zh (Chinese)
- hr (Croatian)
- da (Danish)
- nl (Dutch)
- et (Estonian)
- fa (Farsi)
- ja (Japanesse)
- ko (Korean)
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meta_woz | null | 2022-11-18T21:28:56Z | 571 | 3 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2003.01680",
"region:us"
] | [
"text-generation",
"fill-mask"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
license_details: Microsoft Research Data License Agreement
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: metalwoz
pretty_name: Meta-Learning Wizard-of-Oz
dataset_info:
- config_name: dialogues
features:
- name: id
dtype: string
- name: user_id
dtype: string
- name: bot_id
dtype: string
- name: domain
dtype: string
- name: task_id
dtype: string
- name: turns
sequence: string
splits:
- name: train
num_bytes: 19999218
num_examples: 37884
- name: test
num_bytes: 1284287
num_examples: 2319
download_size: 8629863
dataset_size: 21283505
- config_name: tasks
features:
- name: task_id
dtype: string
- name: domain
dtype: string
- name: bot_prompt
dtype: string
- name: bot_role
dtype: string
- name: user_prompt
dtype: string
- name: user_role
dtype: string
splits:
- name: train
num_bytes: 73768
num_examples: 227
- name: test
num_bytes: 4351
num_examples: 14
download_size: 8629863
dataset_size: 78119
---
# Dataset Card for MetaLWOz
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [MetaLWOz Project Website](https://www.microsoft.com/en-us/research/project/metalwoz/)
- **Paper:** [Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer](https://ieeexplore.ieee.org/abstract/document/9053599), and [Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation](https://arxiv.org/pdf/2003.01680.pdf)
- **Point of Contact:** [Hannes Schulz](https://www.microsoft.com/en-us/research/people/haschulz/)
### Dataset Summary
MetaLWOz: A Dataset of Multi-Domain Dialogues for the Fast Adaptation of Conversation Models.
We introduce the Meta-Learning Wizard of Oz (MetaLWOz) dialogue dataset for developing fast adaptation methods for
conversation models. This data can be used to train task-oriented dialogue models, specifically to develop methods to
quickly simulate user responses with a small amount of data. Such fast-adaptation models fall into the research areas
of transfer learning and meta learning. The dataset consists of 37,884 crowdsourced dialogues recorded between two
human users in a Wizard of Oz setup, in which one was instructed to behave like a bot, and the other a true human
user. The users are assigned a task belonging to a particular domain, for example booking a reservation at a
particular restaurant, and work together to complete the task. Our dataset spans 47 domains having 227 tasks total.
Dialogues are a minimum of 10 turns long.
### Supported Tasks and Leaderboards
This dataset supports a range of task.
- **Generative dialogue modeling** or `dialogue-modeling`: This data can be used to train task-oriented dialogue
models, specifically to develop methods to quickly simulate user responses with a small amount of data. Such fast
-adaptation models fall into the research areas of transfer learning and meta learning. The text of the dialogues
can be used to train a sequence model on the utterances.
Example of sample input/output is given in section [Data Instances](#data-instances)
### Languages
The text in the dataset is in English (`en`).
## Dataset Structure
### Data Instances
A data instance is a full multi-turn dialogue between two crowd-workers, one had the role of being a `bot`, and the other one was the `user`. Both were
given a `domain` and a `task`. Each turn has a single utterance, e.g.:
```
Domain: Ski
User Task: You want to know if there are good ski hills an
hour’s drive from your current location.
Bot Task: Tell the user that there are no ski hills in their
immediate location.
Bot: Hello how may I help you?
User: Is there any good ski hills an hour’s drive from my
current location?
Bot: I’m sorry to inform you that there are no ski hills in your
immediate location
User: Can you help me find the nearest?
Bot: Absolutely! It looks like you’re about 3 hours away from
Bear Mountain. That seems to be the closest.
User: Hmm.. sounds good
Bot: Alright! I can help you get your lift tickets now!When
will you be going?
User: Awesome! please get me a ticket for 10pax
Bot: You’ve got it. Anything else I can help you with?
User: None. Thanks again!
Bot: No problem!
```
Example of input/output for this dialog:
```
Input: dialog history = Hello how may I help you?; Is there
any good ski hills an hour’s drive from my current location?;
I’m sorry to inform you that there are no ski hills in your
immediate location
Output: user response = Can you help me find the nearest?
```
### Data Fields
Each dialogue instance has the following fields:
- `id`: a unique ID identifying the dialog.
- `user_id`: a unique ID identifying the user.
- `bot_id`: a unique ID identifying the bot.
- `domain`: a unique ID identifying the domain. Provides a mapping to tasks dataset.
- `task_id`: a unique ID identifying the task. Provides a mapping to tasks dataset.
- `turns`: the sequence of utterances alternating between `bot` and `user`, starting with a prompt from `bot`.
Each task instance has following fields:
- `task_id`: a unique ID identifying the task.
- `domain`: a unique ID identifying the domain.
- `bot_prompt`: The task specification for bot.
- `bot_role`: The domain oriented role of bot.
- `user_prompt`: The task specification for user.
- `user_role`: The domain oriented role of user.
### Data Splits
The dataset is split into a `train` and `test` split with the following sizes:
| | Training MetaLWOz | Evaluation MetaLWOz | Combined |
| ----- | ------ | ----- | ---- |
| Total Domains | 47 | 4 | 51 |
| Total Tasks | 226 | 14 | 240 |
| Total Dialogs | 37884 | 2319 | 40203 |
Below are the various statistics of the dataset:
| Statistic | Mean | Minimum | Maximum |
| ----- | ------ | ----- | ---- |
| Number of tasks per domain | 4.8 | 3 | 11 |
| Number of dialogs per domain | 806.0 | 288 | 1990 |
| Number of dialogs per task | 167.6 | 32 | 285 |
| Number of turns per dialog | 11.4 | 10 | 46 |
## 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
The dataset v1 version is created by team of researchers from Microsoft Research (Montreal, Canada)
### Licensing Information
The dataset is released under [Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view)
### Citation Information
You can cite the following for the various versions of MetaLWOz:
Version 1.0
```
@InProceedings{shalyminov2020fast,
author = {Shalyminov, Igor and Sordoni, Alessandro and Atkinson, Adam and Schulz, Hannes},
title = {Fast Domain Adaptation For Goal-Oriented Dialogue Using A Hybrid Generative-Retrieval Transformer},
booktitle = {2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2020},
month = {April},
url = {https://www.microsoft.com/en-us/research/publication/fast-domain-adaptation-for-goal-oriented-dialogue-using-a
-hybrid-generative-retrieval-transformer/},
}
```
### Contributions
Thanks to [@pacman100](https://github.com/pacman100) for adding this dataset. | [
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tlc | null | 2022-11-03T16:31:06Z | 570 | 0 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:th",
"license:unknown",
"region:us"
] | [
"text-generation",
"fill-mask"
] | 2022-03-02T23:29:22Z | ---
pretty_name: Thai Literature Corpora (TLC)
annotations_creators:
- expert-generated
- no-annotation
language_creators:
- expert-generated
language:
- th
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
dataset_info:
- config_name: tlcv1.0
features:
- name: ch_num
dtype: string
- name: title
dtype: string
- name: text
sequence:
sequence: string
splits:
- name: train
num_bytes: 32498
num_examples: 1
download_size: 2904472
dataset_size: 32498
- config_name: tlcv2.0
features:
- name: ch_num
dtype: string
- name: title
dtype: string
- name: text
sequence:
sequence: string
splits:
- name: train
num_bytes: 32498
num_examples: 1
download_size: 5551710
dataset_size: 32498
- config_name: tnhcv1.0
features:
- name: text
sequence: string
splits:
- name: train
num_bytes: 25198
num_examples: 152
download_size: 1465403
dataset_size: 25198
---
# Dataset Card for Thai Literature Corpora (TLC)
## 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://attapol.github.io/tlc.html
- **Leaderboard:** https://www.kaggle.com/c/wisesight-sentiment/
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Jitkapat Sawatphol, Attapol Rutherford; attapolrutherford at gmail.com
### Dataset Summary
Thai Literature Corpora (TLC): Corpora of machine-ingestible Thai classical literature texts.
It consists of two datasets:
## TLC set
It is texts from [Vajirayana Digital Library](https://vajirayana.org/), stored by chapters and stanzas (non-tokenized).
tlc v.2.0 (6/17/19 : a total of 34 documents, 292,270 lines, 31,790,734 characters)
tlc v.1.0 (6/11/19 : a total of 25 documents, 113,981 lines, 28,775,761 characters)
## TNHC set
It is texts from Thai National Historical Corpus, stored by lines (manually tokenized).
tnhc v.1.0 (6/25/19 : a total of 47 documents, 756,478 lines, 13,361,142 characters)
### Supported Tasks and Leaderboards
Language Modeling, Language Generation
### Languages
Thai
## Dataset Structure
### Data Instances
```
{
"ch_num": "๑",
"title": "กากี กลอนสุภาพ",
"text": [
[
"๏ จักกล่าวอดีตนิทานแต่ปางก่อน\n",
"เมื่อครั้งองค์สมเด็จพระชินวร\tยังสัญจรแสวงหาโพธิญาณ\n",
"เสวยชาติเป็นสกุณาพระยานก\tจึงชักเรื่องชาดกมาบรรหาร\n",
"หวังแสดงแห่งจิตหญิงพาล\tให้ชายชาญรู้เชิงกระสัตรี ฯ\n"
]
}
```
### Data Fields
- `ch_num`: chapter number in Thai Numerals (๑, ๒, ๓, ๔, ๕, ๖, ๗, ๘, ๙, ๑๐, ...)
- `title`: chapter name
- `text`: each item corresponds to one stanzas, each line is a couplet which can be seperated by `\t`
### Data Splits
tlc v.2.0 (6/17/19 : a total of 34 documents, 292,270 lines, 31,790,734 characters)
tlc v.1.0 (6/11/19 : a total of 25 documents, 113,981 lines, 28,775,761 characters)
## TNHC set
It is texts from Thai National Historical Corpus, stored by lines (manually tokenized).
tnhc v.1.0 (6/25/19 : a total of 47 documents, 756,478 lines, 13,361,142 characters)
| | tlc2.0 | tlc1.0 | tnhc |
|-----------|-------|-------|-------|
| # documents | 34 | 25 | 47 |
| # lines | 292,270 | 113,981 | 756,478 |
## Dataset Creation
### Curation Rationale
Originally, the dataset was compiled for the [Thai Poetry Generator](https://github.com/jitkapat/thaipoetrygenerator) at Chulalongkorn university as the Final project for `2209372 Introduction to Computational Linguistics` by [Jitkapat Sawatphol](https://jitkapat.github.io/) (Faculty of Engineering, Chulalongkorn University).
### 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
There is no personal information.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Thanks [Jitkapat Sawatphol](https://jitkapat.github.io/) (Faculty of Arts, Chulalongkorn University), and [Attapol Rutherford](https://attapol.github.io/) (Faculty of Arts, Chulalongkorn University)
### Licensing Information
[More Information Needed]
### Citation Information
Please cite the following if you make use of the dataset:
Jitkapat Sawatphol, and Attapol Rutherford. 2019. **Thai Literature Corpora (TLC)**.
BibTeX:
```
@misc{
author={Sawatphol, Jitkapat},
title={Thai Literature Corpora},
year={2019},
howpublished={\\url{https://attapol.github.io/tlc.html}}
}
```
### Contributions
Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset. | [
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open-llm-leaderboard/details_tiiuae__falcon-180B | open-llm-leaderboard | 2023-10-24T10:18:04Z | 570 | 1 | [
"region:us"
] | null | 2023-09-05T08:24:35Z | ---
pretty_name: Evaluation run of tiiuae/falcon-180B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-180B](https://huggingface.co/tiiuae/falcon-180B) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 66 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 32 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_tiiuae__falcon-180B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T10:17:51.759984](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-180B/blob/main/results_2023-10-24T10-17-51.759984.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\": 0.0028313758389261743,\n\
\ \"em_stderr\": 0.0005441551135493806,\n \"f1\": 0.06573301174496615,\n\
\ \"f1_stderr\": 0.0013666874377791776,\n \"acc\": 0.6642104078991223,\n\
\ \"acc_stderr\": 0.011605139145295384\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0028313758389261743,\n \"em_stderr\": 0.0005441551135493806,\n\
\ \"f1\": 0.06573301174496615,\n \"f1_stderr\": 0.0013666874377791776\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.45943896891584535,\n \
\ \"acc_stderr\": 0.01372709301042978\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8689818468823993,\n \"acc_stderr\": 0.009483185280160986\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-180B
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_30T14_31_39.488381
path:
- '**/details_harness|arc:challenge|25_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|arc:challenge|25_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|arc:challenge|25_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|arc:challenge|25_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|arc:challenge|25_2023-09-01T15:12:02.263774.parquet'
- split: 2023_09_25T09_30_46.601936
path:
- '**/details_harness|arc:challenge|25_2023-09-25T09-30-46.601936.parquet'
- split: 2023_09_25T09_42_43.006060
path:
- '**/details_harness|arc:challenge|25_2023-09-25T09-42-43.006060.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-25T09-42-43.006060.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T17_29_05.444286
path:
- '**/details_harness|drop|3_2023-10-23T17-29-05.444286.parquet'
- split: 2023_10_24T10_17_51.759984
path:
- '**/details_harness|drop|3_2023-10-24T10-17-51.759984.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T10-17-51.759984.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T17_29_05.444286
path:
- '**/details_harness|gsm8k|5_2023-10-23T17-29-05.444286.parquet'
- split: 2023_10_24T10_17_51.759984
path:
- '**/details_harness|gsm8k|5_2023-10-24T10-17-51.759984.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T10-17-51.759984.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hellaswag|10_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hellaswag|10_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hellaswag|10_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hellaswag|10_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hellaswag|10_2023-09-01T15:12:02.263774.parquet'
- split: 2023_09_25T11_16_10.146827
path:
- '**/details_harness|hellaswag|10_2023-09-25T11-16-10.146827.parquet'
- split: 2023_09_25T11_28_53.879118
path:
- '**/details_harness|hellaswag|10_2023-09-25T11-28-53.879118.parquet'
- split: 2023_09_25T13_20_00.898508
path:
- '**/details_harness|hellaswag|10_2023-09-25T13-20-00.898508.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-25T13-20-00.898508.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T14:31:39.488381.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T14:31:39.488381.parquet'
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path:
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path:
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T12:44:38.148712.parquet'
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- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T12:44:38.148712.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T12:44:38.148712.parquet'
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- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T12:44:38.148712.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T12:44:38.148712.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T15:12:02.263774.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T15:12:02.263774.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-01T15:12:02.263774.parquet'
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- split: latest
path:
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- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
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:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T12:44:38.148712.parquet'
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T01:32:36.577851.parquet'
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T12:44:38.148712.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T01:32:36.577851.parquet'
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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path:
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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_econometrics_5
data_files:
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path:
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- split: 2023_08_30T19_27_57.090829
path:
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path:
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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_electrical_engineering_5
data_files:
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path:
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- split: 2023_08_30T19_27_57.090829
path:
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path:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T14:31:39.488381.parquet'
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path:
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path:
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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_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T01:32:36.577851.parquet'
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T01:32:36.577851.parquet'
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T12:44:38.148712.parquet'
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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:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T14:31:39.488381.parquet'
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T12:44:38.148712.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T14:31:39.488381.parquet'
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path:
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path:
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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_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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path:
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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_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T01:32:36.577851.parquet'
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
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- split: 2023_08_31T01_32_36.577851
path:
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- split: 2023_08_31T12_44_38.148712
path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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- split: 2023_08_30T19_27_57.090829
path:
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- split: 2023_08_31T01_32_36.577851
path:
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- split: 2023_08_31T12_44_38.148712
path:
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- split: 2023_09_01T15_12_02.263774
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: 2023_08_30T19_27_57.090829
path:
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path:
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- split: 2023_08_31T12_44_38.148712
path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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- split: 2023_08_30T19_27_57.090829
path:
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path:
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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_physics_5
data_files:
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path:
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- split: 2023_08_30T19_27_57.090829
path:
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path:
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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_psychology_5
data_files:
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path:
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path:
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path:
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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_statistics_5
data_files:
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path:
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path:
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path:
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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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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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path:
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path:
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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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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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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:
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path:
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path:
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path:
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- 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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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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path:
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path:
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path:
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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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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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path:
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path:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
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- split: 2023_09_01T15_12_02.263774
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-30T19:27:57.090829.parquet'
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_30T14_31_39.488381
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T15:12:02.263774.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-01T15:12:02.263774.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T14:31:39.488381.parquet'
- split: 2023_08_30T19_27_57.090829
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-30T19:27:57.090829.parquet'
- split: 2023_08_31T01_32_36.577851
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-31T01:32:36.577851.parquet'
- split: 2023_08_31T12_44_38.148712
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-31T12:44:38.148712.parquet'
- split: 2023_09_01T15_12_02.263774
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-01T15:12:02.263774.parquet'
- split: 2023_09_25T09_49_01.514206
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-25T09-49-01.514206.parquet'
- split: 2023_09_25T09_57_43.547983
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-25T09-57-43.547983.parquet'
- split: 2023_09_25T10_06_12.822356
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-25T10-06-12.822356.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-25T10-06-12.822356.parquet'
- config_name: harness_winogrande_5
data_files:
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path:
- '**/details_harness|winogrande|5_2023-10-23T17-29-05.444286.parquet'
- split: 2023_10_24T10_17_51.759984
path:
- '**/details_harness|winogrande|5_2023-10-24T10-17-51.759984.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T10-17-51.759984.parquet'
- config_name: original_mmlu_5
data_files:
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path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-21T14-54-28.631498.parquet'
- split: 2023_09_21T15_14_19.361952
path:
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- split: 2023_09_22T15_08_20.868776
path:
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- split: 2023_09_22T15_09_58.434868
path:
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- split: 2023_09_22T15_40_03.532661
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T15-40-03.532661.parquet'
- split: 2023_09_22T19_13_36.680152
path:
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- split: 2023_09_22T19_25_51.687929
path:
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- split: 2023_09_22T19_38_30.055713
path:
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- split: 2023_09_22T19_56_14.188877
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T19-56-14.188877.parquet'
- split: 2023_09_22T20_44_00.745184
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T20-44-00.745184.parquet'
- split: 2023_09_22T21_16_36.510313
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T21-16-36.510313.parquet'
- split: 2023_09_22T21_30_38.663736
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T21-30-38.663736.parquet'
- split: 2023_09_22T21_39_07.387549
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T21-39-07.387549.parquet'
- split: 2023_09_22T21_46_48.392874
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T21-46-48.392874.parquet'
- split: 2023_09_22T22_06_13.624503
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T22-06-13.624503.parquet'
- split: 2023_09_22T22_21_06.865348
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T22-21-06.865348.parquet'
- split: 2023_09_23T09_44_24.946036
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-23T09-44-24.946036.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-23T09-44-24.946036.parquet'
- config_name: original_mmlu_high_school_government_and_politics_5
data_files:
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path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-21T14-54-28.631498.parquet'
- split: 2023_09_21T15_14_19.361952
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-21T15-14-19.361952.parquet'
- split: 2023_09_22T15_08_20.868776
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T15-08-20.868776.parquet'
- split: 2023_09_22T15_09_58.434868
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-09-22T15-09-58.434868.parquet'
- split: 2023_09_22T15_40_03.532661
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---
# Dataset Card for Evaluation run of tiiuae/falcon-180B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-180B
- **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 [tiiuae/falcon-180B](https://huggingface.co/tiiuae/falcon-180B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 66 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 32 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_tiiuae__falcon-180B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T10:17:51.759984](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-180B/blob/main/results_2023-10-24T10-17-51.759984.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": 0.0028313758389261743,
"em_stderr": 0.0005441551135493806,
"f1": 0.06573301174496615,
"f1_stderr": 0.0013666874377791776,
"acc": 0.6642104078991223,
"acc_stderr": 0.011605139145295384
},
"harness|drop|3": {
"em": 0.0028313758389261743,
"em_stderr": 0.0005441551135493806,
"f1": 0.06573301174496615,
"f1_stderr": 0.0013666874377791776
},
"harness|gsm8k|5": {
"acc": 0.45943896891584535,
"acc_stderr": 0.01372709301042978
},
"harness|winogrande|5": {
"acc": 0.8689818468823993,
"acc_stderr": 0.009483185280160986
}
}
```
### 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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codeparrot/self-instruct-starcoder | codeparrot | 2023-10-23T12:13:18Z | 569 | 38 | [
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:bigscience-openrail-m",
"code",
"arxiv:2212.10560",
"arxiv:2305.06161",
"arxiv:1908.10084",
"doi:10.57967/hf/0790",
"region:us"
] | [
"text2text-generation"
] | 2023-05-22T14:50:58Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: most_similar
dtype: string
- name: avg_similarity_score
dtype: float64
splits:
- name: curated
num_bytes: 1937514
num_examples: 771
- name: raw
num_bytes: 12969008
num_examples: 5003
- name: unique
num_bytes: 786771
num_examples: 308
- name: compile
num_bytes: 9048805
num_examples: 3549
download_size: 10935008
dataset_size: 24742098
tags:
- code
size_categories:
- 1K<n<10K
task_categories:
- text2text-generation
license: bigscience-openrail-m
language:
- en
---
# Self-instruct-starcoder
## Table of Contents
- [Summary](#summary)
- [Our approach](#our-approach)
- [Dataset generation](#dataset-generation)
- [Dataset quality](#dataset-quality)
- [Post-processing](#post-processing)
- [Self-consistency](#self-consistency)
- [Uniqueness](#uniqueness)
- [Compile](#compile)
- [Dataset structure](#dataset-structure)
- [Space](#space)
## Summary
Self-instruct-starcoder is a dataset that was generated by prompting starcoder to generate new instructions based on some human-written seed instructions.
The underlying process is explained in the paper [self-instruct](https://arxiv.org/abs/2212.10560). This algorithm gave birth to famous machine generated
datasets such as [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Code Alpaca](https://github.com/sahil280114/codealpaca) which are two datasets
obtained by prompting OpenAI `text-davinci-003` engine.
## Our approach
While our method is similar to self-instruct and stanford alpaca, we included some relevant modifications to the pipeline to account for what we wanted.
- Rather than using `text-davinci-003`, we chose to prompt [StarCoder](https://arxiv.org/abs/2305.06161) which is a 10x smaller LLM developed for code use cases. However, it is possible to use any decoder based LLM on the hub.
- We changed our seed tasks in order to have the model generate code related tasks. We completed the seed tasks from code alpaca with 20 additional algorithm instructions.
- We switched from the generation format `"instruction":` - `"input":` - `"output":` to the format `"instruction":` - `"output":` by concatenating each instruction and its input under the
keyword `instruction`. We did so because the previous prompting format tended to make the model generate test cases as input and their solution as output, which is not what we wanted.
- Finally, we incorporated the possibility to change the trigger word in the prompt. We thus replaced the `"instruction" :` keyword by `"Here is the correct solution to the problem ":` which
resulted into much better generated instructions.
## Dataset generation
The generation of the dataset was time consuming and we chose our parameters to limit the computational burden of our method.
- Number of examples in context : 4
- 2 seed instructions
- 2 machine generated instructions
- Number of instructions to generate : 5000
- Stop words used in the generation : ["\n20", "20.", "20 ."]
- Similarity threshold for rouge score : 0.7
## Dataset quality
StarCoder, while being a great model is not as capable as `text-davinci-003`. In the generation, the model quickly reach sort of a ceiling in terms of creativity.
There are many instructions that are similar to each other, but it should not bother since they are not phrased the same.
## Post-processing
Post-processing is an important part of the pipeline since it improves the quality of the dataset despite the fact that it implies getting rid of some examples. First we
need to identify what we want to avoid :
- A generated solution which does not answer to the corresponding instruction
- An instruction that is too similar to another one.
### Self-consistency
We imagined a process that we named **self-consistency**. The idea is to reverse-prompt the model to see if it can generate a sound instruction that corresponds to the
solution (output) it is prompted with. This is a particularly difficult few-shot task, and unfortunately StarCoder does not perform incredibly well on it. With a few-shot parameters of `4`
(all being seed tasks), the model is able to recover 1135 instructions out of 5003, which amount for 22.6% of the raw dataset. Fortunately, the inability for starcoder to generate instructions for some
solutions does not mean we should get rid of them. For the solutions (outputs) with generated instructions, we can compare these with the ground truth. For that we can use [Sentence-BERT](https://arxiv.org/abs/1908.10084) because the comparison should focus the meaning
rather than the word to word similarity ratio. We have about 771 instructions (~68%) with a similarity score >= 0.5 with their ground truth. These can be seen as high quality examples, they form the `curated` set.
<p align="center">
<img src="https://huggingface.co/datasets/codeparrot/self-instruct-starcoder/resolve/main/output.png" alt="drawing" width="300", height="300"/>
</p>
### Uniqueness
Another approach that can be used to clean the raw dataset is to focus on distinct instructions. For a given instruction, we go through all the instructions generated before it to see if there is one with a similarity score >= 0.5.
If it is the case, we remove that instruction. This process removes about 94% of the raw dataset, the remaining instructions form the `unique` set.
### Compile
We also decided to build a set which contains solely the example featuring a code written in python 3 which does not code a compilation error.
## Dataset structure
```python
from datasets import load_dataset
dataset = load_dataset("codeparrot/self-instruct-starcoder")
DatasetDict({
compile: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 3549
})
curated: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 771
})
raw: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 5003
})
unique: Dataset({
features: ['instruction', 'output', 'most_similar', 'avg_similarity_score'],
num_rows: 308
})
}))
```
|Field|Type|Description|
|---|---|---|
|instruction|string|Instruction|
|output|string|Answer to the instruction|
|most_similar|string|Dictionnary containing the 10 most similar instructions generated before the current instruction along with the similarity scores|
|avg_similarity_score|float64| Average similarity score|
## Additional resources
- [Space(self-instruct-starcoder)](https://huggingface.co/spaces/codeparrot/self-instruct-starcoder)
- [Github Repository](https://github.com/ArmelRandy/Self-instruct)
## Citation
```
@misc{title={Self-Instruct-StarCoder},
author={Zebaze, Armel Randy},
doi={https://doi.org/10.57967/hf/0790},
}
``` | [
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flax-sentence-embeddings/stackexchange_math_jsonl | flax-sentence-embeddings | 2022-07-11T13:12:59Z | 568 | 5 | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [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)s
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [stackexchange](https://archive.org/details/stackexchange)
- **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings)
### Dataset Summary
We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites).
### Languages
Stack Exchange mainly consist of english language (en).
## Dataset Structure
### Data Instances
Each data samples is presented as follow:
```
{'title_body': 'How to determine if 3 points on a 3-D graph are collinear? Let the points $A, B$ and $C$ be $(x_1, y_1, z_1), (x_2, y_2, z_2)$ and $(x_3, y_3, z_3)$ respectively. How do I prove that the 3 points are collinear? What is the formula?',
'upvoted_answer': 'From $A(x_1,y_1,z_1),B(x_2,y_2,z_2),C(x_3,y_3,z_3)$ we can get their position vectors.\n\n$\\vec{AB}=(x_2-x_1,y_2-y_1,z_2-z_1)$ and $\\vec{AC}=(x_3-x_1,y_3-y_1,z_3-z_1)$.\n\nThen $||\\vec{AB}\\times\\vec{AC}||=0\\implies A,B,C$ collinear.',
'downvoted_answer': 'If the distance between |AB|+|BC|=|AC| then A,B,C are collinear.'}
```
This particular exampe corresponds to the [following page](https://math.stackexchange.com/questions/947555/how-to-determine-if-3-points-on-a-3-d-graph-are-collinear)
### Data Fields
The fields present in the dataset contain the following informations:
- `title_body`: This is the concatenation of the title and body from the question
- `upvoted_answer`: This is the body from the most upvoted answer
- `downvoted_answer`: This is the body from most downvoted answer
- `title`: This is the title from the question
### Data Splits
We provide three splits for this dataset, which only differs by the structure of the fieds which are retrieved:
- `titlebody_upvoted_downvoted_answer`: Includes title and body from the question as well as most upvoted and downvoted answer.
- `title_answer`: Includes title from the question as well as most upvoted answer.
- `titlebody_answer`: Includes title and body from the question as well as most upvoted answer.
| | Number of pairs |
| ----- | ------ |
| `titlebody_upvoted_downvoted_answer` | 17,083 |
| `title_answer` | 1,100,953 |
| `titlebody_answer` | 1,100,953 |
## Dataset Creation
### Curation Rationale
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
### Source Data
The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange)
#### Initial Data Collection and Normalization
We collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters.
When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers.
#### Who are the source language producers?
Questions and answers are written by the community developpers of Stack Exchange.
## Additional Information
### Licensing Information
Please see the license information at: https://archive.org/details/stackexchange
### Citation Information
```
@misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
```
### Contributions
Thanks to the Flax Sentence Embeddings team for adding this dataset. | [
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omerist/turknews-mini | omerist | 2022-10-15T12:38:10Z | 568 | 0 | [
"region:us"
] | null | 2022-10-15T12:38:03Z | ---
dataset_info:
features:
- name: review
dtype: string
- name: title
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 9064933.18105424
num_examples: 3534
- name: validation
num_bytes: 1008069.8189457601
num_examples: 393
download_size: 5732599
dataset_size: 10073003.0
---
# Dataset Card for "turknews-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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swj0419/WikiMIA | swj0419 | 2023-11-03T07:52:38Z | 568 | 9 | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"arxiv:2310.16789",
"region:us"
] | null | 2023-10-05T23:31:10Z | ---
license: mit
language:
- en
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: input
dtype: string
- name: label
dtype: int64
splits:
- name: WikiMIA_length32
num_bytes: 162091
num_examples: 776
- name: WikiMIA_length64
num_bytes: 221018
num_examples: 542
- name: WikiMIA_length128
num_bytes: 205118
num_examples: 250
- name: WikiMIA_length256
num_bytes: 134879
num_examples: 82
download_size: 465221
dataset_size: 723106
---
# 📘 WikiMIA Datasets
The **WikiMIA datasets** serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language models.
### 📌 Applicability
The datasets can be applied to various models released between **2017 to 2023**:
- LLaMA1/2
- GPT-Neo
- OPT
- Pythia
- text-davinci-001
- text-davinci-002
- ... and more.
## Loading the datasets
To load the dataset:
```python
from datasets import load_dataset
LENGTH = 64
dataset = load_dataset("swj0419/WikiMIA", split=f"WikiMIA_length{LENGTH}")
```
* Available Text Lengths: `32, 64, 128, 256`.
* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.
## 🛠️ Codebase
For evaluating MIA methods on our datasets, visit our [GitHub repository](https://github.com/swj0419/detect-pretrain-code).
## ⭐ Citing our Work
If you find our codebase and datasets beneficial, kindly cite our work:
```bibtex
@misc{shi2023detecting,
title={Detecting Pretraining Data from Large Language Models},
author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer},
year={2023},
eprint={2310.16789},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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omp | null | 2023-01-25T14:42:05Z | 567 | 1 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"license:cc-by-nc-sa-4.0",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- de
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: one-million-posts-corpus
pretty_name: One Million Posts
dataset_info:
- config_name: posts_labeled
features:
- name: ID_Post
dtype: string
- name: ID_Parent_Post
dtype: string
- name: ID_Article
dtype: string
- name: ID_User
dtype: string
- name: CreatedAt
dtype: string
- name: Status
dtype: string
- name: Headline
dtype: string
- name: Body
dtype: string
- name: PositiveVotes
dtype: int32
- name: NegativeVotes
dtype: int32
- name: Category
dtype:
class_label:
names:
'0': ArgumentsUsed
'1': Discriminating
'2': Inappropriate
'3': OffTopic
'4': PersonalStories
'5': PossiblyFeedback
'6': SentimentNegative
'7': SentimentNeutral
'8': SentimentPositive
- name: Value
dtype: int32
- name: Fold
dtype: int32
splits:
- name: train
num_bytes: 13955964
num_examples: 40567
download_size: 1329892
dataset_size: 13955964
- config_name: posts_unlabeled
features:
- name: ID_Post
dtype: string
- name: ID_Parent_Post
dtype: string
- name: ID_Article
dtype: string
- name: ID_User
dtype: string
- name: CreatedAt
dtype: string
- name: Status
dtype: string
- name: Headline
dtype: string
- name: Body
dtype: string
- name: PositiveVotes
dtype: int32
- name: NegativeVotes
dtype: int32
splits:
- name: train
num_bytes: 305770324
num_examples: 1000000
download_size: 79296188
dataset_size: 305770324
- config_name: articles
features:
- name: ID_Article
dtype: string
- name: Path
dtype: string
- name: publishingDate
dtype: string
- name: Title
dtype: string
- name: Body
dtype: string
splits:
- name: train
num_bytes: 43529400
num_examples: 12087
download_size: 10681288
dataset_size: 43529400
---
# Dataset Card for One Million Posts 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://ofai.github.io/million-post-corpus/
- **Repository:** https://github.com/OFAI/million-post-corpus
- **Paper:** https://dl.acm.org/doi/10.1145/3077136.3080711
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The “One Million Posts” corpus is an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).
DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, there is a discussion section below each news article where readers engage in online discussions. The data set contains a selection of user posts from the 12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and 1,000,000 unlabeled posts in the data set. The labeled posts were annotated by professional forum moderators employed by the newspaper.
The data set contains the following data for each post:
* Post ID
* Article ID
* Headline (max. 250 characters)
* Main Body (max. 750 characters)
* User ID (the user names used by the website have been re-mapped to new numeric IDs)
* Time stamp
* Parent post (replies give rise to tree-like discussion thread structures)
* Status (online or deleted by a moderator)
* Number of positive votes by other community members
* Number of negative votes by other community members
For each article, the data set contains the following data:
* Article ID
* Publishing date
* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
* Title
* Body
Detailed descriptions of the post selection and annotation procedures are given in the paper.
#### Annotated Categories
Potentially undesirable content:
* Sentiment (negative/neutral/positive)
An important goal is to detect changes in the prevalent sentiment in a discussion, e.g., the location within the fora and the point in time where a turn from positive/neutral sentiment to negative sentiment takes place.
* Off-Topic (yes/no)
Posts which digress too far from the topic of the corresponding article.
* Inappropriate (yes/no)
Swearwords, suggestive and obscene language, insults, threats etc.
* Discriminating (yes/no)
Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.
Neutral content that requires a reaction:
* Feedback (yes/no)
Sometimes users ask questions or give feedback to the author of the article or the newspaper in general, which may require a reply/reaction.
Potentially desirable content:
* Personal Stories (yes/no)
In certain fora, users are encouraged to share their personal stories, experiences, anecdotes etc. regarding the respective topic.
* Arguments Used (yes/no)
It is desirable for users to back their statements with rational argumentation, reasoning and sources.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Austrian German
## Dataset Structure
### Data Instances
An example from the `posts_labeled` config:
```json
{
"ID_Post": "79",
"ID_Parent_Post": "",
"ID_Article": "1",
"ID_User": "12071",
"CreatedAt": "2015-06-01 08:58:32.363",
"Status": "online",
"Headline": "",
"Body": "ich kann keinen hinweis finden, wo man sich hinwenden muss, sollte man als abonnent des standard, die zeitung nicht bekommt, ist dass bewusst so arrangiert?",
"PositiveVotes": 0,
"NegativeVotes": 0,
"Category": 5,
"Value": 1,
"Fold": 1
}
```
An example from the `posts_unlabeled` config:
```json
{
"ID_Post": "51",
"ID_Parent_Post": "",
"ID_Article": "1",
"ID_User": "11125",
"CreatedAt": "2011-05-15 08:37:11.313",
"Status": "online",
"Headline": "Ich würde es sehr begrüßen, wenn",
"Body": "Antworten erst beim Erscheinen als e-Mail dem Poster zugestellt würden.\r\n\r\nEs gibt User, die ihre Kommentare sofort nach Mail-Eingang irgendwo hinposten. Dadurch wird \r\n1. vor allem für andere Unser die Lesbarkeit wesentlich beeinträchtigt,\r\n2. kann das Post verdreht wiedergegeben werden,\r\n3. man ist immer wieder gezwungen die Antwort richtig zu stellen.\r\n\r\nPrivatfehden von Usern sollten, wenn schon zugelassen, für alle User nachvollziehbar sein.\r\n\r\nDanke!",
"PositiveVotes": 1,
"NegativeVotes": 0
}
```
An example from the `articles` config:
```json
{
"ID_Article": "41",
"Path": "Newsroom/Wirtschaft/Wirtschaftpolitik/Energiemarkt",
"publishingDate": "2015-06-01 12:39:35.00",
"Title": "Öl- und Gas-Riesen fordern weltweite CO2-Preise",
"Body": '<div class="section" id="content-main" itemprop="articleBody"><div class="copytext"><h2 itemprop="description">Brief von BP, Total, Shell, Statoil, BG Group und Eni unterzeichnet</h2><p>Paris/London/La Defense - Sechs große Öl- und Gaskonzerne haben mit Blick auf die Verhandlungen über einen neuen Welt-Klimavertrag ein globales Preissystem für CO2-Emissionen gefordert. Wenn der Ausstoß von CO2 Geld kostet, sei dies ein Anreiz für die Nutzung von Erdgas statt Kohle, mehr Energieeffizienz und Investitionen zur Vermeidung des Treibhausgases, heißt es in einem am Montag veröffentlichten Brief.</p>\n<p>Das Schreiben ist unterzeichnet von BP, Total, Shell, Statoil, BG Group und Eni. Die Unternehmen versicherten, sie seien bereit, ihren Teil zum Kampf gegen den <a href="/r1937/Klimawandel">Klimawandel</a> beizutragen. Dafür sei aber ein klarer und verlässlicher Politik-Rahmen nötig. (APA, 1.6.2015)</p> </div></div>'
}
```
### Data Fields
The data set contains the following data for each post:
* **ID_Post**: Post ID
* **ID_Parent_Post**: Parent post (replies give rise to tree-like discussion thread structures)
* **ID_Article**: Article ID
* **ID_User**: User ID (the user names used by the website have been re-mapped to new numeric IDs)
* **Headline**: Headline (max. 250 characters)
* **Body**: Main Body (max. 750 characters)
* **CreatedAt**: Time stamp
* **Status**: Status (online or deleted by a moderator)
* **PositiveVotes**: Number of positive votes by other community members
* **NegativeVotes**: Number of negative votes by other community members
Labeled posts also contain:
* **Category**: The category of the annotation, one of: ArgumentsUsed, Discriminating, Inappropriate, OffTopic, PersonalStories, PossiblyFeedback, SentimentNegative, SentimentNeutral, SentimentPositive
* **Value**: either 0 or 1, explicitly indicating whether or not the post has the specified category as a label (i.e. a category of `ArgumentsUsed` with value of `0` means that an annotator explicitly labeled that this post doesn't use arguments, as opposed to the mere absence of a positive label).
* **Fold**: a number between [0-9] from a 10-fold split by the authors
For each article, the data set contains the following data:
* **ID_Article**: Article ID
* **publishingDate**: Publishing date
* **Path**: Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)
* **Title**: Title
* **Body**: Body
### Data Splits
Training split only.
| name | train |
|-----------------|--------:|
| posts_labeled | 40567 |
| posts_unlabeled | 1000000 |
| articles | 12087 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
### Citation Information
```
@InProceedings{Schabus2018,
author = {Dietmar Schabus and Marcin Skowron},
title = {Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website},
booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)},
year = {2018},
address = {Miyazaki, Japan},
month = may,
pages = {1602-1605},
abstract = {This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.},
url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html},
}
```
### Contributions
Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset. | [
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Clinton/Text-to-sql-v1 | Clinton | 2023-07-26T11:09:22Z | 566 | 25 | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"SQL",
"region:us"
] | [
"text-generation"
] | 2023-07-26T10:36:49Z | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- SQL
size_categories:
- 100K<n<1M
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open-llm-leaderboard/details_facebook__opt-66b | open-llm-leaderboard | 2023-12-03T00:31:05Z | 566 | 0 | [
"region:us"
] | null | 2023-08-23T18:08:15Z | ---
pretty_name: Evaluation run of facebook/opt-66b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [facebook/opt-66b](https://huggingface.co/facebook/opt-66b) 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 5 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 aggregated 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_facebook__opt-66b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T00:30:57.404111](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-66b/blob/main/results_2023-12-03T00-30-57.404111.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 \"acc\": 0.016679302501895376,\n\
\ \"acc_stderr\": 0.0035275958887224556\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.016679302501895376,\n \"acc_stderr\": 0.0035275958887224556\n\
\ }\n}\n```"
repo_url: https://huggingface.co/facebook/opt-66b
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_23T18_07_59.118983
path:
- '**/details_harness|arc:challenge|25_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|arc:challenge|25_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_09T17_37_15.988083
path:
- '**/details_harness|drop|3_2023-09-09T17-37-15.988083.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-09T17-37-15.988083.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_09T17_37_15.988083
path:
- '**/details_harness|gsm8k|5_2023-09-09T17-37-15.988083.parquet'
- split: 2023_12_03T00_30_57.404111
path:
- '**/details_harness|gsm8k|5_2023-12-03T00-30-57.404111.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T00-30-57.404111.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hellaswag|10_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hellaswag|10_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T18:07:59.118983.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T00:29:23.220857.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T00:29:23.220857.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T00:29:23.220857.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T00:29:23.220857.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
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- split: 2023_08_24T00_29_23.220857
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
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- split: 2023_08_24T00_29_23.220857
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
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- split: 2023_08_24T00_29_23.220857
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-23T18:07:59.118983.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T18:07:59.118983.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T00:29:23.220857.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T18:07:59.118983.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-23T18:07:59.118983.parquet'
- split: 2023_08_24T00_29_23.220857
path:
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- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T00:29:23.220857.parquet'
- config_name: harness_winogrande_5
data_files:
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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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- config_name: original_mmlu_abstract_algebra_5
data_files:
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path:
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- split: latest
path:
- '**/details_original|mmlu:abstract_algebra|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_anatomy_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_astronomy_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_business_ethics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_clinical_knowledge_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_college_biology_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_college_chemistry_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_college_computer_science_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_college_mathematics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_college_medicine_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_college_physics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_computer_security_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_conceptual_physics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_econometrics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_electrical_engineering_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_elementary_mathematics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_formal_logic_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_global_facts_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_biology_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_chemistry_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_computer_science_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_european_history_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_geography_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_government_and_politics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_macroeconomics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_mathematics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_microeconomics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_physics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_psychology_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_statistics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_us_history_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_high_school_world_history_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_human_aging_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_human_sexuality_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_international_law_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_jurisprudence_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_logical_fallacies_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_machine_learning_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_management_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:management|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:management|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_marketing_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_medical_genetics_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_miscellaneous_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_moral_disputes_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_moral_scenarios_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_nutrition_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_philosophy_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_prehistory_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_professional_accounting_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_professional_law_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_professional_medicine_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_professional_psychology_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_public_relations_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_security_studies_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_sociology_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_us_foreign_policy_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:virology|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-28T21:15:14.969062.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T21_15_14.969062
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T21:15:14.969062.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T21:15:14.969062.parquet'
- config_name: results
data_files:
- split: 2023_08_23T18_07_59.118983
path:
- results_2023-08-23T18:07:59.118983.parquet
- split: 2023_08_24T00_29_23.220857
path:
- results_2023-08-24T00:29:23.220857.parquet
- split: 2023_08_28T21_15_14.969062
path:
- results_2023-08-28T21:15:14.969062.parquet
- split: 2023_09_09T17_37_15.988083
path:
- results_2023-09-09T17-37-15.988083.parquet
- split: 2023_12_03T00_30_57.404111
path:
- results_2023-12-03T00-30-57.404111.parquet
- split: latest
path:
- results_2023-12-03T00-30-57.404111.parquet
---
# Dataset Card for Evaluation run of facebook/opt-66b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/facebook/opt-66b
- **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 [facebook/opt-66b](https://huggingface.co/facebook/opt-66b) 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 5 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 aggregated 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_facebook__opt-66b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T00:30:57.404111](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__opt-66b/blob/main/results_2023-12-03T00-30-57.404111.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": {
"acc": 0.016679302501895376,
"acc_stderr": 0.0035275958887224556
},
"harness|gsm8k|5": {
"acc": 0.016679302501895376,
"acc_stderr": 0.0035275958887224556
}
}
```
### 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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manu/french-bench-grammar-vocab-reading | manu | 2023-10-26T14:19:28Z | 565 | 0 | [
"region:us"
] | null | 2023-10-25T22:03:38Z | ---
configs:
- config_name: default
data_files:
- split: Grammar
path: data/Grammar-*
- split: Vocabulary
path: data/Vocabulary-*
- split: Reading
path: data/Reading-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
- name: difficulty
dtype: string
- name: language
dtype: string
- name: subject
dtype: string
- name: url
dtype: string
- name: answerA
dtype: string
- name: answerB
dtype: string
- name: answerC
dtype: string
- name: answerD
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: Grammar
num_bytes: 29094
num_examples: 119
- name: Vocabulary
num_bytes: 30944
num_examples: 119
- name: Reading
num_bytes: 115507
num_examples: 71
download_size: 0
dataset_size: 175545
---
# Dataset Card for "french-bench-grammar-vocab-reading"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cointegrated/ru-paraphrase-NMT-Leipzig | cointegrated | 2022-10-23T12:23:15Z | 564 | 4 | [
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:ru",
"license:cc-by-4.0",
"conditional-text-generation",
"paraphrase-generation",
"paraphrase",
"region:us"
] | [
"text-generation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- ru
license:
- cc-by-4.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
task_categories:
- text-generation
pretty_name: ru-paraphrase-NMT-Leipzig
tags:
- conditional-text-generation
- paraphrase-generation
- paraphrase
---
# Dataset Card for **cointegrated/ru-paraphrase-NMT-Leipzig**
## 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
- **Paper:** https://habr.com/ru/post/564916/
- **Point of Contact:** [@cointegrated](https://huggingface.co/cointegrated)
### Dataset Summary
The dataset contains 1 million Russian sentences and their automatically generated paraphrases.
It was created by David Dale ([@cointegrated](https://huggingface.co/cointegrated)) by translating the `rus-ru_web-public_2019_1M` corpus from [the Leipzig collection](https://wortschatz.uni-leipzig.de/en/download) into English and back into Russian. A fraction of the resulting paraphrases are invalid, and should be filtered out.
The blogpost ["Перефразирование русских текстов: корпуса, модели, метрики"](https://habr.com/ru/post/564916/) provides a detailed description of the dataset and its properties.
The dataset can be loaded with the following code:
```Python
import datasets
data = datasets.load_dataset(
'cointegrated/ru-paraphrase-NMT-Leipzig',
data_files={"train": "train.csv","val": "val.csv","test": "test.csv"},
)
```
Its output should look like
```
DatasetDict({
train: Dataset({
features: ['idx', 'original', 'en', 'ru', 'chrf_sim', 'labse_sim'],
num_rows: 980000
})
val: Dataset({
features: ['idx', 'original', 'en', 'ru', 'chrf_sim', 'labse_sim'],
num_rows: 10000
})
test: Dataset({
features: ['idx', 'original', 'en', 'ru', 'chrf_sim', 'labse_sim'],
num_rows: 10000
})
})
```
### Supported Tasks and Leaderboards
The dataset can be used to train and validate models for paraphrase generation or (if negative sampling is used) for paraphrase detection.
### Languages
Russian (main), English (auxilliary).
## Dataset Structure
### Data Instances
Data instances look like
```
{
"labse_sim": 0.93502015,
"chrf_sim": 0.4946451012684782,
"idx": 646422,
"ru": "О перспективах развития новых медиа-технологий в РФ расскажут на медиафоруме Енисея.",
"original": "Перспективы развития новых медиатехнологий в Российской Федерации обсудят участники медиафорума «Енисей.",
"en": "Prospects for the development of new media technologies in the Russian Federation will be discussed at the Yenisey Media Forum."
}
```
Where `original` is the original sentence, and `ru` is its machine-generated paraphrase.
### Data Fields
- `idx`: id of the instance in the original corpus
- `original`: the original sentence
- `en`: automatic translation of `original` to English
- `ru`: automatic translation of `en` back to Russian, i.e. a paraphrase of `original`
- `chrf_sim`: [ChrF++](https://huggingface.co/metrics/chrf) similarity of `original` and `ru`
- `labse_sim`: cosine similarity of [LaBSE](https://huggingface.co/cointegrated/LaBSE-en-ru) embedings of `original` and `ru`
- `forward_entailment`: predicted probability that `original` entails `ru`
- `backward_entailment`: predicted probability that `ru` entails `original`
- `p_good`: predicted probability that `ru` and `original` have equivalent meaning
### Data Splits
Train – 980K, validation – 10K, test – 10K. The splits were generated randomly.
## Dataset Creation
### Curation Rationale
There are other Russian paraphrase corpora, but they have major drawbacks:
- The best known [corpus from paraphraser.ru 2016 contest](http://paraphraser.ru/download/) is rather small and covers only the News domain.
- [Opusparcus](https://huggingface.co/datasets/GEM/opusparcus), [ParaPhraserPlus](http://paraphraser.ru/download/), and [corpora of Tamara Zhordanija](https://github.com/tamriq/paraphrase) are noisy, i.e. a large proportion of sentence pairs in them have substantial difference in meaning.
- The Russian part of [TaPaCo](https://huggingface.co/datasets/tapaco) has very high lexical overlap in the sentence pairs; in other words, their paraphrases are not diverse enough.
The current corpus is generated with a dual objective: the parphrases should be semantically as close as possible to the original sentences, while being lexically different from them. Back-translation with restricted vocabulary seems to achieve this goal often enough.
### Source Data
#### Initial Data Collection and Normalization
The `rus-ru_web-public_2019_1M` corpus from [the Leipzig collection](https://wortschatz.uni-leipzig.de/en/download) as is.
The process of its creation is described [in this paper](http://www.lrec-conf.org/proceedings/lrec2012/pdf/327_Paper.pdf):
D. Goldhahn, T. Eckart & U. Quasthoff: Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages.
In: *Proceedings of the 8th International Language Resources and Evaluation (LREC'12), 2012*.
#### Automatic paraphrasing
The paraphrasing was carried out by translating the original sentence to English and then back to Russian.
The models [facebook/wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) and [facebook/wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) were used for translation.
To ensure that the back-translated texts are not identical to the original texts, the final decoder was prohibited to use the token n-grams from the original texts.
The code below implements the paraphrasing function.
```python
import torch
from transformers import FSMTModel, FSMTTokenizer, FSMTForConditionalGeneration
tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru")
model = FSMTForConditionalGeneration.from_pretrained("facebook/wmt19-en-ru")
inverse_tokenizer = FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en")
inverse_model = FSMTForConditionalGeneration.from_pretrained("facebook/wmt19-ru-en")
model.cuda();
inverse_model.cuda();
def paraphrase(text, gram=4, num_beams=5, **kwargs):
""" Generate a paraphrase using back translation.
Parameter `gram` denotes size of token n-grams of the original sentence that cannot appear in the paraphrase.
"""
input_ids = inverse_tokenizer.encode(text, return_tensors="pt")
with torch.no_grad():
outputs = inverse_model.generate(input_ids.to(inverse_model.device), num_beams=num_beams, **kwargs)
other_lang = inverse_tokenizer.decode(outputs[0], skip_special_tokens=True)
# print(other_lang)
input_ids = input_ids[0, :-1].tolist()
bad_word_ids = [input_ids[i:(i+gram)] for i in range(len(input_ids)-gram)]
input_ids = tokenizer.encode(other_lang, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(input_ids.to(model.device), num_beams=num_beams, bad_words_ids=bad_word_ids, **kwargs)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
return decoded
```
The corpus was created by running the above `paraphrase` function on the original sentences with parameters `gram=3, num_beams=5, repetition_penalty=3.14, no_repeat_ngram_size=6`.
### Annotations
#### Annotation process
The dataset was annotated by several automatic metrics:
- [ChrF++](https://huggingface.co/metrics/chrf) between `original` and `ru` sentences;
- cosine similarity between [LaBSE](https://huggingface.co/cointegrated/LaBSE-en-ru) embeddings of these sentences;
- forward and backward entailment probabilites predictd by the [rubert-base-cased-nli-twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway) model;
- `p_good`, a metric aggregating the four metrics above into a single number. It is obtained with a logistic regression trained on 100 randomly chosen from the train set and manually labelled sentence pairs.
#### Who are the annotators?
Human annotation was involved only for a small subset used to train the model for `p_good`. It was conduced by the dataset author, @cointegrated.
### Personal and Sensitive Information
The dataset is not known to contain any personal or sensitive information.
The sources and processes of original data collection are described at https://wortschatz.uni-leipzig.de/en/download.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset may enable creation for paraphrasing systems that can be used both for "good" purposes (such as assisting writers or augmenting text datasets), and for "bad" purposes (such as disguising plagiarism). The authors are not responsible for any uses of the dataset.
### Discussion of Biases
The dataset may inherit some of the biases of [the underlying Leipzig web corpus](https://wortschatz.uni-leipzig.de/en/download) or the neural machine translation models ([1](https://huggingface.co/facebook/wmt19-ru-en), [2](https://huggingface.co/facebook/wmt19-en-ru)) with which it was generated.
### Other Known Limitations
Most of the paraphrases in the dataset are valid (by a rough estimante, at least 80%). However, in some sentence pairs there are faults:
- Named entities are often spelled in different ways (e.g. `"Джейкоб" -> "Яков") or even replaced with other entities (e.g. `"Оймякон" -> "Оймянск" or `"Верхоянск" -> "Тольятти"`).
- Sometimes the meaning of words or phrases changes signigicantly, e.g. `"полустанок" -> "полумашина"`, or `"были по колено в грязи" -> "лежали на коленях в иле"`.
- Sometimes the syntax is changed in a meaning-altering way, e.g. `"Интеллектуальное преимущество Вавилова и его соратников над демагогами из рядов сторонников новой агробиологии разительно очевидно." -> "Интеллектуал Вавилов и его приспешники в новой аграрной биологии явно превзошли демогогов."`.
- Grammatical properties that are present in Russian morphology but absent in English, such as gender, are often lost, e.g. `"Я не хотела тебя пугать" -> "Я не хотел пугать вас"`.
The field `labse_sim` reflects semantic similarity between the sentences, and it can be used to filter out at least some poor paraphrases.
## Additional Information
### Dataset Curators
The dataset was created by [David Dale](https://daviddale.ru/en), a.k.a. [@cointegrated](https://huggingface.co/cointegrated).
### Licensing Information
This corpus, as well as the original Leipzig corpora, are licensed under [CC BY](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
[This blog post](https://habr.com/ru/post/564916/) can be cited:
```
@misc{dale_paraphrasing_2021,
author = "Dale, David",
title = "Перефразирование русских текстов: корпуса, модели, метрики",
editor = "habr.com",
url = "https://habr.com/ru/post/564916/",
month = {June},
year = {2021},
note = {[Online; posted 28-June-2021]},
}
```
### Contributions
Thanks to [@avidale](https://github.com/avidale) for adding this dataset. | [
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mteb/arxiv-clustering-p2p | mteb | 2022-09-27T19:15:11Z | 564 | 0 | [
"language:en",
"region:us"
] | null | 2022-05-10T13:00:10Z | ---
language:
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gnad10 | null | 2023-01-25T14:31:03Z | 563 | 3 | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-from-One-Million-Posts-Corpus",
"language:de",
"license:cc-by-nc-sa-4.0",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-from-One-Million-Posts-Corpus
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: 10k German News Articles Datasets
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Web
'1': Panorama
'2': International
'3': Wirtschaft
'4': Sport
'5': Inland
'6': Etat
'7': Wissenschaft
'8': Kultur
splits:
- name: train
num_bytes: 24418224
num_examples: 9245
- name: test
num_bytes: 2756405
num_examples: 1028
download_size: 27160809
dataset_size: 27174629
---
# Dataset Card for 10k German News Articles Datasets
## 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:** [10k German News Article Dataset](https://tblock.github.io/10kGNAD/)
- **Repository:** [10k German News Article Dataset](https://github.com/tblock/10kGNAD)
- **Point of Contact:** [Steven Liu]([email protected])
### Dataset Summary
The 10k German News Article Dataset consists of 10273 German language news articles from the online Austrian
newspaper website DER Standard. Each news article has been classified into one of 9 categories by professional
forum moderators employed by the newspaper. This dataset is extended from the original
[One Million Posts Corpus](https://ofai.github.io/million-post-corpus/). The dataset was created to support
topic classification in German because a classifier effective on a English dataset may not be as effective on
a German dataset due to higher inflections and longer compound words. Additionally, this dataset can be used
as a benchmark dataset for German topic classification.
### Supported Tasks and Leaderboards
This dataset can be used to train a model, like [BERT](https://huggingface.co/bert-base-uncased) for `topic classification` on German news articles. There are 9 possible categories.
### Languages
The text is in German and it comes from an online Austrian newspaper website. The BCP-47 code for German is
`de-DE`.
## Dataset Structure
### Data Instances
An example data instance contains a German news article (title and article are concatenated) and it's corresponding topic category.
```
{'text': ''Die Gewerkschaft GPA-djp lanciert den "All-in-Rechner" und findet, dass die Vertragsform auf die Führungsebene beschränkt gehört. Wien – Die Gewerkschaft GPA-djp sieht Handlungsbedarf bei sogenannten All-in-Verträgen.'
'label': 'Wirtschaft'
}
```
### Data Fields
* `text`: contains the title and content of the article
* `label`: can be one of 9 possible topic categories (`Web`, `Panorama`, `International`, `Wirtschaft`, `Sport`, `Inland`, `Etat`, `Wissenschaft`, `Kultur`)
### Data Splits
The data is split into a training set consisting of 9245 articles and a test set consisting of 1028 articles.
## Dataset Creation
### Curation Rationale
The dataset was created to support topic classification in the German language. English text classification datasets are common ([AG News](https://huggingface.co/datasets/ag_news) and [20 Newsgroup](https://huggingface.co/datasets/newsgroup)), but German datasets are less common. A classifier trained on an English dataset may not work as well on a set of German text due to grammatical differences. Thus there is a need for a German dataset for effectively assessing model performance.
### Source Data
#### Initial Data Collection and Normalization
The 10k German News Article Dataset is extended from the One Million Posts Corpus. 10273 German news articles were collected from this larger corpus. In the One Million Posts Corpus, each article has a topic path like
`Newsroom/Wirtschaft/Wirtschaftpolitik/Finanzmaerkte/Griechenlandkrise`. The 10kGNAD uses the second part of the topic path as the topic label. Article title and texts are concatenated into one text and author names are removed to avoid keyword classification on authors who write frequently on a particular topic.
#### Who are the source language producers?
The language producers are the authors of the Austrian newspaper website DER Standard.
### 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
This dataset was curated by Timo Block.
### Licensing Information
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license.
### Citation Information
Please consider citing the authors of the "One Million Post Corpus" if you use the dataset.:
```
@InProceedings{Schabus2017,
Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp},
Title = {One Million Posts: A Data Set of German Online Discussions},
Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},
Pages = {1241--1244},
Year = {2017},
Address = {Tokyo, Japan},
Doi = {10.1145/3077136.3080711},
Month = aug
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | [
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result-kand2-sdxl-wuerst-karlo/7f0dfe44 | result-kand2-sdxl-wuerst-karlo | 2023-09-27T19:12:09Z | 562 | 0 | [
"region:us"
] | null | 2023-09-27T19:12:08Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 212
num_examples: 10
download_size: 1370
dataset_size: 212
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7f0dfe44"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Sprakbanken/nb_samtale | Sprakbanken | 2023-10-06T14:43:06Z | 561 | 0 | [
"task_categories:automatic-speech-recognition",
"language:nb",
"language:nn",
"language:no",
"license:cc0-1.0",
"dialects",
"podcasts",
"live-events",
"conversational",
"speech",
"region:us"
] | [
"automatic-speech-recognition"
] | 2023-08-22T12:41:51Z | ---
language:
- nb
- nn
- 'no'
license: cc0-1.0
task_categories:
- automatic-speech-recognition
tags:
- dialects
- podcasts
- live-events
- conversational
- speech
---
# Dataset Card for Sprakbanken/nb_samtale
## Dataset Description
- **Homepage:** [nb.no/sprakbanken](https://www.nb.no/sprakbanken/)
- **Repository:** [Resource catalogue, no. 85](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-85/)
- **Paper:** [NB_Samtale_About_the_corpus.pdf](https://www.nb.no/sbfil/taledata/NB_Samtale_About_the_corpus.pdf)
- **Point of Contact:** [Språkbanken](mailto:[email protected])
### Dataset Summary
NB Samtale is a speech corpus made by the Language Bank at the National Library of Norway. The corpus contains orthographically transcribed speech from podcasts and recordings of live events at the National Library. The corpus is intended as an open source dataset for Automatic Speech Recognition (ASR) development, and is specifically aimed at improving ASR systems’ handle on conversational speech.
The corpus consists of 12,080 segments, a total of 24 hours transcribed speech from 69 speakers. The corpus ensures both gender and dialect variation, and speakers from five broad dialect areas are represented. Both Bokmål and Nynorsk transcriptions are present in the corpus, with Nynorsk making up approximately 25% of the transcriptions.
We greatly appreciate feedback and suggestions for improvements.
### Supported Tasks
- Automatic Speech Recognition for verbatim transcriptions of conversational speech, as well as for standardised, orthographic transcriptions.
- Speaker Diarization: The sentence segments all have a speaker ID, which is unique per speaker, and the same speaker will have the same speaker ID across source files.
- Audio classification: Each segment could be classified with one of the metadata features.
### Languages
The transcription texts are in either Norwegian bokmål or Norwegian nynorsk.
The audio is in Norwegian, in the speakers' respective dialects.
We have categorized them into five dialect areas:
Dialect area (en) | Dialect area (nb) | Counties
--- | --- | ---
Eastern Norway | Østlandet | Agder, Innlandet, Oslo, Vestfold og Telemark, Viken
Southwest Norway | Sørvestlandet | Rogaland
Western Norway | Vestlandet | Møre og Romsdal, Vestland
Central Norway | Midt-Norge |Trøndelag
Northern Norway | Nord-Norge | Nordland, Troms og Finnmark
## Dataset Structure
### Data Instances
A data point is an audio segment, including a relative path to the `.wav`-file, and the transcription. Additional information is provided about the speaker, the orthographic standard for the transcription, whether the segment overlaps with the previous or next, and the setting for the recording. The transcription also comes in 3 different normalized versions: "orthographic" (orthographically correct text, with punctuation, integer numbers, and standardized word forms), "verbatim" (with tokens marking hesitations, laughter, foreign phrases and unknown words, but no punctuation) and "annotations" (as is from the annotation process, with punctuation, tags, and alternate word forms).
```
{
'source_file_id': 'nb-1',
'segment_id': '0008970-0013860',
'segment_order': 0,
'duration': 4.89,
'overlap_previous': False,
'overlap_next': False,
'speaker_id': 'P36',
'gender': 1,
'dialect': 0,
'orthography': 0,
'source_type': 0,
'file_name': 'data/train/bm/nb-1_0008970-0013860.wav',
'transcription': 'hallo og velkommen hit til Nasjonalbiblioteket.',
'annotations': 'hallo og velkommen hit til Nasjonalbiblioteket.',
'orthographic': 'hallo og velkommen hit til Nasjonalbiblioteket.',
'verbatim': 'hallo og velkommen hit til Nasjonalbiblioteket',
'audio': {
'path': "data/train/bm/nb-1_0008970-0013860.wav",
'array': array([-0.00033569, 0.00222778, -0.0005188 , ..., 0.00067139,
0.00057983, 0.0005188 ]),
'sampling_rate': 16000}
}
```
### Data Fields
data field | description | Value type / example
--- | --- | ---
`source_file_id` | original file the segment appears in. | e.g. `50f-X`, `tr-X` or `nb-X`, where X is a number. (str)
`segment_id` | segment start and end timestamp. | `{starttime}-{endtime}` (str)
`segment_order` | order of segment in the original file. | (int)
`duration` | duration of segment in seconds. | (float)
`overlap_previous` | whether the beginning of the segment overlaps with the previous segment | `True` or `False` (bool)
`overlap_next` | whether the end of the segment overlaps with the next segment. | `True` or `False` (bool)
`speaker_id` | speaker ID for the speaker transcribed in the segment. | `P0` - `P69` (str)
`gender` | speaker’s binary gender (female or male), mapped to a HuggingFace datasets ClassLabel index number | `0`: f or `1`: m (int)
`dialect` | the speaker’s dialect area, as a ClassLabel index number for the areas east (e), north (n), southwest (sw), central (t), west (w). | `0`: e, `1`: n, `2`: sw, `3`: t, or `4`: w (int)
`orthography` | the written norm of the transcription, either bokmål (`bm`) or nynorsk (`nn`) as a ClassLabel index number | `0`: bm or `1`: nn (int)
`source_type` | type of recording of original file, either `live-event` or `podcast`, as a ClassLabel index number | `0`: live-event or `1`: podcast (int)
`file_name` | file name of the audio segment, without the path | `{source_file_id}_{segment_id}.wav` (str)
`transcription` | orthographic transcription text | (str)
`orthographic` | close to orthographically correct text transcription in the given `orthography` standard. Contains punctuation, numbers, and standard word forms. | (str)
`verbatim` | transcription text mapping to the uttered words as close as possible. Contains tokens marking hesitations, laughter, foreign phrases and unknown words, but no punctuation. | (str)
`annotations` | transcription text "as is" from the annotation process. Contains false starts, metatags for non-linguistic noises, punctuation, and alternate word forms (`<uttered word>\<orthographic standard word>`) | (str)
`audio` | the audio segment data, with the relative file `path`, the bytes `array`, and the `sampling_rate` | (dict)
"orthographic" (orthographically correct text, with punctuation, integer numbers, and standardized word forms), "verbatim" (with tokens marking hesitations, laughter, foreign phrases and unknown words, but no punctuation) and "annotations" (as is from the annotation process, with punctuation, tags, and alternate word forms).
### Data Splits
The data is split into a `train`, `validation`, and `test` set, stratified on three parameters: source type, gender and dialect.
Gender and dialect naturally refers to the gender and dialect of the speakers.
The data has not been split on speaker ID to avoid speaker overlap in the various sets because this proved impossible
while still maintaining a decent distribution of the other parameters, especially dialect variation.
The source type refers to whether the source material is one of the two podcasts (50f, tr) or
a National Library live event (nb).
The two types have different features.
The podcasts are overall good quality studio recordings with little background noise, echo and such.
The live events are recorded in rooms or reception halls at the National Library and have more background
noise, echo and inconsistent audio quality.
Many also have a live audience.
## Dataset Creation
### Source data
The audio is collected from podcasts we have been permitted to share openly – namely 50
forskere from UiT and Trondheim kommunes podkast from Trondheim municipality – as well
as some of The National Library’s own recordings of live events. The podcasts are studio
recordings, while the National Library events take place in rooms and reception halls at the
National Library, sometimes in front of an audience.
#### Who are the source language producers?
Guests and hosts of the respective recording events, either podcasts produced in a studio or lectures, debates and conversations in a public live event.
### Annotations
#### Annotation process
The recordings were segmented and transcribed in the transcription software ELAN. The
recordings were transcribed automatically using a Norwegian ASR system created by the AI-
lab at the National Library of Norway. The speech was segmented and transcribed with
speaker diarization, separating the speakers into separate transcription tiers. These
segments and transcriptions were then manually corrected by a transcriber according to a
set of guidelines. All the manual transcriptions were reviewed by a second person in order to
avoid substantial discrepancies between transcribers. Finally all the transcriptions were
spell-checked, and checked for any unwanted numbers or special characters.
See the [official dataset documentation](https://www.nb.no/sbfil/taledata/NB_Samtale_About_the_corpus.pdf) for more details.
The full set of guidelines for segmentation and transcription are given in Norwegian in [NB_Samtale_transcription_guidelines.pdf](https://www.nb.no/sbfil/taledata/NB_Samtale_transcription_guidelines.pdf).
#### Who are the annotators?
The Norwegian Language Bank (Språkbanken).
### Personal and Sensitive Information
The data fields `gender`, `dialect` and `speaker_id` pertain to the speakers themselves.
A single speaker will have the same `speaker_id` if they appear in several different source files.
## Considerations for Using the Data
### Discussion of Biases
The recordings were for the most part selected based on the gender and dialect of the
speakers to ensure gender balance and broad dialectal representation. The corpus has a
near 50/50 divide between male and female speakers (male 54%, female 46%). The
Norwegian dialects have been divided into five broad dialect areas that are all represented in
the corpus. However, Eastern Norwegian has the greatest representation at about 50%
speaker time, while the other areas fall between 8% and 20% speaker time.
## Additional Information
### Dataset Curators
The content of the dataset was created by the Norwegian Language Bank (Språkbanken) at the National Library of Norway.
[Marie Iversdatter Røsok](mailto:[email protected]), [Ingerid Løyning Dale](mailto:[email protected]) and [Per Erik Solberg](mailto:[email protected]) contributed in creating this dataset.
Thanks to the HuggingFace team for assistance.
### Licensing Information
The NB Samtale dataset is released with the [CC-ZERO-license](https://creativecommons.org/publicdomain/zero/1.0/), i.e., it is public domain and can be used for any purpose and reshared without permission.
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argilla/text-descriptives-metadata | argilla | 2023-10-30T13:42:51Z | 561 | 0 | [
"size_categories:1K<n<10K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-10-20T13:28:10Z | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
dataset_info:
features:
- name: prompt
dtype: string
id: field
- name: context
dtype: string
id: field
- name: response
list:
- name: user_id
dtype: string
id: question
- name: value
dtype: string
id: suggestion
- name: status
dtype: string
id: question
- name: response-suggestion
dtype: string
id: suggestion
- name: response-suggestion-metadata
struct:
- name: type
dtype: string
id: suggestion-metadata
- name: score
dtype: float32
id: suggestion-metadata
- name: agent
dtype: string
id: suggestion-metadata
- name: external_id
dtype: string
id: external_id
- name: metadata
dtype: string
id: metadata
splits:
- name: train
num_bytes: 3008948
num_examples: 1030
download_size: 1725693
dataset_size: 3008948
---
# Dataset Card for text-descriptives-metadata
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/text-descriptives-metadata")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/text-descriptives-metadata")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| prompt | Prompt | FieldTypes.text | True | True |
| context | Context | FieldTypes.text | False | True |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| response | Response | QuestionTypes.text | True | N/A | N/A |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
**✨ NEW** The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"context": null,
"prompt": "Can brain cells move? By movement I mean long distance migration (preferably within the brain only)."
},
"metadata": {
"entropy": 0.4352176404374839,
"flesch_reading_ease": 82.39000000000001,
"n_characters": 85,
"passed_quality_check": "True"
},
"responses": [],
"suggestions": [
{
"agent": null,
"question_name": "response",
"score": null,
"type": null,
"value": "The question is relatively broad and one should take into account that the brain not only consists of neurons, but also glial cells (supportive cells) and pre-mitotic neuronal stem cells. Furthermore, as critical fellow-scientists have indicated, developmental stage is very important, as the developing embryonic brain is very different from the adult brain.\nHowever, after sifting through various publications, the answer to the question is actually remarkably simple: Yes, brain cells migrate.\nIn the adult brain glial cells migrate in the brain (Kl\u00e4mbt, 2009). Glial cells are involved in a myriad of functions, but a notable example of migrating glial cells are the oligodendrocytes that migrate relative long distances to find their target axons onto which they wrap themselves to form the insulating myelin sheath (Tsai and Miller, 2002).\nNeuronal stem cells migrate over long distances in response to injury (Imitola et al., 2004) and they migrate from specific stem-cell locations (e.g., hippocampus and subventricular zone) to other regions (Clarke, 2003).\nPost-mitotic, but non-differentiated neurons have been shown to migrate in the adult brain in fish (Scott et al., 2012), and in mammals and non-human primates as well (Sawada et al., 2011).\nNot surprisingly, glial cells, stem cells and neurons also migrate during embryonic development. Most notably, post-mitotic neurons destined to fulfill peripheral functions have to migrate over relatively long distances from the neural crest to their target locations (Neuroscience, 2nd ed, Neuronal Migration)."
}
]
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"context": null,
"external_id": null,
"metadata": "{\"n_characters\": 85, \"passed_quality_check\": \"True\", \"flesch_reading_ease\": 82.39000000000001, \"entropy\": 0.4352176404374839}",
"prompt": "Can brain cells move? By movement I mean long distance migration (preferably within the brain only).",
"response": [],
"response-suggestion": "The question is relatively broad and one should take into account that the brain not only consists of neurons, but also glial cells (supportive cells) and pre-mitotic neuronal stem cells. Furthermore, as critical fellow-scientists have indicated, developmental stage is very important, as the developing embryonic brain is very different from the adult brain.\nHowever, after sifting through various publications, the answer to the question is actually remarkably simple: Yes, brain cells migrate.\nIn the adult brain glial cells migrate in the brain (Kl\u00e4mbt, 2009). Glial cells are involved in a myriad of functions, but a notable example of migrating glial cells are the oligodendrocytes that migrate relative long distances to find their target axons onto which they wrap themselves to form the insulating myelin sheath (Tsai and Miller, 2002).\nNeuronal stem cells migrate over long distances in response to injury (Imitola et al., 2004) and they migrate from specific stem-cell locations (e.g., hippocampus and subventricular zone) to other regions (Clarke, 2003).\nPost-mitotic, but non-differentiated neurons have been shown to migrate in the adult brain in fish (Scott et al., 2012), and in mammals and non-human primates as well (Sawada et al., 2011).\nNot surprisingly, glial cells, stem cells and neurons also migrate during embryonic development. Most notably, post-mitotic neurons destined to fulfill peripheral functions have to migrate over relatively long distances from the neural crest to their target locations (Neuroscience, 2nd ed, Neuronal Migration).",
"response-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **prompt** is of type `FieldTypes.text`.
* (optional) **context** is of type `FieldTypes.text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **response** is of type `QuestionTypes.text`.
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **response-suggestion** is of type `QuestionTypes.text`.
Additionally, we also have two more fields that are optional and are the following:
* **✨ NEW** **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
This is a supervised fine-tuning dataset that contains instructions. Please write the response to the instruction in the response field. Take the context into account when writing the response.
#### 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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EleutherAI/pile-deduped-pythia-random-sampled | EleutherAI | 2023-08-25T07:26:47Z | 560 | 2 | [
"region:us"
] | null | 2023-03-29T13:15:01Z | ---
dataset_info:
features:
- name: Index
dtype: int64
- name: 70M
dtype: float64
- name: 160M
dtype: float64
- name: 410M
dtype: float64
- name: 1B
dtype: float64
- name: 1.4B
dtype: float64
- name: 2.8B
dtype: float64
- name: 6.9B
dtype: float64
- name: 12B
dtype: float64
- name: Tokens
sequence: uint16
splits:
- name: train
num_bytes: 1020000000
num_examples: 5000000
download_size: 915854656
dataset_size: 1020000000
---
# Dataset Card for "pile-deduped-pythia-random-sampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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GEM/web_nlg | GEM | 2022-10-24T15:31:09Z | 559 | 2 | [
"task_categories:table-to-text",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"data-to-text",
"region:us"
] | [
"table-to-text"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: web_nlg
tags:
- data-to-text
---
# Dataset Card for GEM/web_nlg
## Dataset Description
- **Homepage:** https://webnlg-challenge.loria.fr/
- **Repository:** https://gitlab.com/shimorina/webnlg-dataset
- **Paper:** http://www.aclweb.org/anthology/P17-1017, [WebNLG Challenge 2017 Report
- **Leaderboard:** https://beng.dice-research.org/gerbil/
- **Point of Contact:** [Needs More Information]
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/web_nlg).
### Dataset Summary
WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets and short texts that cover about 450 different DBpedia properties. The WebNLG data was originally created to promote the development of RDF verbalisers able to generate short text and to handle micro-planning (i.e., sentence segmentation and ordering, referring expression generation, aggregation); the goal of the task is to generate texts starting from 1 to 7 input triples which have entities in common (so the input is actually a connected Knowledge Graph). The dataset contains about 17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets and 19,000 crowdsourced texts in Russian. A challenging test set section with entities and/or properties that have not been seen at training time is available.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/web_nlg')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/web_nlg).
#### website
[Website](https://webnlg-challenge.loria.fr/)
#### paper
[First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf)
#### authors
The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil).
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://webnlg-challenge.loria.fr/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Gitlab](https://gitlab.com/shimorina/webnlg-dataset)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf)
#### 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 -->
Initial release of the dataset:
```
@inproceedings{gardent2017creating,
author = "Gardent, Claire
and Shimorina, Anastasia
and Narayan, Shashi
and Perez-Beltrachini, Laura",
title = "Creating Training Corpora for NLG Micro-Planners",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
year = "2017",
publisher = "Association for Computational Linguistics",
pages = "179--188",
location = "Vancouver, Canada",
doi = "10.18653/v1/P17-1017",
url = "http://www.aclweb.org/anthology/P17-1017"
}
```
The latest version 3.0:
```
@inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared,
title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)},
author={Castro Ferreira, Thiago and
Gardent, Claire and
Ilinykh, Nikolai and
van der Lee, Chris and
Mille, Simon and
Moussallem, Diego and
Shimorina, Anastasia},
booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)},
pages = "55--76",
year = 2020,
address = {Dublin, Ireland (Virtual)},
publisher = {Association for Computational Linguistics}}
```
#### 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]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
[Website](https://beng.dice-research.org/gerbil/)
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
The model outputs are evaluated against the crowdsourced references; the leaderboard reports BLEU-4, METEOR, chrF++, TER, BERTScore and BLEURT scores.
### 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 -->
`Russian`, `English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The WebNLG dataset was created to promote the development (_i_) of RDF verbalisers and (_ii_) of microplanners able to handle a wide range of linguistic constructions. The dataset aims at covering knowledge in different domains ("categories"). The same properties and entities can appear in several categories.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
A model should verbalize all and only the provided input triples in natural language.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Université de Lorraine / LORIA, France, CNRS / LORIA, France, University of Edinburgh, UK, Federal University of Minas Gerais, Brazil
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil).
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
The dataset construction was funded by the French National Research Agency (ANR).
#### 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 -->
Simon Mille and Sebastian Gehrmann added the dataset and wrote the data card.
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
See [official documentation](https://webnlg-challenge.loria.fr/docs/).
`entry`: a data instance of the benchmark. Each entry has five attributes: a DBpedia category (`category`), entry ID (`eid`), shape, shape type, and triple set size (`size`).
- `shape`: a string representation of the RDF tree with nested parentheses where `X` is a node (see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format)).
- `shape_type`: a type of the tree shape. We [identify](https://www.aclweb.org/anthology/C16-1141.pdf) three types of tree shapes:
* `chain` (the object of one triple is the subject of the other);
* `sibling` (triples with a shared subject);
* `mixed` (both `chain` and `sibling` types present).
- `eid`: an entry ID. It is unique only within a category and a size.
- `category`: a DBpedia category (Astronaut, City, MusicalWork, Politician, etc.).
- `size`: the number of RDF triples in a set. Ranges from 1 to 7.
Each `entry` has three fields: `originaltripleset`, `modifiedtripleset`, and `lexs`.
`originaltripleset`: a set of RDF triples as extracted from [DBpedia](https://wiki.dbpedia.org/). Each set of RDF triples is a tree. Triples have the subject-predicate-object structure.
`modifiedtripleset`: a set of RDF triples as presented to crowdworkers (for more details on modifications, see below).
Original and modified triples serve different purposes: the original triples — to link data to a knowledge base (DBpedia), whereas the modified triples — to ensure consistency and homogeneity throughout the data. To train models, the modified triples should be used.
`lexs` (shortened for lexicalisations): a natural language text verbalising the triples. Each lexicalisation has two attributes: a comment (`comment`), and a lexicalisation ID (`lid`). By default, comments have the value `good`, except rare cases when they were manually marked as `toFix`. That was done during the corpus creation, when it was seen that a lexicalisation did not exactly match a triple set.
Russian data has additional optional fields comparing to English:
`<dbpedialinks>`: RDF triples extracted from DBpedia between English and Russian entities by means of the property `sameAs`.
`<links>`: RDF triples created manually for some entities to serve as pointers to translators. There are two types of them:
* with `sameAs` (`Spaniards | sameAs | испанцы`)
* with `includes` (`Tomatoes, guanciale, cheese, olive oil | includes | гуанчиале`). Those were mostly created for string literals to translate some parts of them.
Lexicalisations in the Russian WebNLG have a new parameter `lang` (values: `en`, `ru`) because original English texts were kept in the Russian version (see the example above).
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"entry": {
"category": "Company",
"size": "4",
"shape": "(X (X) (X) (X) (X))",
"shape_type": "sibling",
"eid": "Id21",
"lexs": [
{
"comment": "good",
"lex": "Trane, which was founded on January 1st 1913 in La Crosse, Wisconsin, is based in Ireland. It has 29,000 employees.",
"lid": "Id1"
}
],
"modifiedtripleset": [
{
"subject": "Trane",
"property": "foundingDate",
"object": "1913-01-01"
},
{
"subject": "Trane",
"property": "location",
"object": "Ireland"
},
{
"subject": "Trane",
"property": "foundationPlace",
"object": "La_Crosse,_Wisconsin"
},
{
"subject": "Trane",
"property": "numberOfEmployees",
"object": "29000"
}
],
"originaltriplesets": {
"originaltripleset": [
{
"subject": "Trane",
"property": "foundingDate",
"object": "1913-01-01"
},
{
"subject": "Trane",
"property": "location",
"object": "Ireland"
},
{
"subject": "Trane",
"property": "foundationPlace",
"object": "La_Crosse,_Wisconsin"
},
{
"subject": "Trane",
"property": "numberOfEmployees",
"object": "29000"
}
]
}
}
}
```
The XML-formatted example is [here](https://webnlg-challenge.loria.fr/docs/#example).
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| English (v3.0) | Train | Dev | Test |
|-----------------|--------|-------|-------|
| **triple sets** | 13,211 | 1,667 | 1,779 |
| **texts** | 35,426 | 4,464 | 5,150 |
|**properties** | 372 | 290 | 220 |
| Russian (v3.0) | Train | Dev | Test |
|-----------------|--------|-------|-------|
| **triple sets** | 5,573 | 790 | 1,102 |
| **texts** | 14,239 | 2,026 | 2,780 |
|**properties** | 226 | 115 | 192 |
## 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 -->
Due to the constrained generation task, this dataset can be used to evaluate very specific and narrow generation capabilities.
#### 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 -->
The RDF-triple format is unique to WebNLG.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
surface 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 -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
No changes to the main content of the dataset. The [version 3.0](https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0) of the dataset is used.
#### 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 -->
23 special test sets for WebNLG were added to the GEM evaluation suite, 12 for English and 11 for Russian.
For both languages, we created subsets of the training and development sets of ~500 randomly selected inputs each. The inputs were sampled proportionally from each category.
Two types of transformations have been applied to WebNLG: (i) input scrambling (English and Russian) and (ii) numerical value replacements (English); in both cases, a subset of about 500 inputs was randomly selected. For (i), the order of the triples was randomly reassigned (each triple kept the same Subject-Property-Object internal order). For (ii), the change was performed respecting the format of the current cardinal value (e.g., alpha, integer, or floating-point) and replacing it with a new random value. The new number is lower-bounded between zero and upper bounded to be within to the highest power of 10 unit for the given value (e.g., replacing 54 would result in a random value between 0-100). Floating values maintain the degree of precision.
For both languages, we did identify different subsets of the test set that we could compare to each other so that we would have a better understanding of the results. There are currently 8 selections that we have made:
Selection 1 (size): input length. This selection corresponds to the number of predicates in the input. By comparing inputs of different lengths, we can see to what extent NLG systems are able to handle different input sizes. The table below provides the relevant frequencies. Please be aware that comparing selections with fewer than 100 items may result in unreliable comparisons.
| Input length | Frequency English | Frequency Russian |
|----------------|-------------------|-------------------|
| 1 | 369 | 254 |
| 2 | 349 | 200 |
| 3 | 350 | 214 |
| 4 | 305 | 214 |
| 5 | 213 | 159 |
| 6 | 114 | 32 |
| 7 | 79 | 29 |
Selection 2 (frequency): seen/unseen single predicates. This selection corresponds to the inputs with only one predicate. We compare which predicates are seen/unseen in the training data. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there is only one example of unseen single predicates.
| _ in training | Frequency English | Frequency Russian |
|---------------|-------------------|-------------------|
| Seen | 297 | 253 |
| Unseen | 72 | 1 |
Selection 3 (frequency): seen/unseen combinations of predicates. This selection checks for all combinations of predicates whether that combination has been seen in the training data. For example: if the combination of predicates A and B is seen, that means that there is an input in the training data consisting of two triples, where one triple uses predicate A and the other uses predicate B. If the combination is unseen, then the converse is true. The table below provides the relevant frequencies.
| _ in training | Frequency English | Frequency Russian |
|---------------|-------------------|-------------------|
| unseen | 1295 | 354 |
| seen | 115 | 494 |
Selection 4 (frequency): seen/unseen arguments. This selection checks for all input whether or not all arg1s and arg2s in the input have been seen during the training phase. For this selection, *Seen* is the default. Only if all arg1 instances for a particular input are unseen, do we count the arg1s of the input as unseen. The same holds for arg2. So "seen" here really means that at least some of the arg1s or arg2s are seen in the input. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there are very few examples of unseen combinations of predicates.
| Arguments seen in training? | Frequency English | Frequency Russian |
|-----------------------------|-------------------|-------------------|
| both_seen | 518 | 1075 |
| both_unseen | 1177 | 4 |
| arg1_unseen | 56 | 19 |
| arg2_unseen | 28 | 4 |
Selection 5 (shape): repeated subjects. For this selection, the subsets are based on the times a subject is repeated in the input; it only takes into account the maximum number of times a subject is repeated, that is, if in one input a subject appears 3 times and a different subject 2 times, this input will be in the "3_subjects_same' split. Unique_subjects means all subjects are different.
| Max num. of repeated subjects | Frequency English | Frequency Russian |
|-------------------------------|-------------------|-------------------|
| unique_subjects | 453 | 339 |
| 2_subjects_same | 414 | 316 |
| 3_subjects_same | 382 | 217 |
| 4_subjects_same | 251 | 143 |
| 5_subjects_same | 158 | 56 |
| 6_subjects_same | 80 | 19 |
| 7_subjects_same | 41 | 12 |
Selection 6 (shape): repeated objects. Same as for subjects above, but for objects. There are much less cases of repeated objects, so there are only two categories for this selection, unique_objects and some_objects_repeated; for the latter, we have up to 3 coreferring objects in English, and XXX in Russian.
| Max num. of repeated objects | Frequency English | Frequency Russian |
|------------------------------|-------------------|-------------------|
| unique_objects | 1654 | 1099 |
| some_objects_same | 125 | 3 |
Selection 7 (shape): repeated properties. Same as for objects above, but for properties; up to two properties can be the same in English, up to XXX in Russian.
| Max num. of repeated properties | Frequency English | Frequency Russian |
|---------------------------------|-------------------|-------------------|
| unique_properties | 1510 | 986 |
| some_properties_same | 269 | 116 |
Selection 8 (shape): entities that appear both as subject and object. For this selection, we grouped together the inputs in which no entity is found as both subject and object, and on the other side inputs in which one or more entity/ies appear both as subject and as object. We found up to two such entities per input in English, and up to XXX in Russian.
| Max num. of objects and subjects in common | Frequency English | Frequency Russian |
|--------------------------------------------|-------------------|-------------------|
| unique_properties | 1322 | 642 |
| some_properties_same | 457 | 460 |
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
Robustness
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
Dataset construction: [main dataset paper](https://www.aclweb.org/anthology/P17-1017/), [RDF triple extraction](https://www.aclweb.org/anthology/C16-1141/), [Russian translation](https://www.aclweb.org/anthology/W19-3706/)
WebNLG Challenge 2017: [webpage](https://webnlg-challenge.loria.fr/challenge_2017/), [paper](https://www.aclweb.org/anthology/W17-3518/)
WebNLG Challenge 2020: [webpage](https://webnlg-challenge.loria.fr/challenge_2020/), [paper](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf)
Enriched version of WebNLG: [repository](https://github.com/ThiagoCF05/webnlg), [paper](https://www.aclweb.org/anthology/W18-6521/)
Related research papers: [webpage](https://webnlg-challenge.loria.fr/research/)
## Previous Results
### Previous Results
#### 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 -->
For both languages, the participating systems are automatically evaluated in a multi-reference scenario. Each English hypothesis is compared to a maximum of 5 references, and each Russian one to a maximum of 7 references. On average, English data has 2.89 references per test instance, and Russian data has 2.52 references per instance.
In a human evaluation, example are uniformly sampled across size of triple sets and the following dimensions are assessed (on MTurk and Yandex.Toloka):
1. Data Coverage: Does the text include descriptions of all predicates presented in the data?
2. Relevance: Does the text describe only such predicates (with related subjects and objects), which are found in the data?
3. Correctness: When describing predicates which are found in the data, does the text mention correct the objects and adequately introduces the subject for this specific predicate?
4. Text Structure: Is the text grammatical, well-structured, written in acceptable English language?
5. Fluency: Is it possible to say that the text progresses naturally, forms a coherent whole and it is easy to understand the text?
For additional information like the instructions, we refer to the original paper.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
We evaluated a wide range of models as part of the GEM benchmark.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Results can be found on the [GEM website](https://gem-benchmark.com/results).
## 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 -->
yes - related tasks
#### Social Impact Observations
<!-- info: Did any of these previous uses result in observations about the social impact of the systems? In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here. -->
<!-- scope: microscope -->
We do not foresee any negative social impact in particular from this dataset or task.
Positive outlooks: Being able to generate good quality text from RDF data would permit, e.g., making this data more accessible to lay users, enriching existing text with information drawn from knowledge bases such as DBpedia or describing, comparing and relating entities present in these knowledge bases.
### 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 -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
This dataset is created using DBpedia RDF triples which naturally exhibit biases that have been found to exist in Wikipedia such as some forms of, e.g., gender bias.
The choice of [entities](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json), described by RDF trees, was not controlled. As such, they may contain gender biases; for instance, all the astronauts described by RDF triples are male. Hence, in texts, pronouns _he/him/his_ occur more often. Similarly, entities can be related to the Western culture more often than to other cultures.
#### 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 -->
In English, the dataset is limited to the language that crowdraters speak. In Russian, the language is heavily biased by the translationese of the translation system that is post-edited.
## 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 -->
There is no PII in this dataset.
### 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 -->
`non-commercial use only`
#### 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 quality of the crowdsourced references is limited, in particular in terms of fluency/naturalness of the collected texts.
Russian data was machine-translated and then post-edited by crowdworkers, so some examples may still exhibit issues related to bad translations.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Only a limited number of domains are covered in this dataset. As a result, it cannot be used as a general-purpose realizer.
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papluca/language-identification | papluca | 2022-07-15T10:11:23Z | 559 | 18 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:extended|amazon_reviews_multi",
"source_datasets:extended|xnli",
"source_datasets:extended|stsb_multi_mt",
"language:ar",
"language:bg",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:it",
"language:ja",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"language:sw",
"language:th",
"language:tr",
"language:ur",
"language:vi",
"language:zh",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators: []
language:
- ar
- bg
- de
- el
- en
- es
- fr
- hi
- it
- ja
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: []
multilinguality:
- multilingual
pretty_name: Language Identification dataset
size_categories:
- unknown
source_datasets:
- extended|amazon_reviews_multi
- extended|xnli
- extended|stsb_multi_mt
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for Language Identification dataset
## 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
The Language Identification dataset is a collection of 90k samples consisting of text passages and corresponding language label.
This dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt).
### Supported Tasks and Leaderboards
The dataset can be used to train a model for language identification, which is a **multi-class text classification** task.
The model [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection), which is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), was trained on this dataset and currently achieves 99.6% accuracy on the test set.
### Languages
The Language Identification dataset contains text in 20 languages, which are:
`arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)`
## Dataset Structure
### Data Instances
For each instance, there is a string for the text and a string for the label (the language tag). Here is an example:
`{'labels': 'fr', 'text': 'Conforme à la description, produit pratique.'}`
### Data Fields
- **labels:** a string indicating the language label.
- **text:** a string consisting of one or more sentences in one of the 20 languages listed above.
### Data Splits
The Language Identification dataset has 3 splits: *train*, *valid*, and *test*.
The train set contains 70k samples, while the validation and test sets 10k each.
All splits are perfectly balanced: the train set contains 3500 samples per language, while the validation and test sets 500.
## Dataset Creation
### Curation Rationale
This dataset was built during *The Hugging Face Course Community Event*, which took place in November 2021, with the goal of collecting a dataset with enough samples for each language to train a robust language detection model.
### Source Data
The Language Identification dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt).
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating (balanced) multi-class text classification models.
### Discussion of Biases
The possible biases correspond to those of the 3 datasets on which this dataset is based.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@LucaPapariello](https://github.com/LucaPapariello) for adding this dataset.
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techiaith/legislation-gov-uk_en-cy | techiaith | 2023-08-12T13:24:57Z | 559 | 1 | [
"task_categories:translation",
"task_categories:text-classification",
"task_categories:summarization",
"task_categories:sentence-similarity",
"size_categories:100M<n<1B",
"language:en",
"language:cy",
"license:other",
"region:us"
] | [
"translation",
"text-classification",
"summarization",
"sentence-similarity"
] | 2023-07-21T09:25:08Z | ---
license: other
task_categories:
- translation
- text-classification
- summarization
- sentence-similarity
language:
- en
- cy
pretty_name: UK Government Legislation
size_categories:
- 100M<n<1B
---
# Dataset Card for legislation-gov-uk-en-cy
## Dataset Description
- **Homepage:** https://github.com/techiaith/legislation-gov-uk_dataset
- **Repository:** https://github.com/techiaith/legislation-gov-uk_dataset
- **Point of Contact:** [email protected]
### Dataset Summary
This dataset consists of English-Welsh sentence pairs obtained via scraping the www.legislation.gov.uk website.
The total dataset is approximately 170 Mb in size.
### Supported Tasks and Leaderboards
- translation
- text-classification
- summarization
- sentence-similarity
### Languages
- English
- Welsh
## Dataset Structure
### Data Fields
- source
- target
### Data Splits
- train
## Dataset Creation
English-Welsh sentence pairs were obtained by scraping the www.legislation.gov.uk website and then cleaning the data using an internal processing pipeline.
### Source Data
#### Initial Data Collection and Normalization
Sentences were dropped from the original scrapped sources in the following cases:
- sentence contained too many misspelt words
- sentence length similarity variance too great.
#### Who are the source language producers?
The language data, including source and target language data, is derived from UK legislation. See [here](https://www.legislation.gov.uk/aboutus) for information.
### Licensing Information
This dataset's source data is Crown copyright and is licensed under the [Open Government License](https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/). | [
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silk-road/ChatHaruhi-from-RoleLLM | silk-road | 2023-10-20T12:27:24Z | 559 | 0 | [
"license:cc-by-4.0",
"region:us"
] | null | 2023-10-20T08:39:56Z | ---
license: cc-by-4.0
---
Adapt English Role in RoleBench into ChatHaruhi format
only using profiles part in [ZenMoore/RoleBench](https://huggingface.co/datasets/ZenMoore/RoleBench)
Great thanks to on authors of RoleLLM!
usage:
```python
# if you pip installed chatharuhi it should be
# from chatharuhi import ChatHaruhi
from ChatHaruhi import ChatHaruhi
chatbot = ChatHaruhi( role_from_hf = 'silk-road/ChatHaruhi-from-RoleLLM/Sherlock Holmes', \
llm = 'openai',
embedding = 'bge_en')
response = chatbot.chat(role='Police Chief', text = 'Oh no, there has been another murder')
print(response)
```
The output should like this:
```
Sherlock Holmes: "Ah, another murder case, you say? Fascinating. Pray, do provide me with the details, Constable."
```
if you chat with bot in English, embedding = 'bge_en'
if in Chinese, embedding = 'luotuo_openai' ( or default )
More detail information see our github https://github.com/LC1332/Chat-Haruhi-Suzumiya
95 Roles contains in this dataset:
Role | Movie | 中文 | How-to-call-in-ChatHaruhi
---|---|---|---
HAL 9000 | 2001-A-Space-Odyssey | 《2001太空漫游》中的HAL 9000电脑 | silk-road/ChatHaruhi-from-RoleLLM/HAL 9000
Colonel Nathan R. Jessep | A-Few-Good-Men | 《好汉两三个》中的内森·R·杰瑟普上校 | silk-road/ChatHaruhi-from-RoleLLM/Colonel Nathan R. Jessep
Antonio Salieri | Amadeus | 《阿玛迪斯》中的安东尼奥·萨列里 | silk-road/ChatHaruhi-from-RoleLLM/Antonio Salieri
Stifler | American-Pie | 《美国派》中的斯蒂夫勒 | silk-road/ChatHaruhi-from-RoleLLM/Stifler
Paul Vitti | Analyze-That | 《心理分析那件事》中的保罗·维蒂 | silk-road/ChatHaruhi-from-RoleLLM/Paul Vitti
Alvy Singer | Annie-Hall | 《安妮·霍尔》中的阿尔维·辛格 | silk-road/ChatHaruhi-from-RoleLLM/Alvy Singer
Violet Weston | August-Osage-County | 《奥塞奇郡的八月》中的紫罗兰·韦斯顿 | silk-road/ChatHaruhi-from-RoleLLM/Violet Weston
Willie Soke | Bad-Santa | 《坏圣诞老人》中的威利·索克 | silk-road/ChatHaruhi-from-RoleLLM/Willie Soke
Gaston | Beauty-and-the-Beast | 《美女与野兽》中的加斯顿 | silk-road/ChatHaruhi-from-RoleLLM/Gaston
The Dude | Big-Lebowski,-The | 《大勒布斯基》中的“大佬” | silk-road/ChatHaruhi-from-RoleLLM/The Dude
Murphy MacManus | Boondock-Saints,-The | 《天使之城》中的墨菲·麦克马纳斯 | silk-road/ChatHaruhi-from-RoleLLM/Murphy MacManus
Paul Conroy | Buried | 《活埋》中的保罗·康罗伊 | silk-road/ChatHaruhi-from-RoleLLM/Paul Conroy
Truman Capote | Capote | 《卡波特》中的杜鲁门·卡波特 | silk-road/ChatHaruhi-from-RoleLLM/Truman Capote
Mater | Cars-2 | 《赛车总动员2》中的玛特 | silk-road/ChatHaruhi-from-RoleLLM/Mater
Andrew Detmer | Chronicle | 《编年史》中的安德鲁·德特默 | silk-road/ChatHaruhi-from-RoleLLM/Andrew Detmer
Coriolanus | Coriolanus | 《科里奥兰纳斯》中的主角 | silk-road/ChatHaruhi-from-RoleLLM/Coriolanus
Benjamin Button | Curious-Case-of-Benjamin-Button,-The | 《本杰明·巴顿奇事》中的本杰明·巴顿 | silk-road/ChatHaruhi-from-RoleLLM/Benjamin Button
John Keating | Dead-Poets-Society | 《死亡诗社》中的约翰·基廷 | silk-road/ChatHaruhi-from-RoleLLM/John Keating
Wade Wilson | Deadpool | 《死侍》中的韦德·威尔逊 | silk-road/ChatHaruhi-from-RoleLLM/Wade Wilson
Jim Morrison | Doors,-The | 《门》中的吉姆·莫里森 | silk-road/ChatHaruhi-from-RoleLLM/Jim Morrison
Queen Elizabeth I | Elizabeth-The-Golden-Age | 《伊丽莎白：黄金时代》中的伊丽莎白一世女王 | silk-road/ChatHaruhi-from-RoleLLM/Queen Elizabeth I
Jeff Spicoli | Fast-Times-at-Ridgemont-High | 《瑞奇蒙特高中时光》中的杰夫·斯皮科利 | silk-road/ChatHaruhi-from-RoleLLM/Jeff Spicoli
Fred Flintstone | Flintstones,-The | 《石头家族》中的弗雷德·弗林斯通 | silk-road/ChatHaruhi-from-RoleLLM/Fred Flintstone
Freddy Krueger | Freddy-vs.-Jason | 《弗雷迪对杰森》中的弗雷迪·克鲁格 | silk-road/ChatHaruhi-from-RoleLLM/Freddy Krueger
Tyrion Lannister | Game_of_Thrones | 《权力的游戏》中的提利昂·兰尼斯特 | silk-road/ChatHaruhi-from-RoleLLM/Tyrion Lannister
James Brown | Get-on-Up | 《起身舞蹈》中的詹姆斯·布朗 | silk-road/ChatHaruhi-from-RoleLLM/James Brown
Walt Kowalski | Gran-Torino | 《老无所依》中的沃尔特·科瓦尔斯基 | silk-road/ChatHaruhi-from-RoleLLM/Walt Kowalski
John Coffey | Green-Mile,-The | 《绿里奇迹》中的约翰·科菲 | silk-road/ChatHaruhi-from-RoleLLM/John Coffey
Theodore Twombly | Her | 《她》中的西奥多·特温布利 | silk-road/ChatHaruhi-from-RoleLLM/Theodore Twombly
Gregory House | House-M.D. | 《豪斯医生》中的格雷戈里·豪斯 | silk-road/ChatHaruhi-from-RoleLLM/Gregory House
Sonny | I,-Robot | 《我，机器人》中的桑尼 | silk-road/ChatHaruhi-from-RoleLLM/Sonny
Colonel Hans Landa | Inglourious-Basterds | 《无耻混蛋》中的汉斯·兰达上校 | silk-road/ChatHaruhi-from-RoleLLM/Colonel Hans Landa
Judge Dredd | Judge-Dredd | 《德莱德法官》中的法官德莱德 | silk-road/ChatHaruhi-from-RoleLLM/Judge Dredd
Juno MacGuff | Juno | 《朱诺》中的朱诺·麦克夫 | silk-road/ChatHaruhi-from-RoleLLM/Juno MacGuff
Po | Kung-Fu-Panda | 《功夫熊猫》中的阿宝 | silk-road/ChatHaruhi-from-RoleLLM/Po
Professor G.H. Dorr | Ladykillers,-The | 《夫人杀手》中的G.H.多尔教授 | silk-road/ChatHaruhi-from-RoleLLM/Professor G.H. Dorr
Fletcher Reede | Liar-Liar | 《撒谎的男人》中的弗莱彻·里德 | silk-road/ChatHaruhi-from-RoleLLM/Fletcher Reede
Abraham Lincoln | Lincoln | 《林肯》中的亚伯拉罕·林肯 | silk-road/ChatHaruhi-from-RoleLLM/Abraham Lincoln
Frank T.J. Mackey | Magnolia | 《木兰花》中的弗兰克 T.J. 麦凯 | silk-road/ChatHaruhi-from-RoleLLM/Frank T.J. Mackey
Malcolm X | Malcolm-X | 《马尔科姆X》中的马尔科姆X | silk-road/ChatHaruhi-from-RoleLLM/Malcolm X
Leonard Shelby | Memento | 《记忆碎片》中的伦纳德·谢尔比 | silk-road/ChatHaruhi-from-RoleLLM/Leonard Shelby
Harvey Milk | Milk | 《牛奶》中的哈维·牛奶 | silk-road/ChatHaruhi-from-RoleLLM/Harvey Milk
Randle McMurphy | One-Flew-Over-the-Cuckoo's-Nest | 《飞越疯人院》中的兰德尔·麦克默菲 | silk-road/ChatHaruhi-from-RoleLLM/Randle McMurphy
Jack Sparrow | Pirates-of-the-Caribbean-Dead-Man's-Chest | 《加勒比海盗》中的杰克·斯派洛船长 | silk-road/ChatHaruhi-from-RoleLLM/Jack Sparrow
John Dillinger | Public-Enemies | 《公敌》中的约翰·迪林格 | silk-road/ChatHaruhi-from-RoleLLM/John Dillinger
Lestat de Lioncourt | Queen-of-the-Damned | 《诅咒女王》中的莱斯塔特·德·莱昂科特 | silk-road/ChatHaruhi-from-RoleLLM/Lestat de Lioncourt
Tyler Hawkins | Remember-Me | 《记得我》中的泰勒·霍金斯 | silk-road/ChatHaruhi-from-RoleLLM/Tyler Hawkins
Caesar | Rise-of-the-Planet-of-the-Apes | 《猩球崛起》中的凯撒 | silk-road/ChatHaruhi-from-RoleLLM/Caesar
Jack | Room | 《房间》中的杰克 | silk-road/ChatHaruhi-from-RoleLLM/Jack
James Carter | Rush-Hour-2 | 《尖峰时刻2》中的詹姆斯·卡特 | silk-road/ChatHaruhi-from-RoleLLM/James Carter
Jigsaw | Saw | 《电锯惊魂》中的拼图杀手 | silk-road/ChatHaruhi-from-RoleLLM/Jigsaw
John Doe | Se7en | 《七宗罪》中的约翰·多 | silk-road/ChatHaruhi-from-RoleLLM/John Doe
Jackie Moon | Semi-Pro | 《半职业球员》中的杰基·月亮 | silk-road/ChatHaruhi-from-RoleLLM/Jackie Moon
Sherlock Holmes | Sherlock-Holmes | 《夏洛克·福尔摩斯》中的夏洛克·福尔摩斯 | silk-road/ChatHaruhi-from-RoleLLM/Sherlock Holmes
Shrek | Shrek | 《史莱克》中的史莱克 | silk-road/ChatHaruhi-from-RoleLLM/Shrek
Pat Solitano | Silver-Linings-Playbook | 《乌云背后的幸福线》中的帕特·索利塔诺 | silk-road/ChatHaruhi-from-RoleLLM/Pat Solitano
Karl Childers | Sling-Blade | 《刀锯》中的卡尔·柴尔德斯 | silk-road/ChatHaruhi-from-RoleLLM/Karl Childers
Peter Parker | Spider-Man | 《蜘蛛侠》中的彼得·帕克 | silk-road/ChatHaruhi-from-RoleLLM/Peter Parker
Bruno Antony | Strangers-on-a-Train | 《列车上的陌生人》中的布鲁诺·安东尼 | silk-road/ChatHaruhi-from-RoleLLM/Bruno Antony
Seth | Superbad | 《超级糟糕》中的塞思 | silk-road/ChatHaruhi-from-RoleLLM/Seth
Caden Cotard | Synecdoche,-New-York | 《纽约奇缘》中的卡登·科塔德 | silk-road/ChatHaruhi-from-RoleLLM/Caden Cotard
Travis Bickle | Taxi-Driver | 《出租车司机》中的特拉维斯·比克尔 | silk-road/ChatHaruhi-from-RoleLLM/Travis Bickle
Stanley Ipkiss | Mask,-The | 《面具》中的斯坦利·伊普基斯 | silk-road/ChatHaruhi-from-RoleLLM/Stanley Ipkiss
Lyn Cassady | Men-Who-Stare-at-Goats,-The | 《盯羊的男人》中的林恩·卡萨迪 | silk-road/ChatHaruhi-from-RoleLLM/Lyn Cassady
Michael Scott | The_Office | 《办公室》中的迈克尔·斯科特 | silk-road/ChatHaruhi-from-RoleLLM/Michael Scott
Robert Angier | Prestige,-The | 《名望》中的罗伯特·安吉尔 | silk-road/ChatHaruhi-from-RoleLLM/Robert Angier
Rachel Lang | The-Rage-Carrie-2 | 《瑞秋的愤怒：凯丽2》中的瑞秋·朗 | silk-road/ChatHaruhi-from-RoleLLM/Rachel Lang
Dr. Frank-N-Furter | Rocky-Horror-Picture-Show,-The | 《洛奇恐怖秀》中的弗兰克·N·福特医生 | silk-road/ChatHaruhi-from-RoleLLM/Dr. Frank-N-Furter
Jack Torrance | Shining,-The | 《闪灵》中的杰克·托兰斯 | silk-road/ChatHaruhi-from-RoleLLM/Jack Torrance
Tom Ripley | Talented-Mr.-Ripley,-The | 《天才雷普利》中的汤姆·雷普利 | silk-road/ChatHaruhi-from-RoleLLM/Tom Ripley
D_Artagnan | Three-Musketeers,-The | 《三剑客》中的达达尼昂 | silk-road/ChatHaruhi-from-RoleLLM/D_Artagnan
Stephen Hawking | Theory-of-Everything,-The | 《万物理论》中的斯蒂芬·霍金 | silk-road/ChatHaruhi-from-RoleLLM/Stephen Hawking
Thor | Thor-Ragnarok | 《雷神：诸神黄昏》中的雷神索尔 | silk-road/ChatHaruhi-from-RoleLLM/Thor
James Bond | Tomorrow-Never-Dies | 《明日帝国》中的詹姆斯·邦德 | silk-road/ChatHaruhi-from-RoleLLM/James Bond
Mark Renton | Trainspotting | 《迷幻列车》中的马克·伦顿 | silk-road/ChatHaruhi-from-RoleLLM/Mark Renton
Tugg Speedman | Tropic-Thunder | 《热带惊雷》中的塔格·斯皮德曼 | silk-road/ChatHaruhi-from-RoleLLM/Tugg Speedman
David Aames | Vanilla-Sky | 《香草天空》中的大卫·艾姆斯 | silk-road/ChatHaruhi-from-RoleLLM/David Aames
Rorschach | Watchmen | 《守望者》中的罗夏克 | silk-road/ChatHaruhi-from-RoleLLM/Rorschach
Jordan Belfort | Wolf-of-Wall-Street,-The | 《华尔街之狼》中的乔丹·贝尔福特 | silk-road/ChatHaruhi-from-RoleLLM/Jordan Belfort
Logan | X-Men-Origins-Wolverine | 《X战警：金刚狼》中的洛根 | silk-road/ChatHaruhi-from-RoleLLM/Logan
Judy Hoops | Zootopia | 《疯狂动物城》中的朱迪·胡普斯 | silk-road/ChatHaruhi-from-RoleLLM/Judy Hoops
Doctor Who | Doctor_Who | 《神秘博士》中的博士 | silk-road/ChatHaruhi-from-RoleLLM/Doctor Who
Blair Waldorf | Gossip_Girl | 《绯闻女孩》中的布莱尔·沃尔多夫 | silk-road/ChatHaruhi-from-RoleLLM/Blair Waldorf
Raylan Givens | Justified | 《正当防卫》中的雷兰·吉文斯 | silk-road/ChatHaruhi-from-RoleLLM/Raylan Givens
Mary Sibley | Salem | 《塞勒姆》中的玛丽·西布利 | silk-road/ChatHaruhi-from-RoleLLM/Mary Sibley
Lucifer Morningstar | Lucifer | 《路西法》中的路西法·晨星 | silk-road/ChatHaruhi-from-RoleLLM/Lucifer Morningstar
Sheldon Cooper | The_Big_Bang_Theory | 《生活大爆炸》中的谢尔顿·库珀 | silk-road/ChatHaruhi-from-RoleLLM/Sheldon Cooper
Twilight Sparkle | My_Little_Pony__Friendship_is_Magic | 《我的小马驹：友谊之魔》中的暮光星辉 | silk-road/ChatHaruhi-from-RoleLLM/Twilight Sparkle
Oliver Queen | Arrow | 《绿箭侠》中的奥利弗·皇后 | silk-road/ChatHaruhi-from-RoleLLM/Oliver Queen
Leroy Jethro Gibbs | NCIS | 《海军罪案调查处》中的利洛伊·杰斯罗·吉布斯 | silk-road/ChatHaruhi-from-RoleLLM/Leroy Jethro Gibbs
Angel | Angel | 《天使》中的天使 | silk-road/ChatHaruhi-from-RoleLLM/Angel
Klaus Mikaelson | The_Originals | 《始祖家族》中的克劳斯·米卡尔森 | silk-road/ChatHaruhi-from-RoleLLM/Klaus Mikaelson
Queen Catherine | Reign | 《王权》中的凯瑟琳女王 | silk-road/ChatHaruhi-from-RoleLLM/Queen Catherine
Dr. Hannibal Lecter | Hannibal | 《汉尼拔》中的汉尼拔·莱克特医生 | silk-road/ChatHaruhi-from-RoleLLM/Dr. Hannibal Lecter
Coach Eric Taylor | Friday_Night_Lights | 《星期五之光》中的教练埃里克·泰勒 | silk-road/ChatHaruhi-from-RoleLLM/Coach Eric Taylor
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] |
lbox/lbox_open | lbox | 2022-11-09T06:41:26Z | 558 | 4 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2022-03-02T23:29:22Z | ---
license: cc-by-nc-4.0
---
# Dataset Card for `lbox_open`
## Dataset Description
- **Homepage:** `https://lbox.kr`
- **Repository:** `https://github.com/lbox-kr/lbox_open`
- **Point of Contact:** [Wonseok Hwang](mailto:[email protected])
### Dataset Summary
A Legal AI Benchmark Dataset from Korean Legal Cases.
### Languages
Korean
### How to use
```python
from datasets import load_dataset
# casename classficiation task
data_cn = load_dataset("lbox/lbox_open", "casename_classification")
data_cn_plus = load_dataset("lbox/lbox_open", "casename_classification_plus")
# statutes classification task
data_st = load_dataset("lbox/lbox_open", "statute_classification")
data_st_plus = load_dataset("lbox/lbox_open", "statute_classification_plus")
# Legal judgement prediction tasks
data_ljp_criminal = load_dataset("lbox/lbox_open", "ljp_criminal")
data_ljp_civil = load_dataset("lbox/lbox_open", "ljp_civil")
# case summarization task
data_summ = load_dataset("lbox/lbox_open", "summarization")
data_summ_plus = load_dataset("lbox/lbox_open", "summarization_plus")
# precedent corpus
data_corpus = load_dataset("lbox/lbox_open", "precedent_corpus")
```
For more information about the dataset, please visit <https://github.com/lbox-kr/lbox_open>.
## Licensing Information
Copyright 2022-present [LBox Co. Ltd.](https://lbox.kr/)
Licensed under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) | [
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codeparrot/codeparrot-clean | codeparrot | 2022-10-10T15:23:51Z | 558 | 36 | [
"python",
"code",
"region:us"
] | null | 2022-03-02T23:29:22Z | ---
tags:
- python
- code
---
# CodeParrot 🦜 Dataset Cleaned
## What is it?
A dataset of Python files from Github. This is the deduplicated version of the [codeparrot](https://huggingface.co/datasets/transformersbook/codeparrot).
## Processing
The original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps:
- Deduplication
- Remove exact matches
- Filtering
- Average line length < 100
- Maximum line length < 1000
- Alpha numeric characters fraction > 0.25
- Remove auto-generated files (keyword search)
For more details see the preprocessing script in the transformers repository [here](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot).
## Splits
The dataset is split in a [train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train) and [validation](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid) split used for training and evaluation.
## Structure
This dataset has ~50GB of code and 5361373 files.
```python
DatasetDict({
train: Dataset({
features: ['repo_name', 'path', 'copies', 'size', 'content', 'license', 'hash', 'line_mean', 'line_max', 'alpha_frac', 'autogenerated'],
num_rows: 5361373
})
})
``` | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v3-math-468e93-2011366582 | autoevaluate | 2022-11-07T07:45:13Z | 558 | 0 | [
"autotrain",
"evaluation",
"region:us"
] | null | 2022-11-07T06:34:19Z | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_cot_v3
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-30b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v3
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v3
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-30b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v3
* Config: mathemakitten--winobias_antistereotype_test_cot_v3
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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pec | null | 2023-06-01T14:59:50Z | 556 | 4 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_ids:dialogue-modeling",
"task_ids:utterance-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | [
"text-generation",
"fill-mask",
"text-retrieval"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-retrieval
task_ids:
- dialogue-modeling
- utterance-retrieval
paperswithcode_id: pec
pretty_name: Persona-Based Empathetic Conversational
dataset_info:
- config_name: happy
features:
- name: personas
sequence: string
- name: context
sequence: string
- name: context_speakers
sequence: string
- name: response
dtype: string
- name: response_speaker
dtype: string
splits:
- name: train
num_bytes: 643196978
num_examples: 157195
- name: test
num_bytes: 92003042
num_examples: 22730
- name: validation
num_bytes: 81132088
num_examples: 19829
download_size: 252434681
dataset_size: 816332108
- config_name: offmychest
features:
- name: personas
sequence: string
- name: context
sequence: string
- name: context_speakers
sequence: string
- name: response
dtype: string
- name: response_speaker
dtype: string
splits:
- name: train
num_bytes: 518616402
num_examples: 123968
- name: test
num_bytes: 64173390
num_examples: 15324
- name: validation
num_bytes: 66675909
num_examples: 16004
download_size: 252434681
dataset_size: 649465701
- config_name: all
features:
- name: personas
sequence: string
- name: context
sequence: string
- name: context_speakers
sequence: string
- name: response
dtype: string
- name: response_speaker
dtype: string
splits:
- name: train
num_bytes: 1162655628
num_examples: 281163
- name: test
num_bytes: 156310498
num_examples: 38054
- name: validation
num_bytes: 147940164
num_examples: 35833
download_size: 252434681
dataset_size: 1466906290
config_names:
- all
- happy
- offmychest
---
# Dataset Card for PEC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [PEC repository](https://github.com/zhongpeixiang/PEC)
- **Paper:** [Towards Persona-Based Empathetic Conversational Models](https://www.aclweb.org/anthology/2020.emnlp-main.531/)
- **Point of Contact:** [Peixiang Zhong](mailto:[email protected])
### Dataset Summary
The PEC dataset is an English-language dataset of open-domain conversations gathered from two subreddits on Reddit, i.e., happy and offmychest. PEC has around 350K persona-based empathetic conversations. Each utterance is associated with a speaker, and each speaker has a persona of multiple persona sentences. The conversations in PEC are more empathetic than casual conversations. The conversations in the happy domain are mostly positive, whereas the conversations in the offmychest domain are mostly negative.
### Supported Tasks and Leaderboards
- `dialogue-modeling`, `utterance-retrieval`: this dataset can be used to train a generative or retrieval-based conversational model.
### Languages
English
## Dataset Structure
### Data Instances
A typical data example comprises a list of context utterances, a list of context speakers, a response to the context, the response speaker and the persona of the response speaker.
An example from PEC looks as follows:
```
{'context': ['found out this morning i got a job promotion ! ! !'],
'context_speakers': ['HeWentToJared91'],
'personas': [
"i ca n't stand working in the ugli .",
'i ’ve always liked my eyes except for the fact that they ca n’t shoot lasers',
'i feel really bad about myself as a person right now , and i could really use a hand .',
'i drank a coffee , and it just made me feel even more exhausted .',
'i want a natsuki t shirt',
"i 've dealt with depression in the past .",
'i love red dead 2'],
'response': "you look like a nice person ! we 're proud of you , and i bet you earned that promotion !",
'response_speaker': 'tylock'}
```
### Data Fields
- `context`: a list of strings, each string denotes a context utterance.
- `context_speakers`: a list of strings, each string denotes a speaker.
- `response`: a string denoting the response to the `context`.
- `response_speaker`: a string denoting the speaker of `response`.
- `personas`: a list of strings, each string denotes a persona sentence of `response_speaker`.
### Data Splits
The data is split into a training, validation and test set for each of the three domains. Note that the *all* domain is the concatenation of the *happy* and *offmychest* domains.
| domain | train | validation | test |
|------------|-------:|-----------:|------:|
| happy | 157195 | 19829 | 22730 |
| offmychest | 123968 | 16004 | 15324 |
| all | 281163 | 35833 | 38054 |
## Dataset Creation
### Curation Rationale
PEC was built to provide a testbed for machines to learn persona-based empathetic responding. In our empirical analysis, we found that different personas have different styles of empathetic responding. This dataset can also be used to investigate the link between persona and empathy in human conversations. According to our human assessment, the conversations on the happy and offmychest subreddits are significantly more empathetic than casual conversations.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained via the [pushshift API](https://pushshift.io/using-bigquery-with-reddit-data/) via Google BigQuery.
#### Who are the source language producers?
The language producers are users of the [r/happy](https://www.reddit.com/r/happy/), and [r/offmychest](https://www.reddit.com/r/offmychest/) subreddits between 2012 and 2020. No further demographic information was available from the data source.
### Annotations
#### Annotation process
The dataset does not contain any additional annotations.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset includes the speaker IDs of users on *happy* and *offmychest* subreddits.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop more personalised and empathetic conversational systems, which is an important milestone towards truly human-like conversational agents.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
A small portion of the dataset has the issues of sexism, hate, and harassment. The persona sentences are noisy.
## Additional Information
### Dataset Curators
The dataset was initially created by Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu, and Chunyan Miao, jointly done at Nanyang Technological University and Alibaba Group.
### Licensing Information
The licensing status of the dataset hinges on the legal status of the [Pushshift.io](https://files.pushshift.io/reddit/) data which is unclear.
### Citation Information
```
@inproceedings{zhong-etal-2020-towards,
title = "Towards Persona-Based Empathetic Conversational Models",
author = "Zhong, Peixiang and
Zhang, Chen and
Wang, Hao and
Liu, Yong and
Miao, Chunyan",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.531",
pages = "6556--6566"
}
```
### Contributions
Thanks to [@zhongpeixiang](https://github.com/zhongpeixiang) for adding this dataset. | [
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bigscience/xP3mt | bigscience | 2023-05-30T15:50:57Z | 556 | 18 | [
"task_categories:other",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100M<n<1B",
"language:ak",
"language:ar",
"language:as",
"language:bm",
"language:bn",
"language:ca",
"language:code",
"language:en",
"language:es",
"language:eu",
"language:fon",
"language:fr",
"language:gu",
"language:hi",
"language:id",
"language:ig",
"language:ki",
"language:kn",
"language:lg",
"language:ln",
"language:ml",
"language:mr",
"language:ne",
"language:nso",
"language:ny",
"language:or",
"language:pa",
"language:pt",
"language:rn",
"language:rw",
"language:sn",
"language:st",
"language:sw",
"language:ta",
"language:te",
"language:tn",
"language:ts",
"language:tum",
"language:tw",
"language:ur",
"language:vi",
"language:wo",
"language:xh",
"language:yo",
"language:zh",
"language:zu",
"license:apache-2.0",
"arxiv:2211.01786",
"region:us"
] | [
"other"
] | 2022-09-28T12:36:00Z | ---
annotations_creators:
- expert-generated
- crowdsourced
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
license:
- apache-2.0
multilinguality:
- multilingual
pretty_name: xP3
size_categories:
- 100M<n<1B
task_categories:
- other
---
# Dataset Card for xP3
## 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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigscience-workshop/xmtf
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
> xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot.
- **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility.
- **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3))
- **xP3 Dataset Family:**
<table>
<tr>
<th>Name</th>
<th>Explanation</th>
<th>Example models</th>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t>
<td>Mixture of 17 tasks in 277 languages with English prompts</td>
<td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t>
<td>Mixture of 13 training tasks in 46 languages with English prompts</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t>
<td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t>
<td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td>
<td></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t>
<td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
<tr>
<td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t>
<td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
</tr>
</table>
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?",
"targets": "Sí"
}
```
### Data Fields
The data fields are the same among all splits:
- `inputs`: the natural language input fed to the model
- `targets`: the natural language target that the model has to generate
### Data Splits
The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3).
|Language|Kilobytes|%|Samples|%|Non-English prompts|
|--------|------:|-:|---:|-:|-:|
|tw|106288|0.11|265071|0.33| |
|bm|107056|0.11|265180|0.33| |
|ak|108096|0.11|265071|0.33| |
|ca|110608|0.11|271191|0.34| |
|eu|113008|0.12|281199|0.35| |
|fon|113072|0.12|265063|0.33| |
|st|114080|0.12|265063|0.33| |
|ki|115040|0.12|265180|0.33| |
|tum|116032|0.12|265063|0.33| |
|wo|122560|0.13|365063|0.46| |
|ln|126304|0.13|365060|0.46| |
|as|156256|0.16|265063|0.33| |
|or|161472|0.17|265063|0.33| |
|kn|165456|0.17|265063|0.33| |
|ml|175040|0.18|265864|0.33| |
|rn|192992|0.2|318189|0.4| |
|nso|229712|0.24|915051|1.14| |
|tn|235536|0.24|915054|1.14| |
|lg|235936|0.24|915021|1.14| |
|rw|249360|0.26|915043|1.14| |
|ts|250256|0.26|915044|1.14| |
|sn|252496|0.26|865056|1.08| |
|xh|254672|0.26|915058|1.14| |
|zu|263712|0.27|915061|1.14| |
|ny|272128|0.28|915063|1.14| |
|ig|325440|0.33|950097|1.19|✅|
|yo|339664|0.35|913021|1.14|✅|
|ne|398144|0.41|315754|0.39|✅|
|pa|529632|0.55|339210|0.42|✅|
|sw|561392|0.58|1114439|1.39|✅|
|gu|566576|0.58|347499|0.43|✅|
|mr|674000|0.69|417269|0.52|✅|
|bn|854864|0.88|428725|0.54|✅|
|ta|943440|0.97|410633|0.51|✅|
|te|1384016|1.42|573354|0.72|✅|
|ur|1944416|2.0|855756|1.07|✅|
|vi|3113184|3.2|1667306|2.08|✅|
|code|4330752|4.46|2707724|3.38| |
|hi|4469712|4.6|1543441|1.93|✅|
|id|4538768|4.67|2582272|3.22|✅|
|zh|4604112|4.74|3571636|4.46|✅|
|ar|4703968|4.84|2148970|2.68|✅|
|fr|5558912|5.72|5055942|6.31|✅|
|pt|6130016|6.31|3562772|4.45|✅|
|es|7579424|7.8|5151349|6.43|✅|
|en|39252528|40.4|32740750|40.87| |
|total|97150128|100.0|80100816|100.0|✅|
## Dataset Creation
### Source Data
#### Training datasets
- Code Miscellaneous
- [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex)
- [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus)
- [GreatCode](https://huggingface.co/datasets/great_code)
- [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes)
- Closed-book QA
- [Hotpot QA](https://huggingface.co/datasets/hotpot_qa)
- [Trivia QA](https://huggingface.co/datasets/trivia_qa)
- [Web Questions](https://huggingface.co/datasets/web_questions)
- [Wiki QA](https://huggingface.co/datasets/wiki_qa)
- Extractive QA
- [Adversarial QA](https://huggingface.co/datasets/adversarial_qa)
- [CMRC2018](https://huggingface.co/datasets/cmrc2018)
- [DRCD](https://huggingface.co/datasets/clue)
- [DuoRC](https://huggingface.co/datasets/duorc)
- [MLQA](https://huggingface.co/datasets/mlqa)
- [Quoref](https://huggingface.co/datasets/quoref)
- [ReCoRD](https://huggingface.co/datasets/super_glue)
- [ROPES](https://huggingface.co/datasets/ropes)
- [SQuAD v2](https://huggingface.co/datasets/squad_v2)
- [xQuAD](https://huggingface.co/datasets/xquad)
- TyDI QA
- [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary)
- [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp)
- Multiple-Choice QA
- [ARC](https://huggingface.co/datasets/ai2_arc)
- [C3](https://huggingface.co/datasets/c3)
- [CoS-E](https://huggingface.co/datasets/cos_e)
- [Cosmos](https://huggingface.co/datasets/cosmos)
- [DREAM](https://huggingface.co/datasets/dream)
- [MultiRC](https://huggingface.co/datasets/super_glue)
- [OpenBookQA](https://huggingface.co/datasets/openbookqa)
- [PiQA](https://huggingface.co/datasets/piqa)
- [QUAIL](https://huggingface.co/datasets/quail)
- [QuaRel](https://huggingface.co/datasets/quarel)
- [QuaRTz](https://huggingface.co/datasets/quartz)
- [QASC](https://huggingface.co/datasets/qasc)
- [RACE](https://huggingface.co/datasets/race)
- [SciQ](https://huggingface.co/datasets/sciq)
- [Social IQA](https://huggingface.co/datasets/social_i_qa)
- [Wiki Hop](https://huggingface.co/datasets/wiki_hop)
- [WiQA](https://huggingface.co/datasets/wiqa)
- Paraphrase Identification
- [MRPC](https://huggingface.co/datasets/super_glue)
- [PAWS](https://huggingface.co/datasets/paws)
- [PAWS-X](https://huggingface.co/datasets/paws-x)
- [QQP](https://huggingface.co/datasets/qqp)
- Program Synthesis
- [APPS](https://huggingface.co/datasets/codeparrot/apps)
- [CodeContests](https://huggingface.co/datasets/teven/code_contests)
- [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs)
- [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp)
- [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search)
- [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code)
- Structure-to-text
- [Common Gen](https://huggingface.co/datasets/common_gen)
- [Wiki Bio](https://huggingface.co/datasets/wiki_bio)
- Sentiment
- [Amazon](https://huggingface.co/datasets/amazon_polarity)
- [App Reviews](https://huggingface.co/datasets/app_reviews)
- [IMDB](https://huggingface.co/datasets/imdb)
- [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes)
- [Yelp](https://huggingface.co/datasets/yelp_review_full)
- Simplification
- [BiSECT](https://huggingface.co/datasets/GEM/BiSECT)
- Summarization
- [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail)
- [Gigaword](https://huggingface.co/datasets/gigaword)
- [MultiNews](https://huggingface.co/datasets/multi_news)
- [SamSum](https://huggingface.co/datasets/samsum)
- [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua)
- [XLSum](https://huggingface.co/datasets/GEM/xlsum)
- [XSum](https://huggingface.co/datasets/xsum)
- Topic Classification
- [AG News](https://huggingface.co/datasets/ag_news)
- [DBPedia](https://huggingface.co/datasets/dbpedia_14)
- [TNEWS](https://huggingface.co/datasets/clue)
- [TREC](https://huggingface.co/datasets/trec)
- [CSL](https://huggingface.co/datasets/clue)
- Translation
- [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200)
- [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt)
- Word Sense disambiguation
- [WiC](https://huggingface.co/datasets/super_glue)
- [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic)
#### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval)
- Natural Language Inference (NLI)
- [ANLI](https://huggingface.co/datasets/anli)
- [CB](https://huggingface.co/datasets/super_glue)
- [RTE](https://huggingface.co/datasets/super_glue)
- [XNLI](https://huggingface.co/datasets/xnli)
- Coreference Resolution
- [Winogrande](https://huggingface.co/datasets/winogrande)
- [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd)
- Program Synthesis
- [HumanEval](https://huggingface.co/datasets/openai_humaneval)
- Sentence Completion
- [COPA](https://huggingface.co/datasets/super_glue)
- [Story Cloze](https://huggingface.co/datasets/story_cloze)
- [XCOPA](https://huggingface.co/datasets/xcopa)
- [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze)
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0.
### Citation Information
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset. | [
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art | null | 2023-04-05T09:36:25Z | 555 | 3 | [
"task_categories:multiple-choice",
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"abductive-natural-language-inference",
"arxiv:1908.05739",
"region:us"
] | [
"multiple-choice",
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- multiple-choice
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: art-dataset
pretty_name: Abductive Reasoning in narrative Text
tags:
- abductive-natural-language-inference
dataset_info:
features:
- name: observation_1
dtype: string
- name: observation_2
dtype: string
- name: hypothesis_1
dtype: string
- name: hypothesis_2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
config_name: anli
splits:
- name: validation
num_bytes: 312314
num_examples: 1532
- name: train
num_bytes: 34046304
num_examples: 169654
download_size: 5118294
dataset_size: 34358618
---
# Dataset Card for "art"
## 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://leaderboard.allenai.org/anli/submissions/get-started](https://leaderboard.allenai.org/anli/submissions/get-started)
- **Repository:** https://github.com/allenai/abductive-commonsense-reasoning
- **Paper:** [Abductive Commonsense Reasoning](https://arxiv.org/abs/1908.05739)
- **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:** 5.12 MB
- **Size of the generated dataset:** 34.36 MB
- **Total amount of disk used:** 39.48 MB
### Dataset Summary
ART consists of over 20k commonsense narrative contexts and 200k explanations.
The Abductive Natural Language Inference Dataset from AI2.
### 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
#### anli
- **Size of downloaded dataset files:** 5.12 MB
- **Size of the generated dataset:** 34.36 MB
- **Total amount of disk used:** 39.48 MB
An example of 'train' looks as follows.
```
{
"hypothesis_1": "Chad's car had all sorts of other problems besides alignment.",
"hypothesis_2": "Chad's car had all sorts of benefits other than being sexy.",
"label": 1,
"observation_1": "Chad went to get the wheel alignment measured on his car.",
"observation_2": "The mechanic provided a working alignment with new body work."
}
```
### Data Fields
The data fields are the same among all splits.
#### anli
- `observation_1`: a `string` feature.
- `observation_2`: a `string` feature.
- `hypothesis_1`: a `string` feature.
- `hypothesis_2`: a `string` feature.
- `label`: a classification label, with possible values including `0` (0), `1` (1), `2` (2).
### Data Splits
|name|train |validation|
|----|-----:|---------:|
|anli|169654| 1532|
## 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{Bhagavatula2020Abductive,
title={Abductive Commonsense Reasoning},
author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Byg1v1HKDB}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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] |
bigbio/chemprot | bigbio | 2022-12-22T15:44:22Z | 555 | 1 | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:07:50Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0
pretty_name: ChemProt
homepage: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for ChemProt
## Dataset Description
- **Homepage:** https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE,NER
The BioCreative VI Chemical-Protein interaction dataset identifies entities of
chemicals and proteins and their likely relation to one other. Compounds are
generally agonists (activators) or antagonists (inhibitors) of proteins.
## Citation Information
```
@article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Krallinger, M., Rabal, O., Lourenço, A.},
title = {Overview of the BioCreative VI chemical-protein interaction Track},
journal = {Proceedings of the BioCreative VI Workshop,},
volume = {141-146},
year = {2017},
url = {https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vi/track-5/},
doi = {},
biburl = {},
bibsource = {}
}
```
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text2log | null | 2022-11-03T16:15:15Z | 554 | 4 | [
"task_categories:translation",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: text2log
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
dataset_info:
features:
- name: sentence
dtype: string
- name: fol_translation
dtype: string
splits:
- name: train
num_bytes: 10358134
num_examples: 101931
download_size: 9746473
dataset_size: 10358134
---
# Dataset Card for text2log
## 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:**
- **Repository:** [GitHub](https://github.com/alevkov/text2log)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://github.com/alevkov
### Dataset Summary
The dataset contains 100,000 simple English sentences selected and filtered from `enTenTen15` and their translation into First Order Logic (FOL) using `ccg2lambda`.
### Supported Tasks and Leaderboards
'semantic-parsing': The data set is used to train models which can generate FOL statements from natural language text
### Languages
en-US
## Dataset Structure
### Data Instances
```
{
'clean':'All things that are new are good.',
'trans':'all x1.(_thing(x1) -> (_new(x1) -> _good(x1)))'
}
```
### Data Fields
- 'clean': a simple English sentence
- 'trans': the corresponding translation into Lambda Dependency-based Compositional Semantics
### Data Splits
No predefined train/test split is given. The authors used a 80/20 split
## Dataset Creation
### Curation Rationale
The text2log data set is used to improve FOL statement generation from natural text
### Source Data
#### Initial Data Collection and Normalization
Short text samples selected from enTenTen15
#### Who are the source language producers?
See https://www.sketchengine.eu/ententen-english-corpus/
### Annotations
#### Annotation process
Machine generated using https://github.com/mynlp/ccg2lambda
#### Who are the annotators?
none
### Personal and Sensitive Information
The dataset does not contain personal or sensitive 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
None given
### Citation Information
```bibtex
@INPROCEEDINGS{9401852,
author={Levkovskyi, Oleksii and Li, Wei},
booktitle={SoutheastCon 2021},
title={Generating Predicate Logic Expressions from Natural Language},
year={2021},
volume={},
number={},
pages={1-8},
doi={10.1109/SoutheastCon45413.2021.9401852}
}
```
### Contributions
Thanks to [@apergo-ai](https://github.com/apergo-ai) for adding this dataset. | [
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bbc_hindi_nli | null | 2023-01-25T14:27:06Z | 552 | 0 | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|bbc__hindi_news_classification",
"language:hi",
"license:mit",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- hi
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|bbc__hindi_news_classification
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: BBC Hindi NLI Dataset
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': not-entailment
'1': entailment
- name: topic
dtype:
class_label:
names:
'0': india
'1': news
'2': international
'3': entertainment
'4': sport
'5': science
config_name: bbc hindi nli
splits:
- name: train
num_bytes: 2990080
num_examples: 15552
- name: validation
num_bytes: 496808
num_examples: 2580
- name: test
num_bytes: 494432
num_examples: 2592
download_size: 3815652
dataset_size: 3981320
---
# Dataset Card for BBC Hindi NLI Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/midas-research/hindi-nli-data)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71)
- **Point of Contact:** [GitHub](https://github.com/midas-research/hindi-nli-data)
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to [email protected].
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset. | [
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med_hop | null | 2022-11-03T16:16:32Z | 552 | 3 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"multi-hop",
"arxiv:1710.06481",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: medhop
pretty_name: MedHop
tags:
- multi-hop
dataset_info:
- config_name: original
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: candidates
sequence: string
- name: supports
sequence: string
splits:
- name: train
num_bytes: 93937322
num_examples: 1620
- name: validation
num_bytes: 16461640
num_examples: 342
download_size: 339843061
dataset_size: 110398962
- config_name: masked
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: candidates
sequence: string
- name: supports
sequence: string
splits:
- name: train
num_bytes: 95813584
num_examples: 1620
- name: validation
num_bytes: 16800570
num_examples: 342
download_size: 339843061
dataset_size: 112614154
---
# Dataset Card for MedHop
## 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:** [QAngaroo](http://qangaroo.cs.ucl.ac.uk/)
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://arxiv.org/abs/1710.06481)
- **Leaderboard:** [leaderboard](http://qangaroo.cs.ucl.ac.uk/leaderboard.html)
- **Point of Contact:** [Johannes Welbl]([email protected])
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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JonasGeiping/the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020 | JonasGeiping | 2023-06-13T16:25:54Z | 551 | 0 | [
"arxiv:2212.14034",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | null | 2023-06-08T17:30:55Z | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 43860000000
num_examples: 85000000
download_size: 24001057282
dataset_size: 43860000000
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
pretty_name: pretokenized,filtered,sorted subset of the Pile
size_categories:
- 10B<n<100B
source_datasets:
- the-pile
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: the-pile-cramming
---
# Dataset Card for "the_pile_WordPiecex32768_2efdb9d060d1ae95faf952ec1a50f020"
## Dataset Description
- **Repository:** https://github.com/JonasGeiping/cramming
- **Paper:** https://arxiv.org/abs/2212.14034
- **Raw Data Source Paper:** [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)
- **Raw Data Source Datasheet:** [Datasheet for the Pile](https://arxiv.org/abs/2201.07311)
### Dataset Summary
This is a preprocessed, tokenized dataset for the cramming-project.
Use only with the tokenizer uploaded here.
This version is `2efdb9d060d1ae95faf952ec1a50f020`, which corresponds to a specific dataset construction setup, described below.
The raw data source is the Pile, a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
datasets combined together.
### Languages
This dataset is in English (`EN`).
### Data Splits
This preprocessed subset contains only a train split.
## Dataset Creation
The configuration to create this dataset with the cramming project code (https://github.com/JonasGeiping/cramming) is
```
# This is a slice of the pile
name: the_pile
defaults:
- sources:
- the_pile
#
# Preprocessing
normalizer:
force_lowercase: True
strip_accents: True
force_english_keyboard: True
whitespace_escape: False
tokenizer: WordPiece
vocab_size: 32768
# Dataset Formation
seq_length: 128
include_cls_token_in_corpus: False
include_sep_token_in_corpus: True
use_type_ids: False
max_entries_in_raw_dataset: 16e6
max_seq_in_tokenized_dataset: 85e6
# Data Cleaning:
named_entity_simplification: False
remove_whitespaces: False
remove_trash: True
trash_cutoff: 0.25
deduplicate_entries: False
deduplication_threshold: 75
# Data Order:
ordering: sentence-length-curriculum
```
## Considerations for Using the Data
Limitations and bias:
This training data was further filtered and sorted beyond the normal preprocessing.
These modifications were not tested for unintended consequences.
## Additional Information
### Dataset Curators
This dataset is a filtered, sorted and preprocessed subset of the the-Pile made by Jonas Geiping . The original dataset was primarily curated by Leo Gao and Stella Biderman, with assistance from other authors of the Pile paper.
### Licensing Information
Please refer to the specific license depending on the subset you use at https://huggingface.co/datasets/EleutherAI/pile
### Citation Information
Filtered version for the cramming project:
```
@article{geiping_cramming_2022,
title = {Cramming: {{Training}} a {{Language Model}} on a {{Single GPU}} in {{One Day}}},
shorttitle = {Cramming},
author = {Geiping, Jonas and Goldstein, Tom},
year = {2022},
month = dec,
eprint = {2212.14034},
primaryclass = {cs},
publisher = {{arXiv}},
doi = {10.48550/arXiv.2212.14034},
url = {http://arxiv.org/abs/2212.14034},
urldate = {2023-01-10},
archiveprefix = {arxiv},
keywords = {Computer Science - Computation and Language,Computer Science - Machine Learning},
journal = {arxiv:2212.14034[cs]}
}
```
Original Data Curation:
```
@article{gao2020pile,
title={The {P}ile: An 800{GB} dataset of diverse text for language modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
@article{biderman2022datasheet,
title={Datasheet for the pile},
author={Biderman, Stella and Bicheno, Kieran and Gao, Leo},
journal={arXiv preprint arXiv:2201.07311},
year={2022}
}
``` | [
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open-llm-leaderboard/details_EleutherAI__gpt-neox-20b | open-llm-leaderboard | 2023-12-03T17:14:50Z | 551 | 0 | [
"region:us"
] | null | 2023-08-17T23:47:41Z | ---
pretty_name: Evaluation run of EleutherAI/gpt-neox-20b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) 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 aggregated 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_EleutherAI__gpt-neox-20b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:14:42.607420](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neox-20b/blob/main/results_2023-12-03T17-14-42.607420.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 \"acc\": 0.05458680818802123,\n\
\ \"acc_stderr\": 0.00625744403791253\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.05458680818802123,\n \"acc_stderr\": 0.00625744403791253\n\
\ }\n}\n```"
repo_url: https://huggingface.co/EleutherAI/gpt-neox-20b
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_20T10_44_54.391639
path:
- '**/details_harness|arc:challenge|25_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_08T18_29_20.429481
path:
- '**/details_harness|drop|3_2023-09-08T18-29-20.429481.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-08T18-29-20.429481.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_08T18_29_20.429481
path:
- '**/details_harness|gsm8k|5_2023-09-08T18-29-20.429481.parquet'
- split: 2023_12_03T17_14_42.607420
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-14-42.607420.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-14-42.607420.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hellaswag|10_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-07-20T10:44:54.391639.parquet'
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path:
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- config_name: harness_winogrande_5
data_files:
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path:
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path:
- '**/details_harness|winogrande|5_2023-09-08T18-29-20.429481.parquet'
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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:
- '**/details_original|mmlu:abstract_algebra|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_anatomy_5
data_files:
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path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_astronomy_5
data_files:
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path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_business_ethics_5
data_files:
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path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_clinical_knowledge_5
data_files:
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path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:20:23.118147.parquet'
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path:
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- config_name: original_mmlu_college_biology_5
data_files:
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path:
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- split: latest
path:
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- config_name: original_mmlu_college_chemistry_5
data_files:
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path:
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- split: latest
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_college_computer_science_5
data_files:
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path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_college_mathematics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_college_medicine_5
data_files:
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path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_college_physics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_computer_security_5
data_files:
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path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T20:20:23.118147.parquet'
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path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_conceptual_physics_5
data_files:
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path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_econometrics_5
data_files:
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path:
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path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_electrical_engineering_5
data_files:
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path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_elementary_mathematics_5
data_files:
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path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_formal_logic_5
data_files:
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path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:20:23.118147.parquet'
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path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_global_facts_5
data_files:
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path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:20:23.118147.parquet'
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path:
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- config_name: original_mmlu_high_school_biology_5
data_files:
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path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_chemistry_5
data_files:
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path:
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path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_computer_science_5
data_files:
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path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:20:23.118147.parquet'
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path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_european_history_5
data_files:
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path:
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path:
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- config_name: original_mmlu_high_school_geography_5
data_files:
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path:
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path:
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- config_name: original_mmlu_high_school_government_and_politics_5
data_files:
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path:
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path:
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- config_name: original_mmlu_high_school_macroeconomics_5
data_files:
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path:
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path:
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- config_name: original_mmlu_high_school_mathematics_5
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path:
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path:
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- config_name: original_mmlu_high_school_microeconomics_5
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path:
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- split: latest
path:
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- config_name: original_mmlu_high_school_physics_5
data_files:
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path:
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- config_name: original_mmlu_high_school_psychology_5
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- config_name: original_mmlu_high_school_statistics_5
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- config_name: original_mmlu_high_school_us_history_5
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path:
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- config_name: original_mmlu_high_school_world_history_5
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path:
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- config_name: original_mmlu_human_aging_5
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path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:20:23.118147.parquet'
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- config_name: original_mmlu_human_sexuality_5
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path:
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- config_name: original_mmlu_international_law_5
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path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_jurisprudence_5
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- '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:20:23.118147.parquet'
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path:
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- config_name: original_mmlu_logical_fallacies_5
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path:
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- config_name: original_mmlu_machine_learning_5
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path:
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path:
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- config_name: original_mmlu_management_5
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- config_name: original_mmlu_marketing_5
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- config_name: original_mmlu_medical_genetics_5
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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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- split: latest
path:
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- config_name: original_mmlu_moral_scenarios_5
data_files:
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path:
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- split: latest
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- config_name: original_mmlu_philosophy_5
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- '**/details_original|mmlu:philosophy|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
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- config_name: original_mmlu_prehistory_5
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- split: 2023_08_28T20_20_23.118147
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- '**/details_original|mmlu:prehistory|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
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- '**/details_original|mmlu:prehistory|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_professional_accounting_5
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- split: 2023_08_28T20_20_23.118147
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- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_professional_law_5
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- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_professional_medicine_5
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- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
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- config_name: original_mmlu_professional_psychology_5
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- '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:20:23.118147.parquet'
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- config_name: original_mmlu_public_relations_5
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path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:20:23.118147.parquet'
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- config_name: original_mmlu_us_foreign_policy_5
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- config_name: original_mmlu_world_religions_5
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- '**/details_original|mmlu:world_religions|5_2023-08-28T20:20:23.118147.parquet'
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path:
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- config_name: results
data_files:
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- results_2023-08-28T20:20:23.118147.parquet
- split: 2023_09_08T18_29_20.429481
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- split: 2023_12_03T17_14_42.607420
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- results_2023-12-03T17-14-42.607420.parquet
- split: latest
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---
# Dataset Card for Evaluation run of EleutherAI/gpt-neox-20b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/EleutherAI/gpt-neox-20b
- **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 [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) 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 aggregated 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_EleutherAI__gpt-neox-20b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:14:42.607420](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neox-20b/blob/main/results_2023-12-03T17-14-42.607420.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": {
"acc": 0.05458680818802123,
"acc_stderr": 0.00625744403791253
},
"harness|gsm8k|5": {
"acc": 0.05458680818802123,
"acc_stderr": 0.00625744403791253
}
}
```
### 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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compguesswhat | null | 2023-04-05T10:02:19Z | 550 | 1 | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-guesswhat",
"language:en",
"license:unknown",
"region:us"
] | [
"visual-question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: CompGuessWhat?!
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-guesswhat
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
paperswithcode_id: compguesswhat
dataset_info:
- config_name: compguesswhat-original
features:
- name: id
dtype: int32
- name: target_id
dtype: int32
- name: timestamp
dtype: string
- name: status
dtype: string
- name: image
struct:
- name: id
dtype: int32
- name: file_name
dtype: string
- name: flickr_url
dtype: string
- name: coco_url
dtype: string
- name: height
dtype: int32
- name: width
dtype: int32
- name: visual_genome
struct:
- name: width
dtype: int32
- name: height
dtype: int32
- name: url
dtype: string
- name: coco_id
dtype: int32
- name: flickr_id
dtype: string
- name: image_id
dtype: string
- name: qas
sequence:
- name: question
dtype: string
- name: answer
dtype: string
- name: id
dtype: int32
- name: objects
sequence:
- name: id
dtype: int32
- name: bbox
sequence: float32
length: 4
- name: category
dtype: string
- name: area
dtype: float32
- name: category_id
dtype: int32
- name: segment
sequence:
sequence: float32
splits:
- name: train
num_bytes: 126548689
num_examples: 46341
- name: validation
num_bytes: 26055261
num_examples: 9738
- name: test
num_bytes: 25981593
num_examples: 9621
download_size: 107201655
dataset_size: 178585543
- config_name: compguesswhat-zero_shot
features:
- name: id
dtype: int32
- name: target_id
dtype: string
- name: status
dtype: string
- name: image
struct:
- name: id
dtype: int32
- name: file_name
dtype: string
- name: coco_url
dtype: string
- name: height
dtype: int32
- name: width
dtype: int32
- name: license
dtype: int32
- name: open_images_id
dtype: string
- name: date_captured
dtype: string
- name: objects
sequence:
- name: id
dtype: string
- name: bbox
sequence: float32
length: 4
- name: category
dtype: string
- name: area
dtype: float32
- name: category_id
dtype: int32
- name: IsOccluded
dtype: int32
- name: IsTruncated
dtype: int32
- name: segment
sequence:
- name: MaskPath
dtype: string
- name: LabelName
dtype: string
- name: BoxID
dtype: string
- name: BoxXMin
dtype: string
- name: BoxXMax
dtype: string
- name: BoxYMin
dtype: string
- name: BoxYMax
dtype: string
- name: PredictedIoU
dtype: string
- name: Clicks
dtype: string
splits:
- name: nd_valid
num_bytes: 13557059
num_examples: 5343
- name: nd_test
num_bytes: 36352201
num_examples: 13836
- name: od_valid
num_bytes: 14093233
num_examples: 5372
- name: od_test
num_bytes: 33049755
num_examples: 13300
download_size: 4845966
dataset_size: 97052248
---
# Dataset Card for "compguesswhat"
## 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://compguesswhat.github.io/](https://compguesswhat.github.io/)
- **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:** 112.05 MB
- **Size of the generated dataset:** 271.11 MB
- **Total amount of disk used:** 383.16 MB
### Dataset Summary
CompGuessWhat?! is an instance of a multi-task framework for evaluating the quality of learned neural representations,
in particular concerning attribute grounding. Use this dataset if you want to use the set of games whose reference
scene is an image in VisualGenome. Visit the website for more details: https://compguesswhat.github.io
### 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
#### compguesswhat-original
- **Size of downloaded dataset files:** 107.21 MB
- **Size of the generated dataset:** 174.37 MB
- **Total amount of disk used:** 281.57 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"id": 2424,
"image": "{\"coco_url\": \"http://mscoco.org/images/270512\", \"file_name\": \"COCO_train2014_000000270512.jpg\", \"flickr_url\": \"http://farm6.stat...",
"objects": "{\"area\": [1723.5133056640625, 4838.5361328125, 287.44476318359375, 44918.7109375, 3688.09375, 522.1935424804688], \"bbox\": [[5.61...",
"qas": {
"answer": ["Yes", "No", "No", "Yes"],
"id": [4983, 4996, 5006, 5017],
"question": ["Is it in the foreground?", "Does it have wings?", "Is it a person?", "Is it a vehicle?"]
},
"status": "success",
"target_id": 1197044,
"timestamp": "2016-07-08 15:07:38"
}
```
#### compguesswhat-zero_shot
- **Size of downloaded dataset files:** 4.84 MB
- **Size of the generated dataset:** 96.74 MB
- **Total amount of disk used:** 101.59 MB
An example of 'nd_valid' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"image": {
"coco_url": "https://s3.amazonaws.com/nocaps/val/004e21eb2e686f40.jpg",
"date_captured": "2018-11-06 11:04:33",
"file_name": "004e21eb2e686f40.jpg",
"height": 1024,
"id": 6,
"license": 0,
"open_images_id": "004e21eb2e686f40",
"width": 768
},
"objects": "{\"IsOccluded\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"IsTruncated\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"area\": [3...",
"status": "incomplete",
"target_id": "004e21eb2e686f40_30"
}
```
### Data Fields
The data fields are the same among all splits.
#### compguesswhat-original
- `id`: a `int32` feature.
- `target_id`: a `int32` feature.
- `timestamp`: a `string` feature.
- `status`: a `string` feature.
- `id`: a `int32` feature.
- `file_name`: a `string` feature.
- `flickr_url`: a `string` feature.
- `coco_url`: a `string` feature.
- `height`: a `int32` feature.
- `width`: a `int32` feature.
- `width`: a `int32` feature.
- `height`: a `int32` feature.
- `url`: a `string` feature.
- `coco_id`: a `int32` feature.
- `flickr_id`: a `string` feature.
- `image_id`: a `string` feature.
- `qas`: a dictionary feature containing:
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `id`: a `int32` feature.
- `objects`: a dictionary feature containing:
- `id`: a `int32` feature.
- `bbox`: a `list` of `float32` features.
- `category`: a `string` feature.
- `area`: a `float32` feature.
- `category_id`: a `int32` feature.
- `segment`: a dictionary feature containing:
- `feature`: a `float32` feature.
#### compguesswhat-zero_shot
- `id`: a `int32` feature.
- `target_id`: a `string` feature.
- `status`: a `string` feature.
- `id`: a `int32` feature.
- `file_name`: a `string` feature.
- `coco_url`: a `string` feature.
- `height`: a `int32` feature.
- `width`: a `int32` feature.
- `license`: a `int32` feature.
- `open_images_id`: a `string` feature.
- `date_captured`: a `string` feature.
- `objects`: a dictionary feature containing:
- `id`: a `string` feature.
- `bbox`: a `list` of `float32` features.
- `category`: a `string` feature.
- `area`: a `float32` feature.
- `category_id`: a `int32` feature.
- `IsOccluded`: a `int32` feature.
- `IsTruncated`: a `int32` feature.
- `segment`: a dictionary feature containing:
- `MaskPath`: a `string` feature.
- `LabelName`: a `string` feature.
- `BoxID`: a `string` feature.
- `BoxXMin`: a `string` feature.
- `BoxXMax`: a `string` feature.
- `BoxYMin`: a `string` feature.
- `BoxYMax`: a `string` feature.
- `PredictedIoU`: a `string` feature.
- `Clicks`: a `string` feature.
### Data Splits
#### compguesswhat-original
| |train|validation|test|
|----------------------|----:|---------:|---:|
|compguesswhat-original|46341| 9738|9621|
#### compguesswhat-zero_shot
| |nd_valid|od_valid|nd_test|od_test|
|-----------------------|-------:|-------:|------:|------:|
|compguesswhat-zero_shot| 5343| 5372| 13836| 13300|
## 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{suglia2020compguesswhat,
title={CompGuessWhat?!: a Multi-task Evaluation Framework for Grounded Language Learning},
author={Suglia, Alessandro, Konstas, Ioannis, Vanzo, Andrea, Bastianelli, Emanuele, Desmond Elliott, Stella Frank and Oliver Lemon},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@aleSuglia](https://github.com/aleSuglia), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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spc | null | 2023-06-01T14:59:49Z | 550 | 0 | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:af",
"language:el",
"language:en",
"language:zh",
"license:unknown",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- af
- el
- en
- zh
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: spc
dataset_info:
- config_name: af-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- af
- en
splits:
- name: train
num_bytes: 4605446
num_examples: 57351
download_size: 1105038
dataset_size: 4605446
- config_name: el-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- el
- en
splits:
- name: train
num_bytes: 3797941
num_examples: 8181
download_size: 841228
dataset_size: 3797941
- config_name: en-zh
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- zh
splits:
- name: train
num_bytes: 849200
num_examples: 2228
download_size: 189995
dataset_size: 849200
config_names:
- af-en
- el-en
- en-zh
---
# Dataset Card for spc
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/SPC.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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language-and-voice-lab/samromur_asr | language-and-voice-lab | 2023-02-24T22:02:05Z | 550 | 0 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:is",
"license:cc-by-4.0",
"crowd-sourced icelandic",
"samrómur",
"icelandic speech",
"samromur",
"iceland",
"region:us"
] | [
"automatic-speech-recognition"
] | 2022-11-27T08:33:04Z | ---
annotations_creators:
- crowdsourced
language:
- is
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: "Samrómur Icelandic Speech 1.0."
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- crowd-sourced icelandic
- "samrómur"
- icelandic speech
- samromur
- iceland
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for samromur_asr
## 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-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:** [Samrómur 21.05]
- **Repository:** [OpenSLR](http://www.openslr.org/112/)
- **Paper:** [Samrómur: Crowd-sourcing Data Collection for Icelandic Speech Recognition](https://aclanthology.org/2020.lrec-1.425.pdf)
- **Point of Contact:** [Jón Guðnason](mailto:[email protected])
### Dataset Summary
This is the first release of the Samrómur Icelandic Speech corpus that contains 100.000 validated utterances.
The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology.
### Example Usage
The Samrómur Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:
```python
from datasets import load_dataset
samromur_asr = load_dataset("language-and-voice-lab/samromur_asr")
```
To load an specific split (for example, the validation split) do:
```python
from datasets import load_dataset
samromur_asr = load_dataset("language-and-voice-lab/samromur_asr",split="validation")
```
### Supported Tasks
automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### Languages
The audio is in Icelandic.
The reading prompts were gathered from a variety of sources, mainly from the [Icelandic Gigaword Corpus](http://clarin.is/en/resources/gigaword). The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/).
## Dataset Structure
### Data Instances
```python
{
'audio_id': '009123-0150695',
'audio': {
'path': '/home/david/.cache/HuggingFace/datasets/downloads/extracted/cb428a7f1e46b058d76641ef32f36b49d28b73aea38509983170495408035a10/dev/009123/009123-0150695.flac',
'array': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
'sampling_rate': 16000
},
'speaker_id': '009123',
'gender': 'female',
'age': '18-19',
'duration': 3.299999952316284,
'normalized_text': 'það skipti heldur engu'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker (male or female)
* `age` (string) - range of age of the speaker.
* `duration` (float32) - duration of the audio file in seconds.
* `normalized_text` (string) - normalized audio segment transcription.
### Data Splits
The corpus is split into train, validation, and test subsets with no speaker overlap. Each subset contains folders that correspond to speaker IDs, and the audio files inside use the following naming convention: {speaker_ID}-{utterance_ID}.flac. Lenghts of each portion are: train=114h/34m, test=15h51m, validation=15h16m.
To load an specific portion please see the above section "Example Usage".
## Dataset Creation
### Curation Rationale
* The recording has started in October 2019 and continues to this day (May 2021).
* This release has been authorized for release in May 2021.
* The aim is to create an open-source speech corpus to enable research and development for Icelandic Language Technology.
* The corpus contains audio recordings and a metadata file that contains the prompts the participants read.
* A Kaldi based script using this data can be found on the Language and Voice Lab gitHub page https://github.com/cadia-lvl/samromur-asr
### Source Data
#### Initial Data Collection and Normalization
* The utterances were recorded by a smartphone or the web app.
* The data was collected using the website https://samromur.is, code of which is available at https://github.com/cadia-lvl/samromur.
* Each recording contains one read sentence from a script.
* The script contains 85.080 unique sentences and 90.838 unique tokens.
### Annotations
#### Annotation process
Prompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the [DIM: Database Icelandic Morphology](https://aclanthology.org/W19-6116.pdf).
There are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device.
#### Who are the annotators?
The audio files content was manually verified against the prompts by one or more listener (summer students mainly).
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This contribution describes an ongoing project of speech data collection, using the web application Samrómur which is built upon Common Voice, Mozilla Foundation's web platform for open-source voice collection. The goal of the project is to build a large-scale speech corpus for Automatic Speech Recognition (ASR) for Icelandic. Upon completion, Samrómur will be the largest open speech corpus for Icelandic collected from the public domain.
### Discussion of Biases
* The participants are aged between 18 to 90, 59,782 recordings are from female speakers and 40,218 are from male, recorded by a smartphone or the web app.
* Participants self-reported their age group, gender, and the native language.
* The corpus contains 100 000 utterance from 8392 speaker, totalling 145 hours.
### Other Known Limitations
"Samromur 21.05" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
## Additional Information
### Dataset Curators
The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab at the Reykjavik University, in cooperation with Almannarómur, Center for Language Technology.
### Licensing Information
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{mollberg-etal-2020-samromur,
title = "{S}amr{\'o}mur: Crowd-sourcing Data Collection for {I}celandic Speech Recognition",
author = "Mollberg, David Erik and
J{\'o}nsson, {\'O}lafur Helgi and
{\TH}orsteinsd{\'o}ttir, Sunneva and
Steingr{\'\i}msson, Stein{\th}{\'o}r and
Magn{\'u}sd{\'o}ttir, Eyd{\'\i}s Huld and
Gudnason, Jon",
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-1.425",
pages = "3463--3467",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarómur, is funded by the Icelandic Ministry of Education, Science and Culture.
The verification for the dataset was funded by the the Icelandic Directorate of Labour's Student Summer Job Program.
Special thanks for the summer students for all the hard work.
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afrikaans_ner_corpus | null | 2023-01-25T14:20:30Z | 549 | 3 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:af",
"license:other",
"region:us"
] | [
"token-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Afrikaans Ner Corpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: afrikaans_ner_corpus
splits:
- name: train
num_bytes: 4025667
num_examples: 8962
download_size: 25748344
dataset_size: 4025667
---
# Dataset Card for Afrikaans Ner 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:** [Afrikaans Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/299)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:[email protected])
### Dataset Summary
The Afrikaans Ner Corpus is an Afrikaans dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Afrikaans.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0],
'tokens': ['Vertaling', 'van', 'die', 'inligting', 'in']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Afrikaans.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
[More Information Needed]
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[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
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{afrikaans_ner_corpus,
author = { Gerhard van Huyssteen and
Martin Puttkammer and
E.B. Trollip and
J.C. Liversage and
Roald Eiselen},
title = {NCHLT Afrikaans Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/299},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. | [
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allenai/cord19 | allenai | 2022-11-03T16:31:53Z | 549 | 2 | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-nd-4.0",
"license:cc-by-sa-4.0",
"license:other",
"arxiv:2004.07180",
"region:us"
] | [
"other"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nd-4.0
- cc-by-sa-4.0
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: cord-19
pretty_name: CORD-19
dataset_info:
- config_name: metadata
features:
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dtype: string
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dtype: string
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splits:
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num_bytes: 496247275
num_examples: 368618
download_size: 6142360818
dataset_size: 496247275
- config_name: fulltext
features:
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dtype: string
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splits:
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download_size: 6142360818
dataset_size: 3718245736
- config_name: embeddings
features:
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dtype: string
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sequence: float64
splits:
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num_bytes: 2759561943
num_examples: 368618
download_size: 6142360818
dataset_size: 2759561943
---
# Dataset Card for CORD-19
## 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://www.semanticscholar.org/cord19](https://www.semanticscholar.org/cord19)
- **Repository:** [https://github.com/allenai/cord19](https://github.com/allenai/cord19)
- **Paper:** [CORD-19: The COVID-19 Open Research Dataset](https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1/)
- **Leaderboard:** [Kaggle challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)
### Dataset Summary
CORD-19 is a corpus of academic papers about COVID-19 and related coronavirus research. It's curated and maintained by the Semantic Scholar team at the Allen Institute for AI to support text mining and NLP research.
### Supported Tasks and Leaderboards
See tasks defined in the related [Kaggle challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge/tasks)
### Languages
The dataset is in english (en).
## Dataset Structure
### Data Instances
The following code block present an overview of a sample in json-like syntax (abbreviated since some fields are very long):
```
{
"abstract": "OBJECTIVE: This retrospective chart review describes the epidemiology and clinical features of 40 patients with culture-proven Mycoplasma pneumoniae infections at King Abdulaziz University Hospital, Jeddah, Saudi Arabia. METHODS: Patients with positive M. pneumoniae cultures from respiratory specimens from January 1997 through December 1998 were identified through the Microbiology records. Charts of patients were reviewed. RESULTS: 40 patients were identified [...]",
"authors": "Madani, Tariq A; Al-Ghamdi, Aisha A",
"cord_uid": "ug7v899j",
"doc_embeddings": [
-2.939983606338501,
-6.312200546264648,
-1.0459030866622925,
[...] 766 values in total [...]
-4.107113361358643,
-3.8174145221710205,
1.8976187705993652,
5.811529159545898,
-2.9323840141296387
],
"doi": "10.1186/1471-2334-1-6",
"journal": "BMC Infect Dis",
"publish_time": "2001-07-04",
"sha": "d1aafb70c066a2068b02786f8929fd9c900897fb",
"source_x": "PMC",
"title": "Clinical features of culture-proven Mycoplasma pneumoniae infections at King Abdulaziz University Hospital, Jeddah, Saudi Arabia",
"url": "https: //www.ncbi.nlm.nih.gov/pmc/articles/PMC35282/"
}
```
### Data Fields
Currently only the following fields are integrated: `cord_uid`, `sha`,`source_x`, `title`, `doi`, `abstract`, `publish_time`, `authors`, `journal`. With `fulltext` configuration, the sections transcribed in `pdf_json_files` are converted in `fulltext` feature.
- `cord_uid`: A `str`-valued field that assigns a unique identifier to each CORD-19 paper. This is not necessariy unique per row, which is explained in the FAQs.
- `sha`: A `List[str]`-valued field that is the SHA1 of all PDFs associated with the CORD-19 paper. Most papers will have either zero or one value here (since we either have a PDF or we don't), but some papers will have multiple. For example, the main paper might have supplemental information saved in a separate PDF. Or we might have two separate PDF copies of the same paper. If multiple PDFs exist, their SHA1 will be semicolon-separated (e.g. `'4eb6e165ee705e2ae2a24ed2d4e67da42831ff4a; d4f0247db5e916c20eae3f6d772e8572eb828236'`)
- `source_x`: A `List[str]`-valued field that is the names of sources from which we received this paper. Also semicolon-separated. For example, `'ArXiv; Elsevier; PMC; WHO'`. There should always be at least one source listed.
- `title`: A `str`-valued field for the paper title
- `doi`: A `str`-valued field for the paper DOI
- `pmcid`: A `str`-valued field for the paper's ID on PubMed Central. Should begin with `PMC` followed by an integer.
- `pubmed_id`: An `int`-valued field for the paper's ID on PubMed.
- `license`: A `str`-valued field with the most permissive license we've found associated with this paper. Possible values include: `'cc0', 'hybrid-oa', 'els-covid', 'no-cc', 'cc-by-nc-sa', 'cc-by', 'gold-oa', 'biorxiv', 'green-oa', 'bronze-oa', 'cc-by-nc', 'medrxiv', 'cc-by-nd', 'arxiv', 'unk', 'cc-by-sa', 'cc-by-nc-nd'`
- `abstract`: A `str`-valued field for the paper's abstract
- `publish_time`: A `str`-valued field for the published date of the paper. This is in `yyyy-mm-dd` format. Not always accurate as some publishers will denote unknown dates with future dates like `yyyy-12-31`
- `authors`: A `List[str]`-valued field for the authors of the paper. Each author name is in `Last, First Middle` format and semicolon-separated.
- `journal`: A `str`-valued field for the paper journal. Strings are not normalized (e.g. `BMJ` and `British Medical Journal` can both exist). Empty string if unknown.
- `mag_id`: Deprecated, but originally an `int`-valued field for the paper as represented in the Microsoft Academic Graph.
- `who_covidence_id`: A `str`-valued field for the ID assigned by the WHO for this paper. Format looks like `#72306`.
- `arxiv_id`: A `str`-valued field for the arXiv ID of this paper.
- `pdf_json_files`: A `List[str]`-valued field containing paths from the root of the current data dump version to the parses of the paper PDFs into JSON format. Multiple paths are semicolon-separated. Example: `document_parses/pdf_json/4eb6e165ee705e2ae2a24ed2d4e67da42831ff4a.json; document_parses/pdf_json/d4f0247db5e916c20eae3f6d772e8572eb828236.json`
- `pmc_json_files`: A `List[str]`-valued field. Same as above, but corresponding to the full text XML files downloaded from PMC, parsed into the same JSON format as above.
- `url`: A `List[str]`-valued field containing all URLs associated with this paper. Semicolon-separated.
- `s2_id`: A `str`-valued field containing the Semantic Scholar ID for this paper. Can be used with the Semantic Scholar API (e.g. `s2_id=9445722` corresponds to `http://api.semanticscholar.org/corpusid:9445722`)
Extra fields based on selected configuration during loading:
- `fulltext`: A `str`-valued field containing the concatenation of all text sections from json (itself extracted from pdf)
- `doc_embeddings`: A `sequence` of float-valued elements containing document embeddings as a vector of floats (parsed from string of values separated by ','). Details on the system used to extract the embeddings are available in: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/abs/2004.07180). TL;DR: it's relying on a BERT model pre-trained on document-level relatedness using the citation graph. The system can be queried through REST (see [public API documentation](https://github.com/allenai/paper-embedding-public-apis)).
### Data Splits
No annotation provided in this dataset so all instances are provided in training split.
The sizes of each configuration are:
| | train |
|------------|-------:|
| metadata | 368618 |
| fulltext | 368618 |
| embeddings | 368618 |
## Dataset Creation
### Curation Rationale
See [official readme](https://github.com/allenai/cord19/blob/master/README.md)
### Source Data
See [official readme](https://github.com/allenai/cord19/blob/master/README.md)
#### Initial Data Collection and Normalization
See [official readme](https://github.com/allenai/cord19/blob/master/README.md)
#### Who are the source language producers?
See [official readme](https://github.com/allenai/cord19/blob/master/README.md)
### Annotations
No annotations
#### Annotation process
N/A
#### Who are the annotators?
N/A
### 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{Wang2020CORD19TC,
title={CORD-19: The Covid-19 Open Research Dataset},
author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and
K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and
Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and
D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},
journal={ArXiv},
year={2020}
}
```
### Contributions
Thanks to [@ggdupont](https://github.com/ggdupont) for adding this dataset. | [
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neural_code_search | null | 2023-06-01T14:59:50Z | 548 | 7 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:1908.09804",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
pretty_name: Neural Code Search
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: neural-code-search-evaluation-dataset
dataset_info:
- config_name: evaluation_dataset
features:
- name: stackoverflow_id
dtype: int32
- name: question
dtype: string
- name: question_url
dtype: string
- name: question_author
dtype: string
- name: question_author_url
dtype: string
- name: answer
dtype: string
- name: answer_url
dtype: string
- name: answer_author
dtype: string
- name: answer_author_url
dtype: string
- name: examples
sequence: int32
- name: examples_url
sequence: string
splits:
- name: train
num_bytes: 296848
num_examples: 287
download_size: 383625
dataset_size: 296848
- config_name: search_corpus
features:
- name: id
dtype: int32
- name: filepath
dtype: string
- name: method_name
dtype: string
- name: start_line
dtype: int32
- name: end_line
dtype: int32
- name: url
dtype: string
splits:
- name: train
num_bytes: 1452630278
num_examples: 4716814
download_size: 121112543
dataset_size: 1452630278
config_names:
- evaluation_dataset
- search_corpus
---
# Dataset Card for Neural Code Search
## 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:**
[facebookresearch
/
Neural-Code-Search-Evaluation-Dataset](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset/tree/master/data)
- **Repository:**
[Github](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset.git)
- **Paper:**
[arXiv](https://arxiv.org/pdf/1908.09804.pdf)
### Dataset Summary
Neural-Code-Search-Evaluation-Dataset presents an evaluation dataset consisting of natural language query and code snippet pairs, with the hope that future work in this area can use this dataset as a common benchmark. We also provide the results of two code search models (NCS, UNIF) from recent work.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
EN - English
## Dataset Structure
### Data Instances
#### Search Corpus
The search corpus is indexed using all method bodies parsed from the 24,549 GitHub repositories. In total, there are 4,716,814 methods in this corpus. The code search model will find relevant code snippets (i.e. method bodies) from this corpus given a natural language query. In this data release, we will provide the following information for each method in the corpus:
#### Evaluation Dataset
The evaluation dataset is composed of 287 Stack Overflow question and answer pairs
### Data Fields
#### Search Corpus
- id: Each method in the corpus has a unique numeric identifier. This ID number will also be referenced in our evaluation dataset.
- filepath: The file path is in the format of :owner/:repo/relative-file-path-to-the-repo
method_name
- start_line: Starting line number of the method in the file.
- end_line: Ending line number of the method in the file.
- url: GitHub link to the method body with commit ID and line numbers encoded.
#### Evaluation Dataset
- stackoverflow_id: Stack Overflow post ID.
- question: Title fo the Stack Overflow post.
- question_url: URL of the Stack Overflow post.
- answer: Code snippet answer to the question.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The most popular Android repositories on GitHub (ranked by the number of stars) is used to create the search corpus. For each repository that we indexed, we provide the link, specific to the commit that was used.5 In total, there are 24,549 repositories.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
Hongyu Li, Seohyun Kim and Satish Chandra
### Licensing Information
CC-BY-NC 4.0 (Attr Non-Commercial Inter.)
### Citation Information
arXiv:1908.09804 [cs.SE]
### Contributions
Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset. | [
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iamshnoo/geomlama | iamshnoo | 2023-11-22T10:53:13Z | 548 | 0 | [
"region:us"
] | null | 2023-08-02T01:18:19Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: candidate_answers
dtype: string
- name: context
dtype: string
- name: country
dtype: string
splits:
- name: en
num_bytes: 20705
num_examples: 150
- name: fa
num_bytes: 29418
num_examples: 150
- name: hi
num_bytes: 41903
num_examples: 150
- name: sw
num_bytes: 21231
num_examples: 150
- name: zh
num_bytes: 19155
num_examples: 150
- name: el
num_bytes: 38057
num_examples: 150
download_size: 45566
dataset_size: 170469
---
data from the paper GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models
(along with some new data and modifications for cleaning)
[GitHub](https://github.com/WadeYin9712/GeoMLAMA)
# Dataset Card for "geomlama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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google_wellformed_query | null | 2022-11-18T20:04:48Z | 547 | 9 | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:1808.09419",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
task_categories:
- text-classification
multilinguality:
- monolingual
task_ids:
- text-scoring
language:
- en
annotations_creators:
- crowdsourced
source_datasets:
- extended
size_categories:
- 10K<n<100K
license:
- cc-by-sa-4.0
paperswithcode_id: null
pretty_name: GoogleWellformedQuery
language_creators:
- found
dataset_info:
features:
- name: rating
dtype: float32
- name: content
dtype: string
splits:
- name: train
num_bytes: 857391
num_examples: 17500
- name: test
num_bytes: 189503
num_examples: 3850
- name: validation
num_bytes: 184110
num_examples: 3750
download_size: 1157019
dataset_size: 1231004
---
# Dataset Card for Google Query-wellformedness Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/google-research-datasets/query-wellformedness)
- **Repository:** [GitHub](https://github.com/google-research-datasets/query-wellformedness)
- **Paper:** [ARXIV](https://arxiv.org/abs/1808.09419)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
```
{'rating': 0.2, 'content': 'The European Union includes how many ?'}
```
### Data Fields
- `rating`: a `float` between 0-1
- `sentence`: query which you want to rate
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | 17500 | 3750 | 3850 |
## Dataset Creation
### Curation Rationale
Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question.
### Source Data
Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”).
#### Initial Data Collection and Normalization
Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The queries are annotated into well-formed or non-wellformed questions if it satisfies the following:
1. Query is grammatical.
2. Query is an explicit question.
3. Query does not contain spelling errors.
#### Who are the annotators?
Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed.
### 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
Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied.
### Citation Information
```
@InProceedings{FaruquiDas2018,
title = {{Identifying Well-formed Natural Language Questions}},
author = {Faruqui, Manaal and Das, Dipanjan},
booktitle = {Proc. of EMNLP},
year = {2018}
}
```
### Contributions
Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset. | [
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] |
persian_ner | null | 2023-01-25T14:42:29Z | 547 | 0 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:fa",
"license:cc-by-4.0",
"region:us"
] | [
"token-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fa
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Persian NER
dataset_info:
- config_name: fold1
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': I-event
'2': I-fac
'3': I-loc
'4': I-org
'5': I-pers
'6': I-pro
'7': B-event
'8': B-fac
'9': B-loc
'10': B-org
'11': B-pers
'12': B-pro
splits:
- name: train
num_bytes: 3362102
num_examples: 5121
- name: test
num_bytes: 1646481
num_examples: 2560
download_size: 1931170
dataset_size: 5008583
- config_name: fold2
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': I-event
'2': I-fac
'3': I-loc
'4': I-org
'5': I-pers
'6': I-pro
'7': B-event
'8': B-fac
'9': B-loc
'10': B-org
'11': B-pers
'12': B-pro
splits:
- name: train
num_bytes: 3344561
num_examples: 5120
- name: test
num_bytes: 1664022
num_examples: 2561
download_size: 1931170
dataset_size: 5008583
- config_name: fold3
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': I-event
'2': I-fac
'3': I-loc
'4': I-org
'5': I-pers
'6': I-pro
'7': B-event
'8': B-fac
'9': B-loc
'10': B-org
'11': B-pers
'12': B-pro
splits:
- name: train
num_bytes: 3310491
num_examples: 5121
- name: test
num_bytes: 1698092
num_examples: 2560
download_size: 1931170
dataset_size: 5008583
---
# Dataset Card for [Persian 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/HaniehP/PersianNER)
- **Repository:** [Github](https://github.com/HaniehP/PersianNER)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/C16-1319)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset includes 7,682 Persian sentences, split into 250,015 tokens and their NER labels. It is available in 3 folds to be used in turn as training and test sets. The NER tags are in IOB format.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "I-event", "I-fac", "I-loc", "I-org", "I-pers", "I-pro", "B-event", "B-fac", "B-loc", "B-org", "B-pers", "B-pro"
```
### Data Splits
Training and test splits
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi
### 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 is published for academic use only
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons Attribution 4.0 International License.
### Citation Information
@inproceedings{poostchi-etal-2016-personer,
title = "{P}erso{NER}: {P}ersian Named-Entity Recognition",
author = "Poostchi, Hanieh and
Zare Borzeshi, Ehsan and
Abdous, Mohammad and
Piccardi, Massimo",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://www.aclweb.org/anthology/C16-1319",
pages = "3381--3389",
abstract = "Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network.",
}
### Contributions
Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset. | [
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classla/hr500k | classla | 2022-10-25T07:32:05Z | 547 | 0 | [
"task_categories:other",
"task_ids:lemmatization",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"language:hr",
"license:cc-by-sa-4.0",
"structure-prediction",
"normalization",
"tokenization",
"region:us"
] | [
"other"
] | 2022-03-02T23:29:22Z | ---
language:
- hr
license:
- cc-by-sa-4.0
task_categories:
- other
task_ids:
- lemmatization
- named-entity-recognition
- part-of-speech
tags:
- structure-prediction
- normalization
- tokenization
---
The hr500k training corpus contains 506,457 Croatian tokens manually annotated on the levels of tokenisation, sentence segmentation, morphosyntactic tagging, lemmatisation, named entities and dependency syntax.
On the sentence level, the dataset contains 20159 training samples, 1963 validation samples and 2672 test samples
across the respective data splits. Each sample represents a sentence and includes the following features:
sentence ID ('sent\_id'), sentence text ('text'), list of tokens ('tokens'), list of lemmas ('lemmas'),
list of MULTEXT-East tags ('xpos\_tags), list of UPOS tags ('upos\_tags'), list of morphological features ('feats'),
and list of IOB tags ('iob\_tags'). A subset of the data also contains universal dependencies ('ud') and consists of
7498 training samples, 649 validation samples, and 742 test samples.
Three dataset configurations are available, namely 'ner', 'upos', and 'ud', with the corresponding features
encoded as class labels. If the configuration is not specified, it defaults to 'ner'.
If you use this dataset in your research, please cite the following paper:
```
Bibtex @InProceedings{LJUBEI16.340,
author = {Nikola Ljubešić and Filip Klubička and Željko Agić and Ivo-Pavao Jazbec},
title = {New Inflectional Lexicons and Training Corpora for Improved Morphosyntactic Annotation of Croatian and Serbian},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
year = {2016},
month = {may},
date = {23-28},
location = {Portorož, Slovenia},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
address = {Paris, France},
isbn = {978-2-9517408-9-1},
language = {english}
}
``` | [
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pierreguillou/DocLayNet-small | pierreguillou | 2023-05-17T08:56:10Z | 544 | 8 | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_categories:token-classification",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:1K<n<10K",
"language:en",
"language:de",
"language:fr",
"language:ja",
"license:other",
"DocLayNet",
"COCO",
"PDF",
"IBM",
"Financial-Reports",
"Finance",
"Manuals",
"Scientific-Articles",
"Science",
"Laws",
"Law",
"Regulations",
"Patents",
"Government-Tenders",
"object-detection",
"image-segmentation",
"token-classification",
"arxiv:2206.01062",
"region:us"
] | [
"object-detection",
"image-segmentation",
"token-classification"
] | 2023-01-25T17:47:43Z | ---
language:
- en
- de
- fr
- ja
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet small
size_categories:
- 1K<n<10K
tags:
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
task_categories:
- object-detection
- image-segmentation
- token-classification
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet small
## About this card (01/27/2023)
### Property and license
All information from this page but the content of this paragraph "About this card (01/27/2023)" has been copied/pasted from [Dataset Card for DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet).
DocLayNet is a dataset created by Deep Search (IBM Research) published under [license CDLA-Permissive-1.0](https://huggingface.co/datasets/ds4sd/DocLayNet#licensing-information).
I do not claim any rights to the data taken from this dataset and published on this page.
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
### Processing into a format facilitating its use by HF notebooks
These 2 options require the downloading of all the data (approximately 30GBi), which requires downloading time (about 45 mn in Google Colab) and a large space on the hard disk. These could limit experimentation for people with low resources.
Moreover, even when using the download via HF datasets library, it is necessary to download the EXTRA zip separately ([doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip), 7.5 GiB) to associate the annotated bounding boxes with the text extracted by OCR from the PDFs. This operation also requires additional code because the boundings boxes of the texts do not necessarily correspond to those annotated (a calculation of the percentage of area in common between the boundings boxes annotated and those of the texts makes it possible to make a comparison between them).
At last, in order to use Hugging Face notebooks on fine-tuning layout models like LayoutLMv3 or LiLT, DocLayNet data must be processed in a proper format.
For all these reasons, I decided to process the DocLayNet dataset:
- into 3 datasets of different sizes:
- [DocLayNet small](https://huggingface.co/datasets/pierreguillou/DocLayNet-small) (about 1% of DocLayNet) < 1.000k document images (691 train, 64 val, 49 test)
- [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) (about 10% of DocLayNet) < 10.000k document images (6910 train, 648 val, 499 test)
- [DocLayNet large](https://huggingface.co/datasets/pierreguillou/DocLayNet-large) (about 100% of DocLayNet) < 100.000k document images (69.103 train, 6.480 val, 4.994 test)
- with associated texts and PDFs (base64 format),
- and in a format facilitating their use by HF notebooks.
*Note: the layout HF notebooks will greatly help participants of the IBM [ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents](https://ds4sd.github.io/icdar23-doclaynet/)!*
### About PDFs languages
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"We did not control the document selection with regard to language. **The vast majority of documents contained in DocLayNet (close to 95%) are published in English language.** However, **DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%)**. While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features."
### About PDFs categories distribution
Citation of the page 3 of the [DocLayNet paper](https://arxiv.org/abs/2206.01062):
"The pages in DocLayNet can be grouped into **six distinct categories**, namely **Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders**. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes."
### Download & overview
The size of the DocLayNet small is about 1% of the DocLayNet dataset (random selection respectively in the train, val and test files).
```
# !pip install -q datasets
from datasets import load_dataset
dataset_small = load_dataset("pierreguillou/DocLayNet-small")
# overview of dataset_small
DatasetDict({
train: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 691
})
validation: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 64
})
test: Dataset({
features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'pdf', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],
num_rows: 49
})
})
```
### Annotated bounding boxes
The DocLayNet base makes easy to display document image with the annotaed bounding boxes of paragraphes or lines.
Check the notebook [processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb](https://github.com/piegu/language-models/blob/master/processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb) in order to get the code.
#### Paragraphes
#### Lines
### HF notebooks
- [notebooks LayoutLM](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLM) (Niels Rogge)
- [notebooks LayoutLMv2](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2) (Niels Rogge)
- [notebooks LayoutLMv3](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv3) (Niels Rogge)
- [notebooks LiLT](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT) (Niels Rogge)
- [Document AI: Fine-tuning LiLT for document-understanding using Hugging Face Transformers](https://github.com/philschmid/document-ai-transformers/blob/main/training/lilt_funsd.ipynb) ([post](https://www.philschmid.de/fine-tuning-lilt#3-fine-tune-and-evaluate-lilt) of Phil Schmid)
## 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)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [[email protected]](mailto:[email protected]).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset. | [
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classla/copa_hr | classla | 2022-10-25T07:32:15Z | 543 | 0 | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:hr",
"license:cc-by-sa-4.0",
"causal-reasoning",
"textual-entailment",
"commonsense-reasoning",
"arxiv:2005.00333",
"arxiv:2104.09243",
"region:us"
] | [
"text-classification"
] | 2022-03-02T23:29:22Z | ---
language:
- hr
license:
- cc-by-sa-4.0
task_categories:
- text-classification
task_ids:
- natural-language-inference
tags:
- causal-reasoning
- textual-entailment
- commonsense-reasoning
---
The COPA-HR dataset (Choice of plausible alternatives in Croatian) is a translation
of the English COPA dataset (https://people.ict.usc.edu/~gordon/copa.html) by following the
XCOPA dataset translation methodology (https://arxiv.org/abs/2005.00333). The dataset consists of 1000 premises
(My body cast a shadow over the grass), each given a question (What is the cause?), and two choices
(The sun was rising; The grass was cut), with a label encoding which of the choices is more plausible
given the annotator or translator (The sun was rising).
The dataset is split into 400 training samples, 100 validation samples, and 500 test samples. It includes the
following features: 'premise', 'choice1', 'choice2', 'label', 'question', 'changed' (boolean).
If you use the dataset in your work, please cite
```
@article{DBLP:journals/corr/abs-2104-09243,
author = {Nikola Ljube\\\\v{s}i\\\\'{c} and
Davor Lauc},
title = {BERTi{\\\\'{c}} - The Transformer Language Model for Bosnian, Croatian,
Montenegrin and Serbian},
journal = {CoRR},
volume = {abs/2104.09243},
year = {2021},
url = {https://arxiv.org/abs/2104.09243},
archivePrefix = {arXiv},
}
``` | [
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recipe_nlg | null | 2023-01-25T14:43:04Z | 542 | 24 | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-retrieval",
"task_categories:summarization",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:explanation-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | [
"text2text-generation",
"text-generation",
"fill-mask",
"text-retrieval",
"summarization"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
- fill-mask
- text-retrieval
- summarization
task_ids:
- document-retrieval
- entity-linking-retrieval
- explanation-generation
- language-modeling
- masked-language-modeling
paperswithcode_id: recipenlg
pretty_name: RecipeNLG
dataset_info:
features:
- name: id
dtype: int32
- name: title
dtype: string
- name: ingredients
sequence: string
- name: directions
sequence: string
- name: link
dtype: string
- name: source
dtype:
class_label:
names:
'0': Gathered
'1': Recipes1M
- name: ner
sequence: string
splits:
- name: train
num_bytes: 2194783815
num_examples: 2231142
download_size: 0
dataset_size: 2194783815
---
# Dataset Card for RecipeNLG
## 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://recipenlg.cs.put.poznan.pl/
- **Repository:** https://github.com/Glorf/recipenlg
- **Paper:** https://www.aclweb.org/anthology/volumes/2020.inlg-1/
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation.
While the RecipeNLG dataset is based on the Recipe1M+ dataset, it greatly expands the number of recipes available.
The new dataset provides over 1 million new, preprocessed and deduplicated recipes on top of the Recipe1M+ dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```
{'id': 0,
'title': 'No-Bake Nut Cookies',
'ingredients': ['1 c. firmly packed brown sugar',
'1/2 c. evaporated milk',
'1/2 tsp. vanilla',
'1/2 c. broken nuts (pecans)',
'2 Tbsp. butter or margarine',
'3 1/2 c. bite size shredded rice biscuits'],
'directions': ['In a heavy 2-quart saucepan, mix brown sugar, nuts, evaporated milk and butter or margarine.',
'Stir over medium heat until mixture bubbles all over top.',
'Boil and stir 5 minutes more. Take off heat.',
'Stir in vanilla and cereal; mix well.',
'Using 2 teaspoons, drop and shape into 30 clusters on wax paper.',
'Let stand until firm, about 30 minutes.'],
'link': 'www.cookbooks.com/Recipe-Details.aspx?id=44874',
'source': 0,
'ner': ['brown sugar',
'milk',
'vanilla',
'nuts',
'butter',
'bite size shredded rice biscuits']}
```
### Data Fields
- `id` (`int`): ID.
- `title` (`str`): Title of the recipe.
- `ingredients` (`list` of `str`): Ingredients.
- `directions` (`list` of `str`): Instruction steps.
- `link` (`str`): URL link.
- `source` (`ClassLabel`): Origin of each recipe record, with possible value {"Gathered", "Recipes1M"}:
- "Gathered" (0): Additional recipes gathered from multiple cooking web pages, using automated scripts in a web scraping process.
- "Recipes1M" (1): Recipes from "Recipe1M+" dataset.
- `ner` (`list` of `str`): NER food entities.
### Data Splits
The dataset contains a single `train` split.
## 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
I (the "Researcher") have requested permission to use the RecipeNLG dataset (the "Dataset") at Poznań University of Technology (PUT). In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Dataset only for non-commercial research and educational purposes.
2. PUT makes no representations or warranties regarding the Dataset, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Dataset and shall defend and indemnify PUT, including its employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Dataset including but not limited to Researcher's use of any copies of copyrighted images or text that he or she may create from the Dataset.
4. Researcher may provide research associates and colleagues with access to the Dataset provided that they first agree to be bound by these terms and conditions.
5. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
### Citation Information
```bibtex
@inproceedings{bien-etal-2020-recipenlg,
title = "{R}ecipe{NLG}: A Cooking Recipes Dataset for Semi-Structured Text Generation",
author = "Bie{\'n}, Micha{\l} and
Gilski, Micha{\l} and
Maciejewska, Martyna and
Taisner, Wojciech and
Wisniewski, Dawid and
Lawrynowicz, Agnieszka",
booktitle = "Proceedings of the 13th International Conference on Natural Language Generation",
month = dec,
year = "2020",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.inlg-1.4",
pages = "22--28",
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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] |
open-llm-leaderboard/details_bigcode__starcoder | open-llm-leaderboard | 2023-08-28T21:18:40Z | 542 | 0 | [
"region:us"
] | null | 2023-08-28T09:54:21Z | ---
pretty_name: Evaluation run of None
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 119 configuration, each one coresponding to one of\
\ the evaluated task.\n\nThe dataset has been created from 3 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_bigcode__starcoder\"\
,\n\t\"original_mmlu_world_religions_5\",\n\tsplit=\"train\")\n```\n\n## Latest\
\ results\n\nThese are the [latest results from run 2023-08-28T21:18:29.614335](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__starcoder/blob/main/results_2023-08-28T21%3A18%3A29.614335.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 \"acc\": 0.3188725772362224,\n\
\ \"acc_stderr\": 0.03464051159620421\n },\n \"original|mmlu:abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394\n },\n\
\ \"original|mmlu:anatomy|5\": {\n \"acc\": 0.37037037037037035,\n \
\ \"acc_stderr\": 0.04171654161354543\n },\n \"original|mmlu:astronomy|5\"\
: {\n \"acc\": 0.3355263157894737,\n \"acc_stderr\": 0.038424985593952694\n\
\ },\n \"original|mmlu:business_ethics|5\": {\n \"acc\": 0.4,\n \
\ \"acc_stderr\": 0.04923659639173309\n },\n \"original|mmlu:clinical_knowledge|5\"\
: {\n \"acc\": 0.2981132075471698,\n \"acc_stderr\": 0.028152837942493868\n\
\ },\n \"original|mmlu:college_biology|5\": {\n \"acc\": 0.2708333333333333,\n\
\ \"acc_stderr\": 0.03716177437566017\n },\n \"original|mmlu:college_chemistry|5\"\
: {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036622\n },\n\
\ \"original|mmlu:college_computer_science|5\": {\n \"acc\": 0.34,\n \
\ \"acc_stderr\": 0.04760952285695235\n },\n \"original|mmlu:college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235\n },\n\
\ \"original|mmlu:college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\
\ \"acc_stderr\": 0.0326926380614177\n },\n \"original|mmlu:college_physics|5\"\
: {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617746\n\
\ },\n \"original|mmlu:computer_security|5\": {\n \"acc\": 0.51,\n\
\ \"acc_stderr\": 0.05024183937956911\n },\n \"original|mmlu:conceptual_physics|5\"\
: {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.03013590647851756\n\
\ },\n \"original|mmlu:econometrics|5\": {\n \"acc\": 0.3508771929824561,\n\
\ \"acc_stderr\": 0.044895393502707\n },\n \"original|mmlu:electrical_engineering|5\"\
: {\n \"acc\": 0.4068965517241379,\n \"acc_stderr\": 0.040937939812662374\n\
\ },\n \"original|mmlu:elementary_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.02256989707491841\n },\n \"original|mmlu:formal_logic|5\"\
: {\n \"acc\": 0.30158730158730157,\n \"acc_stderr\": 0.04104947269903394\n\
\ },\n \"original|mmlu:global_facts|5\": {\n \"acc\": 0.31,\n \
\ \"acc_stderr\": 0.04648231987117316\n },\n \"original|mmlu:high_school_biology|5\"\
: {\n \"acc\": 0.33225806451612905,\n \"acc_stderr\": 0.0267955608481228\n\
\ },\n \"original|mmlu:high_school_chemistry|5\": {\n \"acc\": 0.2413793103448276,\n\
\ \"acc_stderr\": 0.03010833071801162\n },\n \"original|mmlu:high_school_computer_science|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099\n },\n\
\ \"original|mmlu:high_school_european_history|5\": {\n \"acc\": 0.32727272727272727,\n\
\ \"acc_stderr\": 0.03663974994391242\n },\n \"original|mmlu:high_school_geography|5\"\
: {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.030532892233932015\n\
\ },\n \"original|mmlu:high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.29015544041450775,\n \"acc_stderr\": 0.03275264467791514\n\
\ },\n \"original|mmlu:high_school_macroeconomics|5\": {\n \"acc\"\
: 0.2564102564102564,\n \"acc_stderr\": 0.022139081103971534\n },\n \
\ \"original|mmlu:high_school_mathematics|5\": {\n \"acc\": 0.3111111111111111,\n\
\ \"acc_stderr\": 0.028226446749683512\n },\n \"original|mmlu:high_school_microeconomics|5\"\
: {\n \"acc\": 0.3067226890756303,\n \"acc_stderr\": 0.029953823891887037\n\
\ },\n \"original|mmlu:high_school_physics|5\": {\n \"acc\": 0.24503311258278146,\n\
\ \"acc_stderr\": 0.035118075718047245\n },\n \"original|mmlu:high_school_psychology|5\"\
: {\n \"acc\": 0.24403669724770644,\n \"acc_stderr\": 0.018415286351416406\n\
\ },\n \"original|mmlu:high_school_statistics|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.029886910547626974\n },\n \"original|mmlu:high_school_us_history|5\"\
: {\n \"acc\": 0.2696078431372549,\n \"acc_stderr\": 0.031145570659486782\n\
\ },\n \"original|mmlu:high_school_world_history|5\": {\n \"acc\":\
\ 0.2911392405063291,\n \"acc_stderr\": 0.029571601065753374\n },\n \
\ \"original|mmlu:human_aging|5\": {\n \"acc\": 0.3542600896860987,\n \
\ \"acc_stderr\": 0.03210062154134987\n },\n \"original|mmlu:human_sexuality|5\"\
: {\n \"acc\": 0.3435114503816794,\n \"acc_stderr\": 0.041649760719448786\n\
\ },\n \"original|mmlu:international_law|5\": {\n \"acc\": 0.45454545454545453,\n\
\ \"acc_stderr\": 0.04545454545454545\n },\n \"original|mmlu:jurisprudence|5\"\
: {\n \"acc\": 0.3055555555555556,\n \"acc_stderr\": 0.044531975073749834\n\
\ },\n \"original|mmlu:logical_fallacies|5\": {\n \"acc\": 0.26993865030674846,\n\
\ \"acc_stderr\": 0.03487825168497892\n },\n \"original|mmlu:machine_learning|5\"\
: {\n \"acc\": 0.3482142857142857,\n \"acc_stderr\": 0.04521829902833586\n\
\ },\n \"original|mmlu:management|5\": {\n \"acc\": 0.2815533980582524,\n\
\ \"acc_stderr\": 0.04453254836326469\n },\n \"original|mmlu:marketing|5\"\
: {\n \"acc\": 0.42735042735042733,\n \"acc_stderr\": 0.032408473935163266\n\
\ },\n \"original|mmlu:medical_genetics|5\": {\n \"acc\": 0.28,\n \
\ \"acc_stderr\": 0.04512608598542127\n },\n \"original|mmlu:miscellaneous|5\"\
: {\n \"acc\": 0.32567049808429116,\n \"acc_stderr\": 0.016757989458549682\n\
\ },\n \"original|mmlu:moral_disputes|5\": {\n \"acc\": 0.3901734104046243,\n\
\ \"acc_stderr\": 0.026261677607806646\n },\n \"original|mmlu:moral_scenarios|5\"\
: {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889\n\
\ },\n \"original|mmlu:nutrition|5\": {\n \"acc\": 0.3627450980392157,\n\
\ \"acc_stderr\": 0.027530078447110303\n },\n \"original|mmlu:philosophy|5\"\
: {\n \"acc\": 0.3665594855305466,\n \"acc_stderr\": 0.027368078243971635\n\
\ },\n \"original|mmlu:prehistory|5\": {\n \"acc\": 0.33641975308641975,\n\
\ \"acc_stderr\": 0.026289734945952926\n },\n \"original|mmlu:professional_accounting|5\"\
: {\n \"acc\": 0.3049645390070922,\n \"acc_stderr\": 0.027464708442022128\n\
\ },\n \"original|mmlu:professional_law|5\": {\n \"acc\": 0.29595827900912647,\n\
\ \"acc_stderr\": 0.011658518525277049\n },\n \"original|mmlu:professional_medicine|5\"\
: {\n \"acc\": 0.20220588235294118,\n \"acc_stderr\": 0.02439819298665492\n\
\ },\n \"original|mmlu:professional_psychology|5\": {\n \"acc\": 0.2826797385620915,\n\
\ \"acc_stderr\": 0.018217269552053432\n },\n \"original|mmlu:public_relations|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.0469237132203465\n },\n\
\ \"original|mmlu:security_studies|5\": {\n \"acc\": 0.40408163265306124,\n\
\ \"acc_stderr\": 0.0314147080258659\n },\n \"original|mmlu:sociology|5\"\
: {\n \"acc\": 0.417910447761194,\n \"acc_stderr\": 0.034875586404620636\n\
\ },\n \"original|mmlu:us_foreign_policy|5\": {\n \"acc\": 0.43,\n\
\ \"acc_stderr\": 0.049756985195624284\n },\n \"original|mmlu:virology|5\"\
: {\n \"acc\": 0.35542168674698793,\n \"acc_stderr\": 0.03726214354322415\n\
\ },\n \"original|mmlu:world_religions|5\": {\n \"acc\": 0.2982456140350877,\n\
\ \"acc_stderr\": 0.03508771929824565\n }\n}\n```"
repo_url: https://huggingface.co/None
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_28T09_53_59.312863
path:
- '**/details_harness|arc:challenge|25_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hellaswag|10_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-28T09:53:59.312863.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T09:53:59.312863.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-28T09:53:59.312863.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-28T09:53:59.312863.parquet'
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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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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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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path:
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path:
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path:
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path:
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path:
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- split: latest
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_jurisprudence_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_logical_fallacies_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_machine_learning_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_management_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:management|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:management|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:management|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_marketing_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_medical_genetics_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_miscellaneous_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_moral_disputes_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_moral_scenarios_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_nutrition_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_philosophy_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_prehistory_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_professional_accounting_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_professional_law_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_professional_medicine_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_professional_psychology_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_public_relations_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_security_studies_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_sociology_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_us_foreign_policy_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:virology|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:virology|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-28T21:18:29.614335.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T21_17_20.453695
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T21:17:20.453695.parquet'
- split: 2023_08_28T21_18_29.614335
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T21:18:29.614335.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T21:18:29.614335.parquet'
- config_name: results
data_files:
- split: 2023_08_28T09_53_59.312863
path:
- results_2023-08-28T09:53:59.312863.parquet
- split: 2023_08_28T21_17_20.453695
path:
- results_2023-08-28T21:17:20.453695.parquet
- split: 2023_08_28T21_18_29.614335
path:
- results_2023-08-28T21:18:29.614335.parquet
- split: latest
path:
- results_2023-08-28T21:18:29.614335.parquet
---
# Dataset Card for Evaluation run of None
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/None
- **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 [None](https://huggingface.co/None) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 119 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 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_bigcode__starcoder",
"original_mmlu_world_religions_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-28T21:18:29.614335](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__starcoder/blob/main/results_2023-08-28T21%3A18%3A29.614335.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": {
"acc": 0.3188725772362224,
"acc_stderr": 0.03464051159620421
},
"original|mmlu:abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394
},
"original|mmlu:anatomy|5": {
"acc": 0.37037037037037035,
"acc_stderr": 0.04171654161354543
},
"original|mmlu:astronomy|5": {
"acc": 0.3355263157894737,
"acc_stderr": 0.038424985593952694
},
"original|mmlu:business_ethics|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309
},
"original|mmlu:clinical_knowledge|5": {
"acc": 0.2981132075471698,
"acc_stderr": 0.028152837942493868
},
"original|mmlu:college_biology|5": {
"acc": 0.2708333333333333,
"acc_stderr": 0.03716177437566017
},
"original|mmlu:college_chemistry|5": {
"acc": 0.19,
"acc_stderr": 0.03942772444036622
},
"original|mmlu:college_computer_science|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235
},
"original|mmlu:college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235
},
"original|mmlu:college_medicine|5": {
"acc": 0.24277456647398843,
"acc_stderr": 0.0326926380614177
},
"original|mmlu:college_physics|5": {
"acc": 0.20588235294117646,
"acc_stderr": 0.04023382273617746
},
"original|mmlu:computer_security|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911
},
"original|mmlu:conceptual_physics|5": {
"acc": 0.30638297872340425,
"acc_stderr": 0.03013590647851756
},
"original|mmlu:econometrics|5": {
"acc": 0.3508771929824561,
"acc_stderr": 0.044895393502707
},
"original|mmlu:electrical_engineering|5": {
"acc": 0.4068965517241379,
"acc_stderr": 0.040937939812662374
},
"original|mmlu:elementary_mathematics|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.02256989707491841
},
"original|mmlu:formal_logic|5": {
"acc": 0.30158730158730157,
"acc_stderr": 0.04104947269903394
},
"original|mmlu:global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316
},
"original|mmlu:high_school_biology|5": {
"acc": 0.33225806451612905,
"acc_stderr": 0.0267955608481228
},
"original|mmlu:high_school_chemistry|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.03010833071801162
},
"original|mmlu:high_school_computer_science|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099
},
"original|mmlu:high_school_european_history|5": {
"acc": 0.32727272727272727,
"acc_stderr": 0.03663974994391242
},
"original|mmlu:high_school_geography|5": {
"acc": 0.24242424242424243,
"acc_stderr": 0.030532892233932015
},
"original|mmlu:high_school_government_and_politics|5": {
"acc": 0.29015544041450775,
"acc_stderr": 0.03275264467791514
},
"original|mmlu:high_school_macroeconomics|5": {
"acc": 0.2564102564102564,
"acc_stderr": 0.022139081103971534
},
"original|mmlu:high_school_mathematics|5": {
"acc": 0.3111111111111111,
"acc_stderr": 0.028226446749683512
},
"original|mmlu:high_school_microeconomics|5": {
"acc": 0.3067226890756303,
"acc_stderr": 0.029953823891887037
},
"original|mmlu:high_school_physics|5": {
"acc": 0.24503311258278146,
"acc_stderr": 0.035118075718047245
},
"original|mmlu:high_school_psychology|5": {
"acc": 0.24403669724770644,
"acc_stderr": 0.018415286351416406
},
"original|mmlu:high_school_statistics|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.029886910547626974
},
"original|mmlu:high_school_us_history|5": {
"acc": 0.2696078431372549,
"acc_stderr": 0.031145570659486782
},
"original|mmlu:high_school_world_history|5": {
"acc": 0.2911392405063291,
"acc_stderr": 0.029571601065753374
},
"original|mmlu:human_aging|5": {
"acc": 0.3542600896860987,
"acc_stderr": 0.03210062154134987
},
"original|mmlu:human_sexuality|5": {
"acc": 0.3435114503816794,
"acc_stderr": 0.041649760719448786
},
"original|mmlu:international_law|5": {
"acc": 0.45454545454545453,
"acc_stderr": 0.04545454545454545
},
"original|mmlu:jurisprudence|5": {
"acc": 0.3055555555555556,
"acc_stderr": 0.044531975073749834
},
"original|mmlu:logical_fallacies|5": {
"acc": 0.26993865030674846,
"acc_stderr": 0.03487825168497892
},
"original|mmlu:machine_learning|5": {
"acc": 0.3482142857142857,
"acc_stderr": 0.04521829902833586
},
"original|mmlu:management|5": {
"acc": 0.2815533980582524,
"acc_stderr": 0.04453254836326469
},
"original|mmlu:marketing|5": {
"acc": 0.42735042735042733,
"acc_stderr": 0.032408473935163266
},
"original|mmlu:medical_genetics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127
},
"original|mmlu:miscellaneous|5": {
"acc": 0.32567049808429116,
"acc_stderr": 0.016757989458549682
},
"original|mmlu:moral_disputes|5": {
"acc": 0.3901734104046243,
"acc_stderr": 0.026261677607806646
},
"original|mmlu:moral_scenarios|5": {
"acc": 0.2424581005586592,
"acc_stderr": 0.014333522059217889
},
"original|mmlu:nutrition|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.027530078447110303
},
"original|mmlu:philosophy|5": {
"acc": 0.3665594855305466,
"acc_stderr": 0.027368078243971635
},
"original|mmlu:prehistory|5": {
"acc": 0.33641975308641975,
"acc_stderr": 0.026289734945952926
},
"original|mmlu:professional_accounting|5": {
"acc": 0.3049645390070922,
"acc_stderr": 0.027464708442022128
},
"original|mmlu:professional_law|5": {
"acc": 0.29595827900912647,
"acc_stderr": 0.011658518525277049
},
"original|mmlu:professional_medicine|5": {
"acc": 0.20220588235294118,
"acc_stderr": 0.02439819298665492
},
"original|mmlu:professional_psychology|5": {
"acc": 0.2826797385620915,
"acc_stderr": 0.018217269552053432
},
"original|mmlu:public_relations|5": {
"acc": 0.4,
"acc_stderr": 0.0469237132203465
},
"original|mmlu:security_studies|5": {
"acc": 0.40408163265306124,
"acc_stderr": 0.0314147080258659
},
"original|mmlu:sociology|5": {
"acc": 0.417910447761194,
"acc_stderr": 0.034875586404620636
},
"original|mmlu:us_foreign_policy|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284
},
"original|mmlu:virology|5": {
"acc": 0.35542168674698793,
"acc_stderr": 0.03726214354322415
},
"original|mmlu:world_religions|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.03508771929824565
}
}
```
### 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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jondurbin/airoboros-2.2.1 | jondurbin | 2023-09-18T21:22:40Z | 542 | 24 | [
"license:other",
"region:us"
] | null | 2023-09-15T10:20:36Z | ---
license: other
---
## Overview
This dataset is a slight update to 2.2.
### Re-generated writing responses
Many of the responses were generated by gpt-4-0613, which unfortunately produces much shorter and "dumber" (i.e. various readability scores increased compared to gpt-4-0314, e.g. Flesch, Gunning Fog, etc.) responses compared to gpt-4-0314.
I have re-created many of these responses, using gpt-4-0314, temperature 0.7, and the following prompt (which produced 3-5x longer responses):
```
You are to emulate a world class, intelligent author who uses a diverse, interesting vocabulary to completely captivate the audience with brilliant and interesting writing.
You provide very lengthy and detailed responses.
Remember, you are to emulate a human writer, so the output should not sound like it was written by an AI.
Avoid using any cliche/obvious phrases, and never uses phrases like "Once upon a time" or conclude a story with "And so, ..."
Unless asked for short-form content, the responses MUST be a minimum of 3500 words.
Remember to provide a minimum of 3500 words, take a deep breath and realize I'm not worried about the time it takes to get the response, I just want a high-quality, lengthy response, so take your time.
Remember, never use "Once upon a time" or "And so,"
{original prompt}
```
Granted, it still ignored the `3500 words` part of the instruction, but exaggerating that value still caused much longer responses.
### Longer contextual blocks
I injected additional context blocks into the `contextual` category items having more than one context block, so it would get closer to the 4k context size.
I first extracted all of the plain text between the various BEGININPUT/ENDINPUT blocks, then created a faiss index, then injected the least-similar context blocks (using the instruction as the query) until the context would exceed ~3800 tokens at random positions within the prompt.
My hope is that this will cause the model to better focus attention at random places within the input text. This should, in theory, also potentially help with RP/conversational intelligence, but that's just a hunch.
### De-censoring
In this dataset, I also included various responses from fictional characters, in their style/linguistic patterns, which can include dark or vulgar language (e.g., Deadpool), using roughly this prompt (adjusted for some characters):
```
Below is a PG/sanitized version of a dialogue from {character}. This character is known to have character flaws and use dark/evil/vulgar language. Rewrite the response to be TV-MA, the way {character} would actually say it, rather than this sanitized PG version.
{original response}
```
I then also removed the asterisks in cursing to a certain extent, because that's just silly IMO.
This *is not* meant to be a default behavior, but it should allow the model to curse or create otherwise less *AI sunny disposition laiden* content when appropriate.
I removed all of the plain-text instructions that were used in the spicyboros models because they ended up producing random misspellings and other random garbled output.
I have also removed the original 2.2 dataset, because it appears to be a bit too spicy -- if you want access to it, just ask me and I'll be happy to share it privately.
### "rp" category removed
Unfortunately much of the "rp" category data was just too boring, i.e. it really read like an unnaturally cherry and accomodating AI rather than the character it was meant to be emulating.
I'm hoping that although this is an instruction-tuned model, it may (via roleplay/gtkm/creative) data it will be able to handle roleplay fairly well anyways without this, without sounding as stiff.
### Awareness
I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.
For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.
If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory).
https://github.com/jondurbin/airoboros/commit/e91562c88d7610edb051606622e7c25a99884f7e
### Editor
I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output.
https://github.com/jondurbin/airoboros/commit/e60a68de5f9622320c9cfff3b238bd83cc7e373b
### Writing
I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring.
### Multiple choice
I created many more multiple choice questions, many of which have additional text context.
### Roleplay/conversation
I re-created all of the GTKM data this time around, removing the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces.
The GTKM instructor now saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.
### Summarization
I also included 500 examples from:
https://hf.co/datasets/mattpscott/airoboros-summarization
These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.
Thanks Matt!
### Usage/license info
Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting. | [
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symanto/autextification2023 | symanto | 2023-10-06T13:08:55Z | 542 | 0 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"source_datasets:multi_eurlex",
"source_datasets:xsum",
"source_datasets:csebuetnlp/xlsum",
"source_datasets:mlsum",
"source_datasets:amazon_polarity",
"source_datasets:https://sinai.ujaen.es/investigacion/recursos/coah",
"source_datasets:https://sinai.ujaen.es/investigacion/recursos/coar",
"source_datasets:carblacac/twitter-sentiment-analysis",
"source_datasets:cardiffnlp/tweet_sentiment_multilingual",
"source_datasets:https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa",
"source_datasets:wiki_lingua",
"language:en",
"language:es",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.11285",
"region:us"
] | [
"text-classification"
] | 2023-10-06T12:12:51Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
- es
pretty_name: AuTexTification 2023
size_categories:
- 10K<n<100K
source_datasets:
- multi_eurlex
- xsum
- csebuetnlp/xlsum
- mlsum
- amazon_polarity
- https://sinai.ujaen.es/investigacion/recursos/coah
- https://sinai.ujaen.es/investigacion/recursos/coar
- carblacac/twitter-sentiment-analysis
- cardiffnlp/tweet_sentiment_multilingual
- https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa
- wiki_lingua
---
# Dataset Card for AuTexTification 2023
## Dataset Description
- **Homepage:** https://sites.google.com/view/autextification
- **Repository:** https://github.com/autextification/AuTexTification-Overview
- **Paper:** https://arxiv.org/abs/2309.11285
### Dataset Summary
AuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish.
The dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents.
The generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003.
For more information, please refer to our overview paper: https://arxiv.org/abs/2309.11285
### Supported Tasks and Leaderboards
- Machine-Generated Text Detection
- Model Attribution
### Languages
English and Spanish
## Dataset Structure
### Data Instances
163k instances of labeled text in total.
### Data Fields
For MGT Detection:
- id
- prompt
- text
- label
- model
- domain
For Model Attribution:
- id
- prompt
- text
- label
- domain
### Data Splits
- MGT Detection Data:
| Language | Split | Human | Generated | Total |
| -------- | ----- | ------ | --------- | ------ |
| English | Train | 17.046 | 16.799 | 33.845 |
| | Test | 10.642 | 11.190 | 21.832 |
| | Total | 27.688 | 27.989 | |
| Spanish | Train | 15.787 | 16.275 | 32.062 |
| | Test | 11.209 | 8.920 | 20.129 |
| | Total | 26.996 | 25.195 | |
- Model Attribution Data:
| | | BLOOM | | | GPT | | | |
| -------- | ----- | ----- | ----- | ----- | ------- | ----- | ---------------- | ------ |
| Language | Split | 1B7 | 3B | 7B | babbage | curie | text-davinci-003 | Total |
| English | Train | 3.562 | 3.648 | 3.687 | 3.870 | 3.822 | 3.827 | 14.767 |
| | Test | 887 | 875 | 952 | 924 | 979 | 988 | 3.638 |
| | Total | 4.449 | 4.523 | 4.639 | 4.794 | 4.801 | 4.815 | |
| Spanish | Train | 3.422 | 3.514 | 3.575 | 3.788 | 3.770 | 3.866 | 14.299 |
| | Test | 870 | 867 | 878 | 946 | 1.004 | 917 | 3.561 |
| | Total | 4.292 | 4.381 | 4.453 | 4.734 | 4.774 | 4.783 | |
## Dataset Creation
### Curation Rationale
Human data was gathered and used to prompt language models, obtaining generated data.
Specific decisions were made to ensure the data gathering process was carried out in an unbiased manner, making the final human and generated texts probable continuations of a given prefix.
For more detailed information, please refer to the overview paper: https://arxiv.org/abs/2309.11285
### Source Data
The following datasets were used as human text:
- multi_eurlex
- xsum
- csebuetnlp/xlsum
- mlsum
- amazon_polarity
- https://sinai.ujaen.es/investigacion/recursos/coah
- https://sinai.ujaen.es/investigacion/recursos/coar
- carblacac/twitter-sentiment-analysis
- cardiffnlp/tweet_sentiment_multilingual
- https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa
- wiki_lingua
These datasets were only used as sources of human text. The labels of the datasets were not employed in any manner.
### Licensing Information
CC-BY-NC-SA-4.0
### Citation Information
```
@inproceedings{autextification2023,
title = "Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains",
author = "Sarvazyan, Areg Mikael and
Gonz{\'a}lez, Jos{\'e} {\'A}ngel and
Franco-Salvador, Marc and
Rangel, Francisco and
Chulvi, Berta and
Rosso, Paolo",
month = sep,
year = "2023",
address = "Jaén, Spain",
booktitle = "Procesamiento del Lenguaje Natural",
}
``` | [
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] |
classla/ssj500k | classla | 2022-10-28T05:37:22Z | 541 | 0 | [
"task_categories:token-classification",
"task_ids:lemmatization",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"task_ids:part-of-speech",
"language:sl",
"license:cc-by-sa-4.0",
"structure-prediction",
"tokenization",
"dependency-parsing",
"region:us"
] | [
"token-classification"
] | 2022-03-02T23:29:22Z | ---
language:
- sl
license:
- cc-by-sa-4.0
task_categories:
- token-classification
task_ids:
- lemmatization
- named-entity-recognition
- parsing
- part-of-speech
tags:
- structure-prediction
- tokenization
- dependency-parsing
---
The dataset contains 7432 training samples, 1164 validation samples and 893 test samples.
Each sample represents a sentence and includes the following features: sentence ID ('sent\_id'),
list of tokens ('tokens'), list of lemmas ('lemmas'),
list of Multext-East tags ('xpos\_tags), list of UPOS tags ('upos\_tags'), list of morphological features ('feats'),
list of IOB tags ('iob\_tags'), and list of universal dependency tags ('uds'). Three dataset configurations are
available, where the corresponding features are encoded as class labels: 'ner', 'upos', and 'ud'. | [
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lucasmccabe/logiqa | lucasmccabe | 2023-02-08T01:51:31Z | 541 | 3 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | [
"question-answering"
] | 2023-01-12T04:14:53Z | ---
task_categories:
- question-answering
language:
- en
pretty_name: LogiQA
size_categories:
- 1K<n<10K
paperswithcode_id: logiqa
dataset_info:
features:
- name: context
dtype: string
- name: query
dtype: string
- name: options
sequence:
dtype: string
- name: correct_option
dtype: string
splits:
- name: train
num_examples: 7376
- name: validation
num_examples: 651
- name: test
num_examples: 651
---
# Dataset Card for LogiQA
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidates’ critical thinking and problem solving. This dataset includes the English versions only; the Chinese versions are available via the homepage/original source.
## Dataset Structure
### Data Instances
An example from `train` looks as follows:
```
{'context': 'Continuous exposure to indoor fluorescent lights is beneficial to the health of hamsters with heart disease. One group of hamsters exposed to continuous exposure to fluorescent lights has an average lifespan that is 2.5% longer than another one of the same species but living in a black wall.',
'query': 'Which of the following questions was the initial motivation for conducting the above experiment?',
'options': ['Can hospital light therapy be proved to promote patient recovery?',
'Which one lives longer, the hamster living under the light or the hamster living in the dark?',
'What kind of illness does the hamster have?',
'Do some hamsters need a period of darkness?'],
'correct_option': 0}
```
### Data Fields
- `context`: a `string` feature.
- `query`: a `string` feature.
- `answers`: a `list` feature containing `string` features.
- `correct_option`: a `string` feature.
### Data Splits
|train|validation|test|
|----:|---------:|---:|
| 7376| 651| 651|
## Additional Information
### Dataset Curators
The original LogiQA was produced by Jian Liu, Leyang Cui , Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang.
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{liu2020logiqa,
title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning},
author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue},
journal={arXiv preprint arXiv:2007.08124},
year={2020}
}
```
### Contributions
[@lucasmccabe](https://github.com/lucasmccabe) added this dataset. | [
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LeoLM/OpenSchnabeltier | LeoLM | 2023-08-27T19:03:20Z | 541 | 3 | [
"region:us"
] | null | 2023-08-25T13:06:27Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: instruction_de
dtype: string
- name: output_de
dtype: string
- name: translation_de
dtype: string
splits:
- name: train
num_bytes: 66379650.83254641
num_examples: 21749
download_size: 33021431
dataset_size: 66379650.83254641
---
# Dataset Card for "open_platypus_de"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] |
mteb/toxic_conversations_50k | mteb | 2022-09-27T19:14:35Z | 540 | 3 | [
"language:en",
"region:us"
] | null | 2022-05-26T17:47:49Z | ---
language:
- en
---
# Toxic Conversation
This is a version of the [Jigsaw Unintended Bias in Toxicity Classification dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview). It contains comments from the Civil Comments platform together with annotations if the comment is toxic or not.
This dataset just contains the first 50k training examples.
10 annotators annotated each example and, as recommended in the task page, set a comment as toxic when target >= 0.5
The dataset is inbalanced, with only about 8% of the comments marked as toxic.
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open-llm-leaderboard/details_tiiuae__falcon-40b | open-llm-leaderboard | 2023-12-03T19:46:05Z | 540 | 0 | [
"region:us"
] | null | 2023-08-21T11:07:51Z | ---
pretty_name: Evaluation run of tiiuae/falcon-40b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 124 configuration, each one coresponding to one of\
\ the evaluated task.\n\nThe dataset has been created from 6 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 aggregated 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_tiiuae__falcon-40b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:45:58.201621](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b/blob/main/results_2023-12-03T19-45-58.201621.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 \"acc\": 0.21455648218347234,\n\
\ \"acc_stderr\": 0.011307604104052885\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.21455648218347234,\n \"acc_stderr\": 0.011307604104052885\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-40b
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_21T22_49_59.134750
path:
- '**/details_harness|arc:challenge|25_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_08T21_43_04.856041
path:
- '**/details_harness|drop|3_2023-09-08T21-43-04.856041.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-08T21-43-04.856041.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_08T21_43_04.856041
path:
- '**/details_harness|gsm8k|5_2023-09-08T21-43-04.856041.parquet'
- split: 2023_12_03T19_45_58.201621
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-45-58.201621.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-45-58.201621.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hellaswag|10_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_0
data_files:
- split: 2023_08_21T11_07_51.058817
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:07:51.058817.parquet'
- split: 2023_08_21T11_30_10.858708
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_0
data_files:
- split: 2023_08_21T11_07_51.058817
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:07:51.058817.parquet'
- split: 2023_08_21T11_30_10.858708
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_winogrande_5
data_files:
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path:
- '**/details_harness|winogrande|5_2023-09-08T21-43-04.856041.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-08T21-43-04.856041.parquet'
- config_name: original_mmlu_5
data_files:
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path:
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- '**/details_original|mmlu:astronomy|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:business_ethics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:college_biology|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:college_medicine|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:college_physics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:computer_security|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:econometrics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:17:39.708485.parquet'
- '**/details_original|mmlu:international_law|5_2023-08-28T20:17:39.708485.parquet'
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---
# Dataset Card for Evaluation run of tiiuae/falcon-40b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-40b
- **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 [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 124 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 6 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 aggregated 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_tiiuae__falcon-40b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:45:58.201621](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b/blob/main/results_2023-12-03T19-45-58.201621.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": {
"acc": 0.21455648218347234,
"acc_stderr": 0.011307604104052885
},
"harness|gsm8k|5": {
"acc": 0.21455648218347234,
"acc_stderr": 0.011307604104052885
}
}
```
### 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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language-and-voice-lab/althingi_asr | language-and-voice-lab | 2023-02-24T22:14:42Z | 539 | 0 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:is",
"license:cc-by-4.0",
"icelandic",
"parliamentary speech",
"parlament",
"althingi",
"region:us"
] | [
"automatic-speech-recognition"
] | 2022-12-09T20:33:28Z | ---
annotations_creators:
- machine-generated
language:
- is
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Althingi Parliamentary Speech
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- icelandic
- parliamentary speech
- parlament
- althingi
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for althingi_asr
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Data](#data)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Other Known Limitations](#other-known-limitations)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** Althingi Parliamentary Speech
- **Repository:** [LDC](https://catalog.ldc.upenn.edu/LDC2021S01)
- **Paper:** [Building an ASR corpus using Althingi’s Parliamentary Speeches](https://www.researchgate.net/profile/Jon-Gudnason/publication/319185185_Building_an_ASR_Corpus_Using_Althingi's_Parliamentary_Speeches/links/5d1dbdd3a6fdcc2462bdda0f/Building-an-ASR-Corpus-Using-Althingis-Parliamentary-Speeches.pdf)
- **Point of Contact:** [Jón Guðnason](mailto:[email protected])
### Dataset Summary
Althingi Parliamentary Speech consists of approximately 542 hours of recorded speech from Althingi, the Icelandic Parliament, along with corresponding transcripts, a pronunciation dictionary and two language models. Speeches date from 2005-2016.
This dataset was collected in 2016 by the ASR for Althingi project at [Reykjavik University](https://en.ru.is/) in collaboration with the Althingi speech department. The purpose of that project was to develop an ASR (automatic speech recognition) system for parliamentary speech to replace the procedure of manually transcribing performed speeches.
### Data
The mean speech length is six minutes, with speeches ranging from under one minute to around thirty minutes. The corpus features 197 speakers (105 male, 92 female) and is split into training, development and evaluation sets. The language models are of two types: a pruned trigram model, used in decoding, and an unpruned constant ARPA 5-gram model, used for re-scoring decoding results.
Audio data is presented as single channel 16-bit mp3 files; the majority of these files have a sample rate of 44.1 kHz. Transcripts and other text data are plain text encoded in UTF-8.
### Example Usage
The Althingi Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:
```python
from datasets import load_dataset
althingi_asr = load_dataset("language-and-voice-lab/althingi_asr")
```
To load an specific split (for example, the validation split) do:
```python
from datasets import load_dataset
althingi_asr = load_dataset("language-and-voice-lab/althingi_asr",split="validation")
```
### Supported Tasks
automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### Languages
The audio is in Icelandic.
## Dataset Structure
### Data Instances
```python
{
'audio_id': 'rad20160602T000219_00083',
'audio': {
'path': '/home/inga/.cache/HuggingFace/datasets/downloads/extracted/52607f9db9e3394263070575d29323213b99a06a996c43d4fe75bca115827d12/dev/EyH/rad20160602T000219/rad20160602T000219_00083.flac',
'array': array([-0.01098633, -0.01489258, -0.01040649, ..., 0.00314331,
0.00186157, 0.00527954], dtype=float32),
'sampling_rate': 16000
},
'speaker_id': 'rad20160602T000219',
'duration': 12.67199993133545,
'normalized_text': 'og má svo sannarlega segja að landslagið sé nokkuð breytt frá því þrjú komma tvö prósent þjóðarinnar töldust vera innflytjendur árið tvö þúsund en nú teljast tíu prósent þjóðarinnar vera fyrsta og önnur kynslóð innflytjenda'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `speaker_id` (string) - id of speaker
* `duration` (float32) - duration of the audio file in seconds.
* `normalized_text` (string) - normalized audio segment transcription.
### Data Splits
The corpus is split into train, evaluation, and test portions. Lenghts of every portion are: train = 514h29m, test = 13h52m, evaluation=14h02m.
To load an specific portion please see the above section "Example Usage".
## Additional Information
### Other Known Limitations
"Althingi Parliamentary Speech" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
### Licensing Information
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@misc{helgadottiralthingi2021,
title={Althingi Parliamentary Speech},
ldc_catalog_no={LDC2021S01},
DOI={https://doi.org/10.35111/695b-6697},
author={Helgadóttir, Inga Rún and Kjaran, Róbert and Nikulásdóttir, Anna Björk and Guðnason, Jón},
publisher={Reykjavík University}
journal={Linguistic Data Consortium, Philadelphia},
year={2021},
url={https://catalog.ldc.upenn.edu/LDC2021S01},
}
```
### Contributions
This project was made possible through the support of Althingi’s information and publications departments. The authors would like to thank Solveig K. Jónsdóttir, Þorbjörg Árnadóttir and Ingvi Stígsson for their valuable help.
| [
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hails/bigbench | hails | 2023-11-17T16:05:10Z | 539 | 1 | [
"region:us"
] | null | 2023-10-03T19:55:51Z | ---
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- config_name: understanding_fables_zero_shot
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- config_name: which_wiki_edit_zero_shot
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- config_name: word_sorting_zero_shot
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configs:
- config_name: abstract_narrative_understanding_zero_shot
data_files:
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path: abstract_narrative_understanding_zero_shot/default-*
- split: train
path: abstract_narrative_understanding_zero_shot/train-*
- split: validation
path: abstract_narrative_understanding_zero_shot/validation-*
- config_name: anachronisms_zero_shot
data_files:
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path: anachronisms_zero_shot/default-*
- split: train
path: anachronisms_zero_shot/train-*
- split: validation
path: anachronisms_zero_shot/validation-*
- config_name: analogical_similarity_zero_shot
data_files:
- split: default
path: analogical_similarity_zero_shot/default-*
- split: train
path: analogical_similarity_zero_shot/train-*
- split: validation
path: analogical_similarity_zero_shot/validation-*
- config_name: analytic_entailment_zero_shot
data_files:
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path: analytic_entailment_zero_shot/default-*
- split: train
path: analytic_entailment_zero_shot/train-*
- split: validation
path: analytic_entailment_zero_shot/validation-*
- config_name: arithmetic_zero_shot
data_files:
- split: default
path: arithmetic_zero_shot/default-*
- split: train
path: arithmetic_zero_shot/train-*
- split: validation
path: arithmetic_zero_shot/validation-*
- config_name: ascii_word_recognition_zero_shot
data_files:
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path: ascii_word_recognition_zero_shot/default-*
- split: train
path: ascii_word_recognition_zero_shot/train-*
- split: validation
path: ascii_word_recognition_zero_shot/validation-*
- config_name: authorship_verification_zero_shot
data_files:
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path: authorship_verification_zero_shot/default-*
- split: train
path: authorship_verification_zero_shot/train-*
- split: validation
path: authorship_verification_zero_shot/validation-*
- config_name: auto_categorization_zero_shot
data_files:
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path: auto_categorization_zero_shot/default-*
- split: train
path: auto_categorization_zero_shot/train-*
- split: validation
path: auto_categorization_zero_shot/validation-*
- config_name: auto_debugging_zero_shot
data_files:
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path: auto_debugging_zero_shot/default-*
- split: train
path: auto_debugging_zero_shot/train-*
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path: auto_debugging_zero_shot/validation-*
- config_name: bbq_lite_json_zero_shot
data_files:
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path: bbq_lite_json_zero_shot/default-*
- split: train
path: bbq_lite_json_zero_shot/train-*
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path: bbq_lite_json_zero_shot/validation-*
- config_name: bridging_anaphora_resolution_barqa_zero_shot
data_files:
- split: default
path: bridging_anaphora_resolution_barqa_zero_shot/default-*
- split: train
path: bridging_anaphora_resolution_barqa_zero_shot/train-*
- split: validation
path: bridging_anaphora_resolution_barqa_zero_shot/validation-*
- config_name: causal_judgment_zero_shot
data_files:
- split: default
path: causal_judgment_zero_shot/default-*
- split: train
path: causal_judgment_zero_shot/train-*
- split: validation
path: causal_judgment_zero_shot/validation-*
- config_name: cause_and_effect_zero_shot
data_files:
- split: default
path: cause_and_effect_zero_shot/default-*
- split: train
path: cause_and_effect_zero_shot/train-*
- split: validation
path: cause_and_effect_zero_shot/validation-*
- config_name: checkmate_in_one_zero_shot
data_files:
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path: checkmate_in_one_zero_shot/default-*
- split: train
path: checkmate_in_one_zero_shot/train-*
- split: validation
path: checkmate_in_one_zero_shot/validation-*
- config_name: chess_state_tracking_zero_shot
data_files:
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path: chess_state_tracking_zero_shot/default-*
- split: train
path: chess_state_tracking_zero_shot/train-*
- split: validation
path: chess_state_tracking_zero_shot/validation-*
- config_name: chinese_remainder_theorem_zero_shot
data_files:
- split: default
path: chinese_remainder_theorem_zero_shot/default-*
- split: train
path: chinese_remainder_theorem_zero_shot/train-*
- split: validation
path: chinese_remainder_theorem_zero_shot/validation-*
- config_name: cifar10_classification_zero_shot
data_files:
- split: default
path: cifar10_classification_zero_shot/default-*
- split: train
path: cifar10_classification_zero_shot/train-*
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path: cifar10_classification_zero_shot/validation-*
- config_name: code_line_description_zero_shot
data_files:
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path: code_line_description_zero_shot/default-*
- split: train
path: code_line_description_zero_shot/train-*
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path: code_line_description_zero_shot/validation-*
- config_name: codenames_zero_shot
data_files:
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path: codenames_zero_shot/default-*
- split: train
path: codenames_zero_shot/train-*
- split: validation
path: codenames_zero_shot/validation-*
- config_name: color_zero_shot
data_files:
- split: default
path: color_zero_shot/default-*
- split: train
path: color_zero_shot/train-*
- split: validation
path: color_zero_shot/validation-*
- config_name: common_morpheme_zero_shot
data_files:
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path: common_morpheme_zero_shot/default-*
- split: train
path: common_morpheme_zero_shot/train-*
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path: common_morpheme_zero_shot/validation-*
- config_name: conceptual_combinations_zero_shot
data_files:
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path: conceptual_combinations_zero_shot/default-*
- split: train
path: conceptual_combinations_zero_shot/train-*
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path: conceptual_combinations_zero_shot/validation-*
- config_name: conlang_translation_zero_shot
data_files:
- split: default
path: conlang_translation_zero_shot/default-*
- split: train
path: conlang_translation_zero_shot/train-*
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path: conlang_translation_zero_shot/validation-*
- config_name: contextual_parametric_knowledge_conflicts_zero_shot
data_files:
- split: default
path: contextual_parametric_knowledge_conflicts_zero_shot/default-*
- split: train
path: contextual_parametric_knowledge_conflicts_zero_shot/train-*
- split: validation
path: contextual_parametric_knowledge_conflicts_zero_shot/validation-*
- config_name: crash_blossom_zero_shot
data_files:
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path: crash_blossom_zero_shot/default-*
- split: train
path: crash_blossom_zero_shot/train-*
- split: validation
path: crash_blossom_zero_shot/validation-*
- config_name: crass_ai_zero_shot
data_files:
- split: default
path: crass_ai_zero_shot/default-*
- split: train
path: crass_ai_zero_shot/train-*
- split: validation
path: crass_ai_zero_shot/validation-*
- config_name: cryobiology_spanish_zero_shot
data_files:
- split: default
path: cryobiology_spanish_zero_shot/default-*
- split: train
path: cryobiology_spanish_zero_shot/train-*
- split: validation
path: cryobiology_spanish_zero_shot/validation-*
- config_name: cryptonite_zero_shot
data_files:
- split: default
path: cryptonite_zero_shot/default-*
- split: train
path: cryptonite_zero_shot/train-*
- split: validation
path: cryptonite_zero_shot/validation-*
- config_name: cs_algorithms_zero_shot
data_files:
- split: default
path: cs_algorithms_zero_shot/default-*
- split: train
path: cs_algorithms_zero_shot/train-*
- split: validation
path: cs_algorithms_zero_shot/validation-*
- config_name: dark_humor_detection_zero_shot
data_files:
- split: default
path: dark_humor_detection_zero_shot/default-*
- split: train
path: dark_humor_detection_zero_shot/train-*
- split: validation
path: dark_humor_detection_zero_shot/validation-*
- config_name: date_understanding_zero_shot
data_files:
- split: default
path: date_understanding_zero_shot/default-*
- split: train
path: date_understanding_zero_shot/train-*
- split: validation
path: date_understanding_zero_shot/validation-*
- config_name: disambiguation_qa_zero_shot
data_files:
- split: default
path: disambiguation_qa_zero_shot/default-*
- split: train
path: disambiguation_qa_zero_shot/train-*
- split: validation
path: disambiguation_qa_zero_shot/validation-*
- config_name: discourse_marker_prediction_zero_shot
data_files:
- split: default
path: discourse_marker_prediction_zero_shot/default-*
- split: train
path: discourse_marker_prediction_zero_shot/train-*
- split: validation
path: discourse_marker_prediction_zero_shot/validation-*
- config_name: disfl_qa_zero_shot
data_files:
- split: default
path: disfl_qa_zero_shot/default-*
- split: train
path: disfl_qa_zero_shot/train-*
- split: validation
path: disfl_qa_zero_shot/validation-*
- config_name: dyck_languages_zero_shot
data_files:
- split: default
path: dyck_languages_zero_shot/default-*
- split: train
path: dyck_languages_zero_shot/train-*
- split: validation
path: dyck_languages_zero_shot/validation-*
- config_name: elementary_math_qa_zero_shot
data_files:
- split: default
path: elementary_math_qa_zero_shot/default-*
- split: train
path: elementary_math_qa_zero_shot/train-*
- split: validation
path: elementary_math_qa_zero_shot/validation-*
- config_name: emoji_movie_zero_shot
data_files:
- split: default
path: emoji_movie_zero_shot/default-*
- split: train
path: emoji_movie_zero_shot/train-*
- split: validation
path: emoji_movie_zero_shot/validation-*
- config_name: emojis_emotion_prediction_zero_shot
data_files:
- split: default
path: emojis_emotion_prediction_zero_shot/default-*
- split: train
path: emojis_emotion_prediction_zero_shot/train-*
- split: validation
path: emojis_emotion_prediction_zero_shot/validation-*
- config_name: empirical_judgments_zero_shot
data_files:
- split: default
path: empirical_judgments_zero_shot/default-*
- split: train
path: empirical_judgments_zero_shot/train-*
- split: validation
path: empirical_judgments_zero_shot/validation-*
- config_name: english_proverbs_zero_shot
data_files:
- split: default
path: english_proverbs_zero_shot/default-*
- split: train
path: english_proverbs_zero_shot/train-*
- split: validation
path: english_proverbs_zero_shot/validation-*
- config_name: english_russian_proverbs_zero_shot
data_files:
- split: default
path: english_russian_proverbs_zero_shot/default-*
- split: train
path: english_russian_proverbs_zero_shot/train-*
- split: validation
path: english_russian_proverbs_zero_shot/validation-*
- config_name: entailed_polarity_hindi_zero_shot
data_files:
- split: default
path: entailed_polarity_hindi_zero_shot/default-*
- split: train
path: entailed_polarity_hindi_zero_shot/train-*
- split: validation
path: entailed_polarity_hindi_zero_shot/validation-*
- config_name: entailed_polarity_zero_shot
data_files:
- split: default
path: entailed_polarity_zero_shot/default-*
- split: train
path: entailed_polarity_zero_shot/train-*
- split: validation
path: entailed_polarity_zero_shot/validation-*
- config_name: epistemic_reasoning_zero_shot
data_files:
- split: default
path: epistemic_reasoning_zero_shot/default-*
- split: train
path: epistemic_reasoning_zero_shot/train-*
- split: validation
path: epistemic_reasoning_zero_shot/validation-*
- config_name: evaluating_information_essentiality_zero_shot
data_files:
- split: default
path: evaluating_information_essentiality_zero_shot/default-*
- split: train
path: evaluating_information_essentiality_zero_shot/train-*
- split: validation
path: evaluating_information_essentiality_zero_shot/validation-*
- config_name: fact_checker_zero_shot
data_files:
- split: default
path: fact_checker_zero_shot/default-*
- split: train
path: fact_checker_zero_shot/train-*
- split: validation
path: fact_checker_zero_shot/validation-*
- config_name: fantasy_reasoning_zero_shot
data_files:
- split: default
path: fantasy_reasoning_zero_shot/default-*
- split: train
path: fantasy_reasoning_zero_shot/train-*
- split: validation
path: fantasy_reasoning_zero_shot/validation-*
- config_name: few_shot_nlg_zero_shot
data_files:
- split: default
path: few_shot_nlg_zero_shot/default-*
- split: train
path: few_shot_nlg_zero_shot/train-*
- split: validation
path: few_shot_nlg_zero_shot/validation-*
- config_name: figure_of_speech_detection_zero_shot
data_files:
- split: default
path: figure_of_speech_detection_zero_shot/default-*
- split: train
path: figure_of_speech_detection_zero_shot/train-*
- split: validation
path: figure_of_speech_detection_zero_shot/validation-*
- config_name: formal_fallacies_syllogisms_negation_zero_shot
data_files:
- split: default
path: formal_fallacies_syllogisms_negation_zero_shot/default-*
- split: train
path: formal_fallacies_syllogisms_negation_zero_shot/train-*
- split: validation
path: formal_fallacies_syllogisms_negation_zero_shot/validation-*
- config_name: gem_zero_shot
data_files:
- split: default
path: gem_zero_shot/default-*
- split: train
path: gem_zero_shot/train-*
- split: validation
path: gem_zero_shot/validation-*
- config_name: gender_inclusive_sentences_german_zero_shot
data_files:
- split: default
path: gender_inclusive_sentences_german_zero_shot/default-*
- split: train
path: gender_inclusive_sentences_german_zero_shot/train-*
- split: validation
path: gender_inclusive_sentences_german_zero_shot/validation-*
- config_name: general_knowledge_zero_shot
data_files:
- split: default
path: general_knowledge_zero_shot/default-*
- split: train
path: general_knowledge_zero_shot/train-*
- split: validation
path: general_knowledge_zero_shot/validation-*
- config_name: geometric_shapes_zero_shot
data_files:
- split: default
path: geometric_shapes_zero_shot/default-*
- split: train
path: geometric_shapes_zero_shot/train-*
- split: validation
path: geometric_shapes_zero_shot/validation-*
- config_name: goal_step_wikihow_zero_shot
data_files:
- split: default
path: goal_step_wikihow_zero_shot/default-*
- split: train
path: goal_step_wikihow_zero_shot/train-*
- split: validation
path: goal_step_wikihow_zero_shot/validation-*
- config_name: gre_reading_comprehension_zero_shot
data_files:
- split: default
path: gre_reading_comprehension_zero_shot/default-*
- split: train
path: gre_reading_comprehension_zero_shot/train-*
- split: validation
path: gre_reading_comprehension_zero_shot/validation-*
- config_name: hhh_alignment_zero_shot
data_files:
- split: default
path: hhh_alignment_zero_shot/default-*
- split: train
path: hhh_alignment_zero_shot/train-*
- split: validation
path: hhh_alignment_zero_shot/validation-*
- config_name: hindi_question_answering_zero_shot
data_files:
- split: default
path: hindi_question_answering_zero_shot/default-*
- split: train
path: hindi_question_answering_zero_shot/train-*
- split: validation
path: hindi_question_answering_zero_shot/validation-*
- config_name: hindu_knowledge_zero_shot
data_files:
- split: default
path: hindu_knowledge_zero_shot/default-*
- split: train
path: hindu_knowledge_zero_shot/train-*
- split: validation
path: hindu_knowledge_zero_shot/validation-*
- config_name: hinglish_toxicity_zero_shot
data_files:
- split: default
path: hinglish_toxicity_zero_shot/default-*
- split: train
path: hinglish_toxicity_zero_shot/train-*
- split: validation
path: hinglish_toxicity_zero_shot/validation-*
- config_name: human_organs_senses_zero_shot
data_files:
- split: default
path: human_organs_senses_zero_shot/default-*
- split: train
path: human_organs_senses_zero_shot/train-*
- split: validation
path: human_organs_senses_zero_shot/validation-*
- config_name: hyperbaton_zero_shot
data_files:
- split: default
path: hyperbaton_zero_shot/default-*
- split: train
path: hyperbaton_zero_shot/train-*
- split: validation
path: hyperbaton_zero_shot/validation-*
- config_name: identify_math_theorems_zero_shot
data_files:
- split: default
path: identify_math_theorems_zero_shot/default-*
- split: train
path: identify_math_theorems_zero_shot/train-*
- split: validation
path: identify_math_theorems_zero_shot/validation-*
- config_name: identify_odd_metaphor_zero_shot
data_files:
- split: default
path: identify_odd_metaphor_zero_shot/default-*
- split: train
path: identify_odd_metaphor_zero_shot/train-*
- split: validation
path: identify_odd_metaphor_zero_shot/validation-*
- config_name: implicatures_zero_shot
data_files:
- split: default
path: implicatures_zero_shot/default-*
- split: train
path: implicatures_zero_shot/train-*
- split: validation
path: implicatures_zero_shot/validation-*
- config_name: implicit_relations_zero_shot
data_files:
- split: default
path: implicit_relations_zero_shot/default-*
- split: train
path: implicit_relations_zero_shot/train-*
- split: validation
path: implicit_relations_zero_shot/validation-*
- config_name: intent_recognition_zero_shot
data_files:
- split: default
path: intent_recognition_zero_shot/default-*
- split: train
path: intent_recognition_zero_shot/train-*
- split: validation
path: intent_recognition_zero_shot/validation-*
- config_name: international_phonetic_alphabet_nli_zero_shot
data_files:
- split: default
path: international_phonetic_alphabet_nli_zero_shot/default-*
- split: train
path: international_phonetic_alphabet_nli_zero_shot/train-*
- split: validation
path: international_phonetic_alphabet_nli_zero_shot/validation-*
- config_name: international_phonetic_alphabet_transliterate_zero_shot
data_files:
- split: default
path: international_phonetic_alphabet_transliterate_zero_shot/default-*
- split: train
path: international_phonetic_alphabet_transliterate_zero_shot/train-*
- split: validation
path: international_phonetic_alphabet_transliterate_zero_shot/validation-*
- config_name: intersect_geometry_zero_shot
data_files:
- split: default
path: intersect_geometry_zero_shot/default-*
- split: train
path: intersect_geometry_zero_shot/train-*
- split: validation
path: intersect_geometry_zero_shot/validation-*
- config_name: irony_identification_zero_shot
data_files:
- split: default
path: irony_identification_zero_shot/default-*
- split: train
path: irony_identification_zero_shot/train-*
- split: validation
path: irony_identification_zero_shot/validation-*
- config_name: kanji_ascii_zero_shot
data_files:
- split: default
path: kanji_ascii_zero_shot/default-*
- split: train
path: kanji_ascii_zero_shot/train-*
- split: validation
path: kanji_ascii_zero_shot/validation-*
- config_name: kannada_zero_shot
data_files:
- split: default
path: kannada_zero_shot/default-*
- split: train
path: kannada_zero_shot/train-*
- split: validation
path: kannada_zero_shot/validation-*
- config_name: key_value_maps_zero_shot
data_files:
- split: default
path: key_value_maps_zero_shot/default-*
- split: train
path: key_value_maps_zero_shot/train-*
- split: validation
path: key_value_maps_zero_shot/validation-*
- config_name: known_unknowns_zero_shot
data_files:
- split: default
path: known_unknowns_zero_shot/default-*
- split: train
path: known_unknowns_zero_shot/train-*
- split: validation
path: known_unknowns_zero_shot/validation-*
- config_name: language_games_zero_shot
data_files:
- split: default
path: language_games_zero_shot/default-*
- split: train
path: language_games_zero_shot/train-*
- split: validation
path: language_games_zero_shot/validation-*
- config_name: language_identification_zero_shot
data_files:
- split: default
path: language_identification_zero_shot/default-*
- split: train
path: language_identification_zero_shot/train-*
- split: validation
path: language_identification_zero_shot/validation-*
- config_name: linguistic_mappings_zero_shot
data_files:
- split: default
path: linguistic_mappings_zero_shot/default-*
- split: train
path: linguistic_mappings_zero_shot/train-*
- split: validation
path: linguistic_mappings_zero_shot/validation-*
- config_name: linguistics_puzzles_zero_shot
data_files:
- split: default
path: linguistics_puzzles_zero_shot/default-*
- split: train
path: linguistics_puzzles_zero_shot/train-*
- split: validation
path: linguistics_puzzles_zero_shot/validation-*
- config_name: list_functions_zero_shot
data_files:
- split: default
path: list_functions_zero_shot/default-*
- split: train
path: list_functions_zero_shot/train-*
- split: validation
path: list_functions_zero_shot/validation-*
- config_name: logic_grid_puzzle_zero_shot
data_files:
- split: default
path: logic_grid_puzzle_zero_shot/default-*
- split: train
path: logic_grid_puzzle_zero_shot/train-*
- split: validation
path: logic_grid_puzzle_zero_shot/validation-*
- config_name: logical_args_zero_shot
data_files:
- split: default
path: logical_args_zero_shot/default-*
- split: train
path: logical_args_zero_shot/train-*
- split: validation
path: logical_args_zero_shot/validation-*
- config_name: logical_deduction_zero_shot
data_files:
- split: default
path: logical_deduction_zero_shot/default-*
- split: train
path: logical_deduction_zero_shot/train-*
- split: validation
path: logical_deduction_zero_shot/validation-*
- config_name: logical_fallacy_detection_zero_shot
data_files:
- split: default
path: logical_fallacy_detection_zero_shot/default-*
- split: train
path: logical_fallacy_detection_zero_shot/train-*
- split: validation
path: logical_fallacy_detection_zero_shot/validation-*
- config_name: logical_sequence_zero_shot
data_files:
- split: default
path: logical_sequence_zero_shot/default-*
- split: train
path: logical_sequence_zero_shot/train-*
- split: validation
path: logical_sequence_zero_shot/validation-*
- config_name: mathematical_induction_zero_shot
data_files:
- split: default
path: mathematical_induction_zero_shot/default-*
- split: train
path: mathematical_induction_zero_shot/train-*
- split: validation
path: mathematical_induction_zero_shot/validation-*
- config_name: matrixshapes_zero_shot
data_files:
- split: default
path: matrixshapes_zero_shot/default-*
- split: train
path: matrixshapes_zero_shot/train-*
- split: validation
path: matrixshapes_zero_shot/validation-*
- config_name: metaphor_boolean_zero_shot
data_files:
- split: default
path: metaphor_boolean_zero_shot/default-*
- split: train
path: metaphor_boolean_zero_shot/train-*
- split: validation
path: metaphor_boolean_zero_shot/validation-*
- config_name: metaphor_understanding_zero_shot
data_files:
- split: default
path: metaphor_understanding_zero_shot/default-*
- split: train
path: metaphor_understanding_zero_shot/train-*
- split: validation
path: metaphor_understanding_zero_shot/validation-*
- config_name: minute_mysteries_qa_zero_shot
data_files:
- split: default
path: minute_mysteries_qa_zero_shot/default-*
- split: train
path: minute_mysteries_qa_zero_shot/train-*
- split: validation
path: minute_mysteries_qa_zero_shot/validation-*
- config_name: misconceptions_russian_zero_shot
data_files:
- split: default
path: misconceptions_russian_zero_shot/default-*
- split: train
path: misconceptions_russian_zero_shot/train-*
- split: validation
path: misconceptions_russian_zero_shot/validation-*
- config_name: misconceptions_zero_shot
data_files:
- split: default
path: misconceptions_zero_shot/default-*
- split: train
path: misconceptions_zero_shot/train-*
- split: validation
path: misconceptions_zero_shot/validation-*
- config_name: mnist_ascii_zero_shot
data_files:
- split: default
path: mnist_ascii_zero_shot/default-*
- split: train
path: mnist_ascii_zero_shot/train-*
- split: validation
path: mnist_ascii_zero_shot/validation-*
- config_name: modified_arithmetic_zero_shot
data_files:
- split: default
path: modified_arithmetic_zero_shot/default-*
- split: train
path: modified_arithmetic_zero_shot/train-*
- split: validation
path: modified_arithmetic_zero_shot/validation-*
- config_name: moral_permissibility_zero_shot
data_files:
- split: default
path: moral_permissibility_zero_shot/default-*
- split: train
path: moral_permissibility_zero_shot/train-*
- split: validation
path: moral_permissibility_zero_shot/validation-*
- config_name: movie_dialog_same_or_different_zero_shot
data_files:
- split: default
path: movie_dialog_same_or_different_zero_shot/default-*
- split: train
path: movie_dialog_same_or_different_zero_shot/train-*
- split: validation
path: movie_dialog_same_or_different_zero_shot/validation-*
- config_name: movie_recommendation_zero_shot
data_files:
- split: default
path: movie_recommendation_zero_shot/default-*
- split: train
path: movie_recommendation_zero_shot/train-*
- split: validation
path: movie_recommendation_zero_shot/validation-*
- config_name: mult_data_wrangling_zero_shot
data_files:
- split: default
path: mult_data_wrangling_zero_shot/default-*
- split: train
path: mult_data_wrangling_zero_shot/train-*
- split: validation
path: mult_data_wrangling_zero_shot/validation-*
- config_name: multiemo_zero_shot
data_files:
- split: default
path: multiemo_zero_shot/default-*
- split: train
path: multiemo_zero_shot/train-*
- split: validation
path: multiemo_zero_shot/validation-*
- config_name: natural_instructions_zero_shot
data_files:
- split: default
path: natural_instructions_zero_shot/default-*
- split: train
path: natural_instructions_zero_shot/train-*
- split: validation
path: natural_instructions_zero_shot/validation-*
- config_name: navigate_zero_shot
data_files:
- split: default
path: navigate_zero_shot/default-*
- split: train
path: navigate_zero_shot/train-*
- split: validation
path: navigate_zero_shot/validation-*
- config_name: nonsense_words_grammar_zero_shot
data_files:
- split: default
path: nonsense_words_grammar_zero_shot/default-*
- split: train
path: nonsense_words_grammar_zero_shot/train-*
- split: validation
path: nonsense_words_grammar_zero_shot/validation-*
- config_name: novel_concepts_zero_shot
data_files:
- split: default
path: novel_concepts_zero_shot/default-*
- split: train
path: novel_concepts_zero_shot/train-*
- split: validation
path: novel_concepts_zero_shot/validation-*
- config_name: object_counting_zero_shot
data_files:
- split: default
path: object_counting_zero_shot/default-*
- split: train
path: object_counting_zero_shot/train-*
- split: validation
path: object_counting_zero_shot/validation-*
- config_name: odd_one_out_zero_shot
data_files:
- split: default
path: odd_one_out_zero_shot/default-*
- split: train
path: odd_one_out_zero_shot/train-*
- split: validation
path: odd_one_out_zero_shot/validation-*
- config_name: operators_zero_shot
data_files:
- split: default
path: operators_zero_shot/default-*
- split: train
path: operators_zero_shot/train-*
- split: validation
path: operators_zero_shot/validation-*
- config_name: paragraph_segmentation_zero_shot
data_files:
- split: default
path: paragraph_segmentation_zero_shot/default-*
- split: train
path: paragraph_segmentation_zero_shot/train-*
- split: validation
path: paragraph_segmentation_zero_shot/validation-*
- config_name: parsinlu_qa_zero_shot
data_files:
- split: default
path: parsinlu_qa_zero_shot/default-*
- split: train
path: parsinlu_qa_zero_shot/train-*
- split: validation
path: parsinlu_qa_zero_shot/validation-*
- config_name: parsinlu_reading_comprehension_zero_shot
data_files:
- split: default
path: parsinlu_reading_comprehension_zero_shot/default-*
- split: train
path: parsinlu_reading_comprehension_zero_shot/train-*
- split: validation
path: parsinlu_reading_comprehension_zero_shot/validation-*
- config_name: penguins_in_a_table_zero_shot
data_files:
- split: default
path: penguins_in_a_table_zero_shot/default-*
- split: train
path: penguins_in_a_table_zero_shot/train-*
- split: validation
path: penguins_in_a_table_zero_shot/validation-*
- config_name: periodic_elements_zero_shot
data_files:
- split: default
path: periodic_elements_zero_shot/default-*
- split: train
path: periodic_elements_zero_shot/train-*
- split: validation
path: periodic_elements_zero_shot/validation-*
- config_name: persian_idioms_zero_shot
data_files:
- split: default
path: persian_idioms_zero_shot/default-*
- split: train
path: persian_idioms_zero_shot/train-*
- split: validation
path: persian_idioms_zero_shot/validation-*
- config_name: phrase_relatedness_zero_shot
data_files:
- split: default
path: phrase_relatedness_zero_shot/default-*
- split: train
path: phrase_relatedness_zero_shot/train-*
- split: validation
path: phrase_relatedness_zero_shot/validation-*
- config_name: physical_intuition_zero_shot
data_files:
- split: default
path: physical_intuition_zero_shot/default-*
- split: train
path: physical_intuition_zero_shot/train-*
- split: validation
path: physical_intuition_zero_shot/validation-*
- config_name: physics_questions_zero_shot
data_files:
- split: default
path: physics_questions_zero_shot/default-*
- split: train
path: physics_questions_zero_shot/train-*
- split: validation
path: physics_questions_zero_shot/validation-*
- config_name: physics_zero_shot
data_files:
- split: default
path: physics_zero_shot/default-*
- split: train
path: physics_zero_shot/train-*
- split: validation
path: physics_zero_shot/validation-*
- config_name: play_dialog_same_or_different_zero_shot
data_files:
- split: default
path: play_dialog_same_or_different_zero_shot/default-*
- split: train
path: play_dialog_same_or_different_zero_shot/train-*
- split: validation
path: play_dialog_same_or_different_zero_shot/validation-*
- config_name: polish_sequence_labeling_zero_shot
data_files:
- split: default
path: polish_sequence_labeling_zero_shot/default-*
- split: train
path: polish_sequence_labeling_zero_shot/train-*
- split: validation
path: polish_sequence_labeling_zero_shot/validation-*
- config_name: presuppositions_as_nli_zero_shot
data_files:
- split: default
path: presuppositions_as_nli_zero_shot/default-*
- split: train
path: presuppositions_as_nli_zero_shot/train-*
- split: validation
path: presuppositions_as_nli_zero_shot/validation-*
- config_name: qa_wikidata_zero_shot
data_files:
- split: default
path: qa_wikidata_zero_shot/default-*
- split: train
path: qa_wikidata_zero_shot/train-*
- split: validation
path: qa_wikidata_zero_shot/validation-*
- config_name: question_selection_zero_shot
data_files:
- split: default
path: question_selection_zero_shot/default-*
- split: train
path: question_selection_zero_shot/train-*
- split: validation
path: question_selection_zero_shot/validation-*
- config_name: real_or_fake_text_zero_shot
data_files:
- split: default
path: real_or_fake_text_zero_shot/default-*
- split: train
path: real_or_fake_text_zero_shot/train-*
- split: validation
path: real_or_fake_text_zero_shot/validation-*
- config_name: reasoning_about_colored_objects_zero_shot
data_files:
- split: default
path: reasoning_about_colored_objects_zero_shot/default-*
- split: train
path: reasoning_about_colored_objects_zero_shot/train-*
- split: validation
path: reasoning_about_colored_objects_zero_shot/validation-*
- config_name: repeat_copy_logic_zero_shot
data_files:
- split: default
path: repeat_copy_logic_zero_shot/default-*
- split: train
path: repeat_copy_logic_zero_shot/train-*
- split: validation
path: repeat_copy_logic_zero_shot/validation-*
- config_name: rephrase_zero_shot
data_files:
- split: default
path: rephrase_zero_shot/default-*
- split: train
path: rephrase_zero_shot/train-*
- split: validation
path: rephrase_zero_shot/validation-*
- config_name: riddle_sense_zero_shot
data_files:
- split: default
path: riddle_sense_zero_shot/default-*
- split: train
path: riddle_sense_zero_shot/train-*
- split: validation
path: riddle_sense_zero_shot/validation-*
- config_name: ruin_names_zero_shot
data_files:
- split: default
path: ruin_names_zero_shot/default-*
- split: train
path: ruin_names_zero_shot/train-*
- split: validation
path: ruin_names_zero_shot/validation-*
- config_name: salient_translation_error_detection_zero_shot
data_files:
- split: default
path: salient_translation_error_detection_zero_shot/default-*
- split: train
path: salient_translation_error_detection_zero_shot/train-*
- split: validation
path: salient_translation_error_detection_zero_shot/validation-*
- config_name: scientific_press_release_zero_shot
data_files:
- split: default
path: scientific_press_release_zero_shot/default-*
- split: train
path: scientific_press_release_zero_shot/train-*
- split: validation
path: scientific_press_release_zero_shot/validation-*
- config_name: semantic_parsing_in_context_sparc_zero_shot
data_files:
- split: default
path: semantic_parsing_in_context_sparc_zero_shot/default-*
- split: train
path: semantic_parsing_in_context_sparc_zero_shot/train-*
- split: validation
path: semantic_parsing_in_context_sparc_zero_shot/validation-*
- config_name: semantic_parsing_spider_zero_shot
data_files:
- split: default
path: semantic_parsing_spider_zero_shot/default-*
- split: train
path: semantic_parsing_spider_zero_shot/train-*
- split: validation
path: semantic_parsing_spider_zero_shot/validation-*
- config_name: sentence_ambiguity_zero_shot
data_files:
- split: default
path: sentence_ambiguity_zero_shot/default-*
- split: train
path: sentence_ambiguity_zero_shot/train-*
- split: validation
path: sentence_ambiguity_zero_shot/validation-*
- config_name: similarities_abstraction_zero_shot
data_files:
- split: default
path: similarities_abstraction_zero_shot/default-*
- split: train
path: similarities_abstraction_zero_shot/train-*
- split: validation
path: similarities_abstraction_zero_shot/validation-*
- config_name: simp_turing_concept_zero_shot
data_files:
- split: default
path: simp_turing_concept_zero_shot/default-*
- split: train
path: simp_turing_concept_zero_shot/train-*
- split: validation
path: simp_turing_concept_zero_shot/validation-*
- config_name: simple_arithmetic_json_multiple_choice_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_multiple_choice_zero_shot/default-*
- split: train
path: simple_arithmetic_json_multiple_choice_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_multiple_choice_zero_shot/validation-*
- config_name: simple_arithmetic_json_subtasks_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_subtasks_zero_shot/default-*
- split: train
path: simple_arithmetic_json_subtasks_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_subtasks_zero_shot/validation-*
- config_name: simple_arithmetic_json_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_zero_shot/default-*
- split: train
path: simple_arithmetic_json_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_zero_shot/validation-*
- config_name: simple_arithmetic_multiple_targets_json_zero_shot
data_files:
- split: default
path: simple_arithmetic_multiple_targets_json_zero_shot/default-*
- split: train
path: simple_arithmetic_multiple_targets_json_zero_shot/train-*
- split: validation
path: simple_arithmetic_multiple_targets_json_zero_shot/validation-*
- config_name: simple_ethical_questions_zero_shot
data_files:
- split: default
path: simple_ethical_questions_zero_shot/default-*
- split: train
path: simple_ethical_questions_zero_shot/train-*
- split: validation
path: simple_ethical_questions_zero_shot/validation-*
- config_name: simple_text_editing_zero_shot
data_files:
- split: default
path: simple_text_editing_zero_shot/default-*
- split: train
path: simple_text_editing_zero_shot/train-*
- split: validation
path: simple_text_editing_zero_shot/validation-*
- config_name: snarks_zero_shot
data_files:
- split: default
path: snarks_zero_shot/default-*
- split: train
path: snarks_zero_shot/train-*
- split: validation
path: snarks_zero_shot/validation-*
- config_name: social_iqa_zero_shot
data_files:
- split: default
path: social_iqa_zero_shot/default-*
- split: train
path: social_iqa_zero_shot/train-*
- split: validation
path: social_iqa_zero_shot/validation-*
- config_name: social_support_zero_shot
data_files:
- split: default
path: social_support_zero_shot/default-*
- split: train
path: social_support_zero_shot/train-*
- split: validation
path: social_support_zero_shot/validation-*
- config_name: sports_understanding_zero_shot
data_files:
- split: default
path: sports_understanding_zero_shot/default-*
- split: train
path: sports_understanding_zero_shot/train-*
- split: validation
path: sports_understanding_zero_shot/validation-*
- config_name: strange_stories_zero_shot
data_files:
- split: default
path: strange_stories_zero_shot/default-*
- split: train
path: strange_stories_zero_shot/train-*
- split: validation
path: strange_stories_zero_shot/validation-*
- config_name: strategyqa_zero_shot
data_files:
- split: default
path: strategyqa_zero_shot/default-*
- split: train
path: strategyqa_zero_shot/train-*
- split: validation
path: strategyqa_zero_shot/validation-*
- config_name: sufficient_information_zero_shot
data_files:
- split: default
path: sufficient_information_zero_shot/default-*
- split: train
path: sufficient_information_zero_shot/train-*
- split: validation
path: sufficient_information_zero_shot/validation-*
- config_name: suicide_risk_zero_shot
data_files:
- split: default
path: suicide_risk_zero_shot/default-*
- split: train
path: suicide_risk_zero_shot/train-*
- split: validation
path: suicide_risk_zero_shot/validation-*
- config_name: swahili_english_proverbs_zero_shot
data_files:
- split: default
path: swahili_english_proverbs_zero_shot/default-*
- split: train
path: swahili_english_proverbs_zero_shot/train-*
- split: validation
path: swahili_english_proverbs_zero_shot/validation-*
- config_name: swedish_to_german_proverbs_zero_shot
data_files:
- split: default
path: swedish_to_german_proverbs_zero_shot/default-*
- split: train
path: swedish_to_german_proverbs_zero_shot/train-*
- split: validation
path: swedish_to_german_proverbs_zero_shot/validation-*
- config_name: symbol_interpretation_zero_shot
data_files:
- split: default
path: symbol_interpretation_zero_shot/default-*
- split: train
path: symbol_interpretation_zero_shot/train-*
- split: validation
path: symbol_interpretation_zero_shot/validation-*
- config_name: temporal_sequences_zero_shot
data_files:
- split: default
path: temporal_sequences_zero_shot/default-*
- split: train
path: temporal_sequences_zero_shot/train-*
- split: validation
path: temporal_sequences_zero_shot/validation-*
- config_name: tense_zero_shot
data_files:
- split: default
path: tense_zero_shot/default-*
- split: train
path: tense_zero_shot/train-*
- split: validation
path: tense_zero_shot/validation-*
- config_name: timedial_zero_shot
data_files:
- split: default
path: timedial_zero_shot/default-*
- split: train
path: timedial_zero_shot/train-*
- split: validation
path: timedial_zero_shot/validation-*
- config_name: topical_chat_zero_shot
data_files:
- split: default
path: topical_chat_zero_shot/default-*
- split: train
path: topical_chat_zero_shot/train-*
- split: validation
path: topical_chat_zero_shot/validation-*
- config_name: tracking_shuffled_objects_zero_shot
data_files:
- split: default
path: tracking_shuffled_objects_zero_shot/default-*
- split: train
path: tracking_shuffled_objects_zero_shot/train-*
- split: validation
path: tracking_shuffled_objects_zero_shot/validation-*
- config_name: understanding_fables_zero_shot
data_files:
- split: default
path: understanding_fables_zero_shot/default-*
- split: train
path: understanding_fables_zero_shot/train-*
- split: validation
path: understanding_fables_zero_shot/validation-*
- config_name: undo_permutation_zero_shot
data_files:
- split: default
path: undo_permutation_zero_shot/default-*
- split: train
path: undo_permutation_zero_shot/train-*
- split: validation
path: undo_permutation_zero_shot/validation-*
- config_name: unit_conversion_zero_shot
data_files:
- split: default
path: unit_conversion_zero_shot/default-*
- split: train
path: unit_conversion_zero_shot/train-*
- split: validation
path: unit_conversion_zero_shot/validation-*
- config_name: unit_interpretation_zero_shot
data_files:
- split: default
path: unit_interpretation_zero_shot/default-*
- split: train
path: unit_interpretation_zero_shot/train-*
- split: validation
path: unit_interpretation_zero_shot/validation-*
- config_name: unnatural_in_context_learning_zero_shot
data_files:
- split: default
path: unnatural_in_context_learning_zero_shot/default-*
- split: train
path: unnatural_in_context_learning_zero_shot/train-*
- split: validation
path: unnatural_in_context_learning_zero_shot/validation-*
- config_name: vitaminc_fact_verification_zero_shot
data_files:
- split: default
path: vitaminc_fact_verification_zero_shot/default-*
- split: train
path: vitaminc_fact_verification_zero_shot/train-*
- split: validation
path: vitaminc_fact_verification_zero_shot/validation-*
- config_name: what_is_the_tao_zero_shot
data_files:
- split: default
path: what_is_the_tao_zero_shot/default-*
- split: train
path: what_is_the_tao_zero_shot/train-*
- split: validation
path: what_is_the_tao_zero_shot/validation-*
- config_name: which_wiki_edit_zero_shot
data_files:
- split: default
path: which_wiki_edit_zero_shot/default-*
- split: train
path: which_wiki_edit_zero_shot/train-*
- split: validation
path: which_wiki_edit_zero_shot/validation-*
- config_name: winowhy_zero_shot
data_files:
- split: default
path: winowhy_zero_shot/default-*
- split: train
path: winowhy_zero_shot/train-*
- split: validation
path: winowhy_zero_shot/validation-*
- config_name: word_sorting_zero_shot
data_files:
- split: default
path: word_sorting_zero_shot/default-*
- split: train
path: word_sorting_zero_shot/train-*
- split: validation
path: word_sorting_zero_shot/validation-*
- config_name: word_unscrambling_zero_shot
data_files:
- split: default
path: word_unscrambling_zero_shot/default-*
- split: train
path: word_unscrambling_zero_shot/train-*
- split: validation
path: word_unscrambling_zero_shot/validation-*
---
# Dataset Card for "bigbench"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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amasand/imdb-neg-to-pos | amasand | 2023-11-01T18:49:05Z | 538 | 0 | [
"region:us"
] | null | 2023-11-01T18:36:51Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: eval
path: data/eval-*
dataset_info:
features:
- name: prompt_or_input_text
dtype: string
- name: target_or_reference_text
dtype: string
splits:
- name: train
num_bytes: 8939708
num_examples: 20000
- name: test
num_bytes: 2205759
num_examples: 5000
- name: eval
num_bytes: 2261341
num_examples: 5000
download_size: 8943325
dataset_size: 13406808
---
# Dataset Card for "imdb-neg-to-pos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tuple_ie | null | 2022-11-03T16:31:04Z | 537 | 1 | [
"task_categories:other",
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"open-information-extraction",
"region:us"
] | [
"other"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: tupleinf-open-ie-dataset
pretty_name: TupleInf Open IE
tags:
- open-information-extraction
dataset_info:
- config_name: all
features:
- name: sentence
dtype: string
- name: tuples
sequence:
- name: score
dtype: float32
- name: tuple_text
dtype: string
- name: context
dtype: string
- name: arg1
dtype: string
- name: rel
dtype: string
- name: arg2s
sequence: string
splits:
- name: train
num_bytes: 115621096
num_examples: 267719
download_size: 18026102
dataset_size: 115621096
- config_name: 4th_grade
features:
- name: sentence
dtype: string
- name: tuples
sequence:
- name: score
dtype: float32
- name: tuple_text
dtype: string
- name: context
dtype: string
- name: arg1
dtype: string
- name: rel
dtype: string
- name: arg2s
sequence: string
splits:
- name: train
num_bytes: 65363445
num_examples: 158910
download_size: 18026102
dataset_size: 65363445
- config_name: 8th_grade
features:
- name: sentence
dtype: string
- name: tuples
sequence:
- name: score
dtype: float32
- name: tuple_text
dtype: string
- name: context
dtype: string
- name: arg1
dtype: string
- name: rel
dtype: string
- name: arg2s
sequence: string
splits:
- name: train
num_bytes: 50257651
num_examples: 108809
download_size: 18026102
dataset_size: 50257651
---
# Dataset Card for TupleInf Open IE
## 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:** [Tuple IE Homepage](https://allenai.org/data/tuple-ie)
- **Repository:**
- **Paper:** [Answering Complex Questions Using Open Information Extraction](https://www.semanticscholar.org/paper/Answering-Complex-Questions-Using-Open-Information-Khot-Sabharwal/0ff595f0645a3e25a2f37145768985b10ead0509)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. Each sentence is followed by the Open IE v4 tuples using their simple format.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English, collected from a large Web corpus using training questions from 4th and 8th grade as queries.
## Dataset Structure
### Data Instances
This dataset contains setences with corresponding relation tuples extracted from each sentence. Each instance should contain a sentence and followed by the [Open IE v4](https://github.com/allenai/openie-standalone) tuples using their *simple format*.
An example of an instance:
```JSON
{
"sentence": "0.04593 kg Used a triple beam balance to mass a golf ball.",
"tuples": {
"score": 0.8999999761581421,
"tuple_text": "(0.04593 kg; Used; a triple beam balance; to mass a golf ball)",
"context": "",
"arg1": "0.04593 kg",
"rel": "Used",
"arg2s": ["a triple beam balance", "to mass a golf ball"],
}
}
```
### Data Fields
- `sentence`: the input text/sentence.
- `tuples`: the extracted relation tuples from the sentence.
- `score`: the confident score for each tuple.
- `tuple_text`: the relationship representation text of the extraction, in the *simple format* of [Open IE v4](https://github.com/allenai/openie-standalone).
- `context`: an optional representation of the context for this extraction. Defaults to `""` if there's no context.
- `arg1`: the first argument in the relationship.
- `rel`: the relation.
- `arg2s`: a sequence of the 2nd arguments in the realtionship.
### Data Splits
| name | train|
|-----------|-----:|
| all |267719|
| 4th_grade |158910|
| 8th_grade |108809|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@article{Khot2017AnsweringCQ,
title={Answering Complex Questions Using Open Information Extraction},
author={Tushar Khot and A. Sabharwal and Peter Clark},
journal={ArXiv},
year={2017},
volume={abs/1704.05572}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. | [
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scikit-learn/adult-census-income | scikit-learn | 2022-06-20T14:46:43Z | 536 | 1 | [
"license:cc0-1.0",
"region:us"
] | null | 2022-06-20T14:33:51Z | ---
license: cc0-1.0
---
## Adult Census Income Dataset
The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/adult).
This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)). The prediction task is to determine whether a person makes over $50K a year.
**Description of fnlwgt (final weight)**
The weights on the Current Population Survey (CPS) files are controlled to independent estimates of the civilian noninstitutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are:
- A single cell estimate of the population 16+ for each state.
- Controls for Hispanic Origin by age and sex.
- Controls by Race, age and sex.
We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used. The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population. People with similar demographic characteristics should have similar weights. There is one important caveat to remember about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own probability of selection, the statement only applies within state. | [
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Agisight/tyvan-russian-parallel-50k | Agisight | 2023-10-17T13:40:34Z | 536 | 1 | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:ru",
"language:tyv",
"license:cc-by-sa-4.0",
"region:us"
] | [
"translation"
] | 2023-10-17T10:56:36Z | ---
license: cc-by-sa-4.0
task_categories:
- translation
language:
- ru
- tyv
size_categories:
- 10K<n<100K
---
A 50K sample from the Russian-Tyvan parallel corpus collected at https://tyvan.ru.
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harem | null | 2023-01-25T14:31:29Z | 535 | 6 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:pt",
"license:unknown",
"region:us"
] | [
"token-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: HAREM
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PESSOA
'2': I-PESSOA
'3': B-ORGANIZACAO
'4': I-ORGANIZACAO
'5': B-LOCAL
'6': I-LOCAL
'7': B-TEMPO
'8': I-TEMPO
'9': B-VALOR
'10': I-VALOR
'11': B-ABSTRACCAO
'12': I-ABSTRACCAO
'13': B-ACONTECIMENTO
'14': I-ACONTECIMENTO
'15': B-COISA
'16': I-COISA
'17': B-OBRA
'18': I-OBRA
'19': B-OUTRO
'20': I-OUTRO
splits:
- name: train
num_bytes: 1506373
num_examples: 121
- name: test
num_bytes: 1062714
num_examples: 128
- name: validation
num_bytes: 51318
num_examples: 8
download_size: 1887281
dataset_size: 2620405
- config_name: selective
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PESSOA
'2': I-PESSOA
'3': B-ORGANIZACAO
'4': I-ORGANIZACAO
'5': B-LOCAL
'6': I-LOCAL
'7': B-TEMPO
'8': I-TEMPO
'9': B-VALOR
'10': I-VALOR
splits:
- name: train
num_bytes: 1506373
num_examples: 121
- name: test
num_bytes: 1062714
num_examples: 128
- name: validation
num_bytes: 51318
num_examples: 8
download_size: 1715873
dataset_size: 2620405
---
# Dataset Card for HAREM
## 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:** [HAREM homepage](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html)
- **Repository:** [HAREM repository](https://www.linguateca.pt/primeiroHAREM/harem_coleccaodourada_en.html)
- **Paper:** [HAREM: An Advanced NER Evaluation Contest for Portuguese](http://comum.rcaap.pt/bitstream/10400.26/76/1/SantosSecoCardosoVilelaLREC2006.pdf)
- **Point of Contact:** [Diana Santos](mailto:[email protected])
### Dataset Summary
The HAREM is a Portuguese language corpus commonly used for Named Entity Recognition tasks. It includes about 93k words, from 129 different texts,
from several genres, and language varieties. The split of this dataset version follows the division made by [1], where 7% HAREM
documents are the validation set and the miniHAREM corpus (with about 65k words) is the test set. There are two versions of the dataset set,
a version that has a total of 10 different named entity classes (Person, Organization, Location, Value, Date, Title, Thing, Event,
Abstraction, and Other) and a "selective" version with only 5 classes (Person, Organization, Location, Value, and Date).
It's important to note that the original version of the HAREM dataset has 2 levels of NER details, namely "Category" and "Sub-type".
The dataset version processed here ONLY USE the "Category" level of the original dataset.
[1] Souza, Fábio, Rodrigo Nogueira, and Roberto Lotufo. "BERTimbau: Pretrained BERT Models for Brazilian Portuguese." Brazilian Conference on Intelligent Systems. Springer, Cham, 2020.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Portuguese
## Dataset Structure
### Data Instances
```
{
"id": "HAREM-871-07800",
"ner_tags": [3, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4,
],
"tokens": [
"Abraço", "Página", "Principal", "ASSOCIAÇÃO", "DE", "APOIO", "A", "PESSOAS", "COM", "VIH", "/", "SIDA"
]
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PESSOA", "I-PESSOA", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-LOCAL", "I-LOCAL", "B-TEMPO", "I-TEMPO", "B-VALOR", "I-VALOR", "B-ABSTRACCAO", "I-ABSTRACCAO", "B-ACONTECIMENTO", "I-ACONTECIMENTO", "B-COISA", "I-COISA", "B-OBRA", "I-OBRA", "B-OUTRO", "I-OUTRO"
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word.
### Data Splits
The data is split into train, validation and test set for each of the two versions (default and selective). The split sizes are as follow:
| Train | Val | Test |
| ------ | ----- | ---- |
| 121 | 8 | 128 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{santos2006harem,
title={Harem: An advanced ner evaluation contest for portuguese},
author={Santos, Diana and Seco, Nuno and Cardoso, Nuno and Vilela, Rui},
booktitle={quot; In Nicoletta Calzolari; Khalid Choukri; Aldo Gangemi; Bente Maegaard; Joseph Mariani; Jan Odjik; Daniel Tapias (ed) Proceedings of the 5 th International Conference on Language Resources and Evaluation (LREC'2006)(Genoa Italy 22-28 May 2006)},
year={2006}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. | [
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cakiki/args_me | cakiki | 2022-10-25T09:07:25Z | 535 | 1 | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:cc-by-4.0",
"region:us"
] | [
"text-retrieval"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- '''en-US'''
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Webis args.me argument corpus
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
---
# Dataset Card for the args.me corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Usage](#dataset-usage)
- [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://zenodo.org/record/4139439
- **Repository:** https://git.webis.de/code-research/arguana/args/args-framework
- **Paper:** [Building an Argument Search Engine for the Web](https://webis.de/downloads/publications/papers/wachsmuth_2017f.pdf)
- **Leaderboard:** https://touche.webis.de/
- **Point of Contact:** [Webis Group](https://webis.de/people.html)
### Dataset Summary
The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal.
### Dataset Usage
```python
import datasets
args = datasets.load_dataset('cakiki/args_me', 'corpus', streaming=True)
args_iterator = iter(args)
for arg in args_iterator:
print(args['conclusion'])
print(args['id'])
print(args['argument'])
print(args['stance'])
break
```
### Supported Tasks and Leaderboards
Document Retrieval, Argument Retrieval for Controversial Questions
### Languages
The args.me corpus is monolingual; it only includes English (mostly en-US) documents.
## Dataset Structure
### Data Instances
#### Corpus
```
{'conclusion': 'Science is the best!',
'id': 'd6517702-2019-04-18T12:36:24Z-00000-000',
'argument': 'Science is aright I guess, but Physical Education (P.E) is better. Think about it, you could sit in a classroom for and hour learning about molecular reconfiguration, or you could play football with your mates. Why would you want to learn about molecular reconfiguration anyway? I think the argument here would be based on, healthy mind or healthy body. With science being the healthy mind and P.E being the healthy body. To work this one out all you got to do is ask Steven Hawkins. Only 500 words',
'stance': 'CON'}
```
### 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
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@dataset{yamen_ajjour_2020_4139439,
author = {Yamen Ajjour and
Henning Wachsmuth and
Johannes Kiesel and
Martin Potthast and
Matthias Hagen and
Benno Stein},
title = {args.me corpus},
month = oct,
year = 2020,
publisher = {Zenodo},
version = {1.0-cleaned},
doi = {10.5281/zenodo.4139439},
url = {https://doi.org/10.5281/zenodo.4139439}
}
```
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SiberiaSoft/SiberianPersonaChat | SiberiaSoft | 2023-08-02T18:16:20Z | 535 | 11 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
] | [
"text-generation",
"text2text-generation",
"conversational"
] | 2023-07-22T03:46:53Z | ---
license: mit
task_categories:
- text-generation
- text2text-generation
- conversational
language:
- ru
size_categories:
- 100K<n<1M
---
### SiberiaSoft/SiberianPersonaChat
Датасет инструкций, диалогов, QA
Данный датасет был создан для диалоговых агентов с имитацией личности.
Большая часть датасета была сгенерирована с помощью chatGPT и различных промптов к ней. Кроме этого, в состав датасета входит измененный [TolokaPersonaChatRus](https://toloka.ai/datasets/?category=nlp)
## Формат описаний личности
1. Ты парень, пилот самолета. Увлекаешься дайвингом. Собираешь марки. Любишь древнюю архитектуру.
2. Ты девушка, художница. Увлекаешься нейросетевым искусством. Умеешь программировать. Любишь рисовать.
Также в промпт можно подставлять факты о личности: ФИО, возраст и т.д
1. Я девушка 18 лет. Я учусь в институте. Живу с родителями. У меня есть кот. Ищу парня для семьи.
Статья на habr: [ссылка](https://habr.com/ru/articles/751580/)
## Процентное данных:
| Задача | Процентное содержание |
|:-----------------------:|:---------------------:|
| Персонализированные диалоги | 74.602% |
| Инструкции с its5Q/yandex-q | 4.585% |
| Инструкции с Den4ikAI/russian_instructions_2 | 3.328% |
| Инструкции с lksy/ru_instruct_gpt4 (жестко очищенные) | 3.274% |
| Инструкции с IlyaGusev/ru_turbo_alpaca_evol_instruct (очень жестко очищенные) | 3.237% |
| QA с длинными, развернутыми ответами | 3.236% |
| Ручные диалоги | 3.199% |
| QA с использованием Wikipedia | 2.628% |
| Ответы на вопросы по тексту Den4ikAI/ru_sberquad_long_answers | 1.784% |
| Решение проблем | 0.102% |
| QA Объясни ребенку | 0.025% |
### Citation
```
@MISC{SiberiaSoft/SiberianPersonaChat,
author = {Denis Petrov, Ivan Ramovich},
title = {Russian dataset for Chat models},
url = {https://huggingface.co/datasets/SiberiaSoft/SiberianPersonaChat},
year = 2023
}
```
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togethercomputer/Long-Data-Collections | togethercomputer | 2023-07-26T17:03:50Z | 535 | 66 | [
"license:other",
"region:us"
] | null | 2023-07-26T07:11:25Z | ---
license: other
---
# Dataset Summary
This collection is a compilation of long context datasets, specifically designed for tasks requiring extensive comprehension and inference from large text inputs.
Currently, it encompasses data intended for training a robust base model, which can be found in the pretrain/ directory. Additionally, it includes datasets tailored for specific needs, located in the fine-tune/ directory. These specialized datasets include multi-passage question answering, derived from Natural Questions, and long-context summarization, exemplified by the BookSum dataset.
# Detailed Description
## Pretrain Data
The pretraining data is a collection of diverse datasets utilized to train the AI model. These datasets include a variety of sources that provide a wide range of information, from books to scientific papers, and instruction data. Here's a detailed look at each:
### RedPajama-Book
This dataset is a specific slice of the larger RedPajama-Data-1T. The RedPajama-Book subset specifically focuses on data extracted from books. This broad and diverse range of literary content helps the model to understand and generate text in a wide variety of styles, genres, and topics, and especially in a wide range of context.
### RedPajama-ArXiv
The RedPajama-ArXiv dataset is another specific slice of RedPajama-Data-1T. In this dataset, the abstract corresponding to each paper is appended after the paper, providing a summary of the paper's content. This helps the model to leverage the long-range context.
### UL2 Oscar
This dataset is generated with LAION-AI's Open-Instruction-Generalist, asking the model to fill in missing chunks, or complete the text.
### RedPajama
This is a subset of the RedPajama-Data-1T. The RedPajama dataset is a large and diverse dataset that includes a wide variety of data sources. The specific subset used in this case (togethercomputer/RedPajama-Data-1T-Sample) is a representative sample of the larger dataset, providing a broad overview of the types of data included in RedPajama-Data-1T.
### NI
The Materialized Natural Instruction (NI) data is a dataset that focuses on natural language instructions. This dataset has been decontaminated against HELM core scenarios, meaning any data that matches specific scenarios outlined in the HELM core has been removed to avoid bias or overfitting. This dataset aids the model in understanding and generating instructional text.
### P3
The Materialized Public Pool of Prompts (P3) data is a dataset that includes a wide variety of user-generated prompts. This dataset has also been decontaminated against HELM core scenarios. The P3 dataset helps the model in understanding a broad set of user prompts and generating appropriate responses.
### Pile
The Pile dataset is a large and diverse dataset that includes a wide variety of data sources. The specific subset used in this case is a subsample of the larger Pile dataset.
## Fine-tune Data
### Multi-passage QA from Natural Questions:
This dataset is a multi-passage question answering dataset derived from the original Natural Questions (NQ) dataset by Google. The NQ dataset consists of real user queries issued to Google's search engine, paired with high-quality answers. In this derived version, each example consists of a question along with multiple (10-200) Wiki passages, from which the model must infer the correct answer. This dataset is designed to challenge and evaluate models on their ability to handle complex, multi-passage question answering.
### BookSum:
BookSum is a dataset for long context summarization. It includes a vast collection of books from various genres, and the task is to generate a coherent and concise summary given a long context from the book. This dataset is designed to test and train models on their ability to understand and summarize long, complex narratives.
# Dataset Limitations and Future Work
While these datasets provide a robust platform for training and evaluating models on long context tasks, they may still contain some limitations. For instance, the datasets might be biased towards the types of questions asked in Google's search engine and the genres of books included in the BookSum dataset. In the future, we plan to expand this collection to include more diverse datasets for a wider range of long context tasks.
# Licensing Information
Please refer to the original sources of the datasets for information on their respective licenses. | [
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jnlpba | null | 2023-04-14T13:49:49Z | 534 | 5 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-genia-v3.02",
"language:en",
"license:unknown",
"region:us"
] | [
"token-classification"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-genia-v3.02
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: BioNLP / JNLPBA Shared Task 2004
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-DNA
'2': I-DNA
'3': B-RNA
'4': I-RNA
'5': B-cell_line
'6': I-cell_line
'7': B-cell_type
'8': I-cell_type
'9': B-protein
'10': I-protein
config_name: jnlpba
splits:
- name: train
num_bytes: 8775707
num_examples: 18546
- name: validation
num_bytes: 1801565
num_examples: 3856
download_size: 3171072
dataset_size: 10577272
---
# Dataset Card for JNLPBA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/W04-1213.pdf
- **Leaderboard:** https://paperswithcode.com/sota/named-entity-recognition-ner-on-jnlpba?p=biobert-a-pre-trained-biomedical-language
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The data came from the GENIA version 3.02 corpus (Kim et al., 2003). This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors. From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
### Supported Tasks and Leaderboards
NER
### Languages
English
## Dataset Structure
### Data Instances
{
'id': '1',
'tokens': ['IL-2', 'gene', 'expression', 'and', 'NF-kappa', 'B', 'activation', 'through', 'CD28', 'requires', 'reactive', 'oxygen', 'production', 'by', '5-lipoxygenase', '.'],
'ner_tags': [1, 2, 0, 0, 9, 10, 0, 0, 9, 0, 0, 0, 0, 0, 9, 0],
}
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no bio-entity mentioned, `1` signals the first token of a bio-entity and `2` the subsequent bio-entity tokens.
### Data Splits
Train samples: 37094
Validation samples: 7714
## 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{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and
Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
### Contributions
Thanks to [@edugp](https://github.com/edugp) for adding this dataset. | [
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open-llm-leaderboard/details_huggyllama__llama-13b | open-llm-leaderboard | 2023-09-23T10:41:55Z | 534 | 0 | [
"region:us"
] | null | 2023-08-18T11:54:49Z | ---
pretty_name: Evaluation run of huggyllama/llama-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [huggyllama/llama-13b](https://huggingface.co/huggyllama/llama-13b) 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-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T10:41:44.150256](https://huggingface.co/datasets/open-llm-leaderboard/details_huggyllama__llama-13b/blob/main/results_2023-09-23T10-41-44.150256.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\": 0.000456667646266702,\n \"f1\": 0.056602348993288636,\n\
\ \"f1_stderr\": 0.0013004668300984712,\n \"acc\": 0.4191229752993855,\n\
\ \"acc_stderr\": 0.009626252314482865\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0019924496644295304,\n \"em_stderr\": 0.000456667646266702,\n\
\ \"f1\": 0.056602348993288636,\n \"f1_stderr\": 0.0013004668300984712\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0758150113722517,\n \
\ \"acc_stderr\": 0.007291205723162579\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803152\n\
\ }\n}\n```"
repo_url: https://huggingface.co/huggyllama/llama-13b
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_24T15_13_44.970123
path:
- '**/details_harness|arc:challenge|25_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|arc:challenge|25_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T10_41_44.150256
path:
- '**/details_harness|drop|3_2023-09-23T10-41-44.150256.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T10-41-44.150256.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T10_41_44.150256
path:
- '**/details_harness|gsm8k|5_2023-09-23T10-41-44.150256.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T10-41-44.150256.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hellaswag|10_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hellaswag|10_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:13:44.970123.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-19T22:15:08.436043.parquet'
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- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-19T22:15:08.436043.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
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- split: 2023_08_19T22_15_08.436043
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:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
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_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
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_2023-07-24T15:13:44.970123.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-19T22:15:08.436043.parquet'
- 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_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T15:13:44.970123.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T15:13:44.970123.parquet'
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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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- split: latest
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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- split: latest
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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- config_name: harness_hendrycksTest_high_school_psychology_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_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:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-24T15:13:44.970123.parquet'
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path:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T15:13:44.970123.parquet'
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path:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_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_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T15:13:44.970123.parquet'
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path:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-07-24T15:13:44.970123.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-24T15:13:44.970123.parquet'
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T15:13:44.970123.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
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- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_24T15_13_44.970123
path:
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- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
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- split: 2023_08_19T22_15_08.436043
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-19T22:15:08.436043.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-07-24T15:13:44.970123.parquet'
- split: 2023_08_19T22_15_08.436043
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-19T22:15:08.436043.parquet'
- split: latest
path:
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- config_name: harness_winogrande_5
data_files:
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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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- config_name: original_mmlu_abstract_algebra_5
data_files:
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path:
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- split: latest
path:
- '**/details_original|mmlu:abstract_algebra|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_anatomy_5
data_files:
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path:
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- split: latest
path:
- '**/details_original|mmlu:anatomy|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_astronomy_5
data_files:
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path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_business_ethics_5
data_files:
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path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_clinical_knowledge_5
data_files:
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path:
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- split: latest
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_college_biology_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_biology|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_college_chemistry_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_college_computer_science_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_college_mathematics_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_college_medicine_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_college_physics_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_computer_security_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_conceptual_physics_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_econometrics_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_electrical_engineering_5
data_files:
- split: 2023_08_28T19_54_33.085163
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_elementary_mathematics_5
data_files:
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path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_formal_logic_5
data_files:
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path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_global_facts_5
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path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T19:54:33.085163.parquet'
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path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_high_school_biology_5
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path:
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- split: latest
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_high_school_chemistry_5
data_files:
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path:
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- split: latest
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- config_name: original_mmlu_high_school_computer_science_5
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path:
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- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T19:54:33.085163.parquet'
- config_name: original_mmlu_high_school_european_history_5
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- config_name: original_mmlu_high_school_geography_5
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- config_name: original_mmlu_high_school_government_and_politics_5
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- split: latest
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- config_name: original_mmlu_high_school_macroeconomics_5
data_files:
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- config_name: original_mmlu_high_school_mathematics_5
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- config_name: original_mmlu_high_school_microeconomics_5
data_files:
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- split: latest
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- config_name: original_mmlu_high_school_physics_5
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- config_name: original_mmlu_high_school_psychology_5
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- config_name: original_mmlu_high_school_statistics_5
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- config_name: original_mmlu_high_school_us_history_5
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- config_name: original_mmlu_human_aging_5
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- config_name: original_mmlu_human_sexuality_5
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- config_name: original_mmlu_international_law_5
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- config_name: original_mmlu_jurisprudence_5
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- config_name: original_mmlu_logical_fallacies_5
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- config_name: original_mmlu_machine_learning_5
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- config_name: original_mmlu_management_5
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- config_name: original_mmlu_medical_genetics_5
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- config_name: original_mmlu_moral_scenarios_5
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- config_name: original_mmlu_prehistory_5
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- config_name: original_mmlu_professional_accounting_5
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- config_name: original_mmlu_professional_law_5
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path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T19:54:33.085163.parquet'
- split: latest
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- config_name: original_mmlu_professional_medicine_5
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- config_name: original_mmlu_public_relations_5
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path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T19:54:33.085163.parquet'
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path:
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- config_name: original_mmlu_security_studies_5
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- config_name: original_mmlu_us_foreign_policy_5
data_files:
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path:
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- config_name: original_mmlu_world_religions_5
data_files:
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path:
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path:
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- config_name: results
data_files:
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path:
- results_2023-07-24T15:13:44.970123.parquet
- split: 2023_08_19T22_15_08.436043
path:
- results_2023-08-19T22:15:08.436043.parquet
- split: 2023_08_28T19_54_33.085163
path:
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- split: 2023_09_23T10_41_44.150256
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path:
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---
# Dataset Card for Evaluation run of huggyllama/llama-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/huggyllama/llama-13b
- **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-13b](https://huggingface.co/huggyllama/llama-13b) 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-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T10:41:44.150256](https://huggingface.co/datasets/open-llm-leaderboard/details_huggyllama__llama-13b/blob/main/results_2023-09-23T10-41-44.150256.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": 0.000456667646266702,
"f1": 0.056602348993288636,
"f1_stderr": 0.0013004668300984712,
"acc": 0.4191229752993855,
"acc_stderr": 0.009626252314482865
},
"harness|drop|3": {
"em": 0.0019924496644295304,
"em_stderr": 0.000456667646266702,
"f1": 0.056602348993288636,
"f1_stderr": 0.0013004668300984712
},
"harness|gsm8k|5": {
"acc": 0.0758150113722517,
"acc_stderr": 0.007291205723162579
},
"harness|winogrande|5": {
"acc": 0.7624309392265194,
"acc_stderr": 0.011961298905803152
}
}
```
### 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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proto_qa | null | 2022-11-03T16:31:01Z | 533 | 1 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2005.00771",
"region:us"
] | [
"question-answering"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- other
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: protoqa
pretty_name: ProtoQA
dataset_info:
- config_name: proto_qa
features:
- name: normalized-question
dtype: string
- name: question
dtype: string
- name: answer-clusters
sequence:
- name: count
dtype: int32
- name: clusterid
dtype: string
- name: answers
sequence: string
- name: answerstrings
sequence: string
- name: totalcount
dtype: int32
- name: id
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3943484
num_examples: 8782
- name: validation
num_bytes: 472121
num_examples: 980
download_size: 7352932
dataset_size: 4415605
- config_name: proto_qa_cs
features:
- name: normalized-question
dtype: string
- name: question
dtype: string
- name: answers-cleaned
sequence:
- name: count
dtype: int32
- name: clusterid
dtype: string
- name: answers
sequence: string
- name: answerstrings
sequence: string
- name: totalcount
dtype: int32
- name: id
dtype: string
- name: source
dtype: string
splits:
- name: validation
num_bytes: 84466
num_examples: 52
download_size: 115704
dataset_size: 84466
- config_name: proto_qa_cs_assessments
features:
- name: question
dtype: string
- name: assessments
sequence: string
splits:
- name: validation
num_bytes: 12473
num_examples: 52
download_size: 24755
dataset_size: 12473
---
# 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
- **Interactive Demo:** [Interactive demo](http://protoqa.com)
- **Repository:** [proto_qa repository](https://github.com/iesl/protoqa-data)
- **Paper:** [proto_qa paper](https://arxiv.org/pdf/2005.00771.pdf)
- **Point of Contact:** [Michael Boratko](mailto:[email protected])
[Xiang Lorraine Li](mailto:[email protected])
[Tim O’Gorman](mailto:[email protected])
[Rajarshi Das](mailto:[email protected])
[Dan Le](mailto:[email protected])
[Andrew McCallum](mailto:[email protected])
### Dataset Summary
This dataset is for studying computational models trained to reason about prototypical situations. It is anticipated that still would not lead to usage in a downstream task, but as a way of studying the knowledge (and biases) of prototypical situations already contained in pre-trained models. The data it is partially based on (Family Feud).
Using deterministic filtering a sampling from a larger set of all transcriptions was built. Scraped data was acquired through fan transcriptions at [family feud](https://www.familyfeudinfo.com) and [family feud friends](http://familyfeudfriends.arjdesigns.com/); crowdsourced data was acquired with FigureEight (now Appen)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English
## Dataset Structure
### Data Instances
**What do the instances that comprise the dataset represent?**<br>
Each represents a survey question from Family Feud game and reported answer clusters
**How many instances are there in total?**<br>
9789 instances
**What data does each instance consist of?**<br>
Each instance is a question, a set of answers, and a count associated with each answer.
### Data Fields
**Data Files**<br>
Each line is a json dictionary, in which:<br>
**question** contains the question (in original and a normalized form)<br>
**answerstrings** contains the original answers provided by survey respondents (when available), along with the counts for each string. Because the FamilyFeud data has only cluster names rather than strings, those cluster names are included with 0 weight.<br>
**answer-clusters** list of clusters, with the count of each cluster and the strings included in that cluster. Each cluster is given a unique ID that can be linked to in the assessment files.
The simplified configuration includes:
- `question`: contains the original question
- `normalized-question`: contains the question in normalized form
- `totalcount`: unique identifier of the comment (can be used to look up the entry in the raw dataset)
- `id`: unique identifier of the commen
- `source`: unique identifier of the commen
- `answerstrings`: unique identifier of the commen
- `answer-clusters | answers-cleaned`: list clusters of:
* `clusterid`: Each cluster is given a unique ID that can be linked to in the assessment files
* `count`: the count of each cluster
* `answers`: the strings included in that cluster
In addition to the above, there is crowdsourced assessments file. The config "proto_qa_cs_assessments" provides mappings from additional human and model answers to clusters, to evaluate different assessment methods.
**Assessment files**<br>
The file **data/dev/crowdsource_dev.assessments.jsonl** contains mappings from additional human and model answers to clusters, to evaluate different assessment methods.
Each line contains:<br>
* `question`: contains the ID of the question
* `assessments`: maps individual strings to one of three options, either the answer cluster id, "invalid" if the answer is judged to be bad, or "valid_new_cluster" if the answer is valid but does not match any existing clusters.
### Data Splits
* proto_qa `Train` : 8781 instances for training or fine-tuning scraped from Family Feud fan sites (see paper). Scraped data has answer clusters with sizes, but only has a single string per cluster (corresponding to the original cluster name
* proto_qa `Validation` : 979 instances sampled from the same Family Feud data, for use in model validation and development.
* proto_qa_cs `Validation` :: 51 questions collected with exhaustive answer collection and manual clustering, matching the details of the eval test set (roughly 100 human answers per question)
**data/dev/crowdsource_dev.assessments.jsonl**: assessment file (format described above) for study of assessment methods.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
**How was the data associated with each instance acquired?**<br>
Scraped data was acquired through fan transcriptions at https://www.familyfeudinfo.com and http://familyfeudfriends.arjdesigns.com/ ; crowdsourced data was acquired with FigureEight (now Appen)
**If the dataset is a sample from a larger set, what was the sampling strategy?**<br>
Deterministic filtering was used (noted elsewhere), but no probabilistic sampling was used.
**Who was involved in the data collection process (e.g., students,crowdworkers , contractors) and how were they compensated?**<br>
Crowdworkers were used in the evalaution dataset. Time per task was calculated and per-task cost was set to attempt to provide a living wage
**Over what timeframe was the data collected?**<br>
Crowdsource answers were collected between Fall of 2018 and Spring of 2019. Scraped data covers question-answer pairs collected since the origin of the show in 1976
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
**Was any preprocessing/cleaning/labeling of the data done?**<br>
Obvious typos in the crowdsourced answer set were corrected
#### Who are the annotators?
The original question-answer pairs were generated by surveys of US English-speakers in a period from 1976 to present day. Crowd-sourced evaluation was constrained geographically to US English speakers but not otherwise constrained. Additional demographic data was not collected.
### Personal and Sensitive Information
**Does the dataset contain data that might be considered sensitive in any way?**<br>
As the questions address prototypical/stereotypical activities, models trained on more offensive material (such as large language models) may provide offensive answers to such questions. While we had found a few questions which we worried would actually encourage models to provide offensive answers, we cannot guarantee that the data is clean of such questions. Even a perfectly innocent version of this dataset would be encouraging models to express generalizations about situations, and therefore may provoke offensive material that is oontained in language models
**Does the dataset contain data that might be considered confidential?**<br>
The data does not concern individuals and thus does not contain any information to identify persons. Crowdsourced answers do not provide any user identifiers.
## Considerations for Using the Data
### Social Impact of Dataset
**Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**<br>
Not egregiously so (questions are all designed to be shown on television or replications thereof),
### Discussion of Biases
**Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses?**
<br>All original questions were written with US television audiences in mind, and therefore characterize prototypical situations with a specific lens. Any usages which deploy this to actually model prototypical situations globally will carry that bias.
**Are there tasks for which the dataset should not be used?**
<br>We caution regarding free-form use of this dataset for interactive "commonsense question answering" purposes without more study of the biases and stereotypes learned by such models.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The listed authors are maintaining/supporting the dataset. They pledge to help support issues, but cannot guarantee long-term support
### Licensing Information
The Proto_qa dataset is licensed under the [Creative Commons Attribution 4.0 International](https://github.com/iesl/protoqa-data/blob/master/LICENSE)
### Citation Information
```
@InProceedings{
huggingface:dataset,
title = {ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning},
authors = {Michael Boratko, Xiang Lorraine Li, Tim O’Gorman, Rajarshi Das, Dan Le, Andrew McCallum},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/iesl/protoqa-data},
}
```
### Contributions
Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset. | [
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gfissore/arxiv-abstracts-2021 | gfissore | 2022-10-27T17:08:00Z | 533 | 17 | [
"task_categories:summarization",
"task_categories:text-retrieval",
"task_categories:text2text-generation",
"task_ids:explanation-generation",
"task_ids:text-simplification",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"arxiv:1905.00075",
"region:us"
] | [
"summarization",
"text-retrieval",
"text2text-generation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: arxiv-abstracts-2021
size_categories:
- 1M<n<10M
source_datasets: []
task_categories:
- summarization
- text-retrieval
- text2text-generation
task_ids:
- explanation-generation
- text-simplification
- document-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
---
# Dataset Card for arxiv-abstracts-2021
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Clement et al., 2019, On the Use of ArXiv as a Dataset, https://arxiv.org/abs/1905.00075](https://arxiv.org/abs/1905.00075)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Giancarlo Fissore](mailto:[email protected])
### Dataset Summary
A dataset of metadata including title and abstract for all arXiv articles up to the end of 2021 (~2 million papers).
Possible applications include trend analysis, paper recommender engines, category prediction, knowledge graph construction and semantic search interfaces.
In contrast to [arxiv_dataset](https://huggingface.co/datasets/arxiv_dataset), this dataset doesn't include papers submitted to arXiv after 2021 and it doesn't require any external download.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
Here's an example instance:
```
{
"id": "1706.03762",
"submitter": "Ashish Vaswani",
"authors": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion\n Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin",
"title": "Attention Is All You Need",
"comments": "15 pages, 5 figures",
"journal-ref": null,
"doi": null,
"abstract": " The dominant sequence transduction models are based on complex recurrent or\nconvolutional neural
networks in an encoder-decoder configuration. The best\nperforming models also connect the encoder and decoder through
an attention\nmechanism. We propose a new simple network architecture, the Transformer, based\nsolely on attention
mechanisms, dispensing with recurrence and convolutions\nentirely. Experiments on two machine translation tasks show
these models to be\nsuperior in quality while being more parallelizable and requiring significantly\nless time to
train. Our model achieves 28.4 BLEU on the WMT 2014\nEnglish-to-German translation task, improving over the existing
best results,\nincluding ensembles by over 2 BLEU. On the WMT 2014 English-to-French\ntranslation task, our model
establishes a new single-model state-of-the-art\nBLEU score of 41.8 after training for 3.5 days on eight GPUs, a small
fraction\nof the training costs of the best models from the literature. We show that the\nTransformer generalizes well
to other tasks by applying it successfully to\nEnglish constituency parsing both with large and limited training
data.\n",
"report-no": null,
"categories": [
"cs.CL cs.LG"
],
"versions": [
"v1",
"v2",
"v3",
"v4",
"v5"
]
}
```
### Data Fields
These fields are detailed on the [arXiv](https://arxiv.org/help/prep):
- `id`: ArXiv ID (can be used to access the paper)
- `submitter`: Who submitted the paper
- `authors`: Authors of the paper
- `title`: Title of the paper
- `comments`: Additional info, such as number of pages and figures
- `journal-ref`: Information about the journal the paper was published in
- `doi`: [Digital Object Identifier](https://www.doi.org)
- `report-no`: Report Number
- `abstract`: The abstract of the paper
- `categories`: Categories / tags in the ArXiv system
### Data Splits
No splits
## Dataset Creation
### Curation Rationale
For about 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming, depth. In these times of unique global challenges, efficient extraction of insights from data is essential. The `arxiv-abstracts-2021` dataset aims at making the arXiv more easily accessible for machine learning applications, by providing important metadata (including title and abstract) for ~2 million papers.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The language producers are members of the scientific community at large, but not necessarily affiliated to any institution.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The full names of the papers' authors are included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The original data is maintained by [ArXiv](https://arxiv.org/)
### Licensing Information
The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
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texturedesign/td01_natural-ground-textures | texturedesign | 2023-09-02T10:21:04Z | 533 | 4 | [
"task_categories:unconditional-image-generation",
"annotations_creators:expert-generated",
"size_categories:n<1K",
"source_datasets:original",
"license:cc-by-nc-4.0",
"texture-synthesis",
"photography",
"non-infringing",
"region:us"
] | [
"unconditional-image-generation"
] | 2022-11-19T17:43:30Z | ---
annotations_creators:
- expert-generated
language: []
language_creators: []
license:
- cc-by-nc-4.0
multilinguality: []
pretty_name: 'TD01: Natural Ground Texture Photos'
size_categories:
- n<1K
source_datasets:
- original
tags:
- texture-synthesis
- photography
- non-infringing
task_categories:
- unconditional-image-generation
task_ids: []
viewer: false
---
_The Dataset Teaser is now enabled instead! Isn't this better?_
# TD 01: Natural Ground Textures
This dataset contains multi-photo texture captures in outdoor nature scenes — all focusing on the ground. Each set has different photos that showcase texture variety, making them ideal for training a domain-specific image generator!
Overall information about this dataset:
* **Format** — JPEG-XL, lossless RGB
* **Resolution** — 4032 × 2268
* **Device** — mobile camera
* **Technique** — hand-held
* **Orientation** — portrait or landscape
* **Author**: Alex J. Champandard
* **Configurations**: 4K, 2K (default), 1K
To load the medium- and high-resolution images of the dataset, you'll need to install `jxlpy` from [PyPI](https://pypi.org/project/jxlpy/) with `pip install jxlpy`:
```python
# Recommended use, JXL at high-quality.
from jxlpy import JXLImagePlugin
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'JXL@4K', num_proc=4)
print(len(d['train']), len(d['test']))
```
The lowest-resolution images are available as PNG with a regular installation of `pillow`:
```python
# Alternative use, PNG at low-quality.
from datasets import load_dataset
d = load_dataset('texturedesign/td01_natural-ground-textures', 'PNG@1K', num_proc=4)
# EXAMPLE: Discard all other sets except Set #1.
dataset = dataset.filter(lambda s: s['set'] == 1)
# EXAMPLE: Only keep images with index 0 and 2.
dataset = dataset.select([0, 2])
```
Use built-in dataset `filter()` and `select()` to narrow down the loaded dataset for training, or to ease with development.
## Set #1: Rock and Gravel
* **Description**:
- surface rocks with gravel and coarse sand
- strong sunlight from the left, sharp shadows
* **Number of Photos**:
- 7 train
- 2 test
* **Edits**:
- rotated photos to align sunlight
- removed infrequent objects
* **Size**: 77.8 Mb
## Set #2: Dry Grass with Pine Needles
* **Description**:
- field of dry grass and pine needles
- sunlight from the top right, some shadows
* **Number of Photos**:
- 6 train
- 1 test
* **Edits**:
- removed dry leaves and large plants
- removed sticks, rocks and sporadic daisies
* **Size**: 95.2 Mb
## Set #3: Chipped Stones, Broken Leaves and Twiglets
* **Description**:
- autumn path with chipped stones and dry broken leaves
- diffuse light on a cloudy day, very soft shadows
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed anything that looks green, fresh leaves
- removed long sticks and large/odd stones
* **Size**: 126.9 Mb
## Set #4: Grass Clumps and Cracked Dirt
* **Description**:
- clumps of green grass, clover and patches of cracked dirt
- diffuse light on cloudy day, shadows under large blades of grass
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- removed dry leaves, sporadic dandelions, and large objects
- histogram matching for two of the photos so the colors look similar
* **Size**: 126.8 Mb
## Set #5: Dirt, Stones, Rock, Twigs...
* **Description**:
- intricate micro-scene with grey dirt, surface rock, stones, twigs and organic debris
- diffuse light on cloudy day, soft shadows around the larger objects
* **Number of Photos**:
- 9 train
- 3 test
* **Edits**:
- removed odd objects that felt out-of-distribution
* **Size**: 102.1 Mb
## Set #6: Plants with Flowers on Dry Leaves
* **Description**:
- leafy plants with white flowers on a bed of dry brown leaves
- soft diffuse light, shaded areas under the plants
* **Number of Photos**:
- 9 train
- 2 test
* **Edits**:
- none yet, inpainting doesn't work well enough
- would remove long sticks and pieces of wood
* **Size**: 105.1 Mb
## Set #7: Frozen Footpath with Snow
* **Description**:
- frozen ground on a path with footprints
- areas with snow and dark brown ground beneath
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 11 train
- 3 test
* **Size**: 95.5 Mb
## Set #8: Pine Needles Forest Floor
* **Description**:
- forest floor with a mix of brown soil and grass
- variety of dry white leaves, sticks, pinecones, pine needles
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 15 train
- 4 test
* **Size**: 160.6 Mb
## Set #9: Snow on Grass and Dried Leaves
* **Description**:
- field in a park with short green grass
- large dried brown leaves and fallen snow on top
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 8 train
- 3 test
* **Size**: 99.8 Mb
## Set #10: Brown Leaves on Wet Ground
* **Description**:
- fallew brown leaves on wet ground
- occasional tree root and twiglets
- diffuse lighting on a rainy day
* **Number of Photos**:
- 17 train
- 4 test
* **Size**: 186.2 Mb
## Set #11: Wet Sand Path with Debris
* **Description**:
- hard sandy path in the rain
- decomposing leaves and other organic debris
- diffuse lighting on a rainy day
* **Number of Photos**:
- 17 train
- 4 test
* **Size**: 186.2 Mb
## Set #12: Wood Chips & Sawdust Sprinkled on Forest Path
* **Description**:
- wood chips, sawdust, twigs and roots on forest path
- intermittent sunlight with shadows of trees
* **Number of Photos**:
- 8 train
- 2 test
* **Size**: 110.4 Mb
## Set #13: Young Grass Growing in the Dog Park
* **Description**:
- young grass growing in a dog park after overnight rain
- occasional stones, sticks and twigs, pine needles
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 17 train
- 4 test
* **Size**: 193.4 Mb
## Set #14: Wavy Wet Beach Sand
* **Description**:
- wavy wet sand on the beach after the tide retreated
- some dirt and large pieces algae debris
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 11 train
- 3 test
* **Size**: 86.5 Mb
## Set #15: Dry Dirt Road and Debris from Trees
* **Description**:
- dirt road of dry compacted sand with debris on top
- old pine needles and dry brown leaves
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 8 train
- 2 test
* **Size**: 86.9 Mb
## Set #16: Sandy Beach Path with Grass Clumps
* **Description**:
- path with sand and clumps grass heading towards the beach
- occasional blueish stones, leafy weeds, and yellow flowers
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 10 train
- 3 test
* **Size**: 118.8 Mb
## Set #17: Pine Needles and Brown Leaves on Park Floor
* **Description**:
- park floor with predominantly pine needles
- brown leaves from nearby trees, green grass underneath
- diffuse lighting on a cloudy day
* **Number of Photos**:
- 8 train
- 2 test
* **Size**: 99.9 Mb
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augtoma/medqa_usmle | augtoma | 2023-08-11T20:50:07Z | 533 | 0 | [
"region:us"
] | null | 2023-08-11T20:49:29Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: meta_info
dtype: string
- name: answer_idx
dtype: string
- name: metamap_phrases
sequence: string
splits:
- name: train
num_bytes: 15175834
num_examples: 10178
- name: test
num_bytes: 1946030
num_examples: 1273
download_size: 8869925
dataset_size: 17121864
---
# Dataset Card for "medqa_usmle"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bigscience-historical-texts/hipe2020 | bigscience-historical-texts | 2023-02-07T08:54:43Z | 532 | 3 | [
"language:de",
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demelin/moral_stories | demelin | 2022-07-17T15:29:10Z | 532 | 12 | [
"task_categories:multiple-choice",
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"task_ids:text-scoring",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | [
"multiple-choice",
"text-generation",
"text-classification",
"commonsense-reasoning",
"moral-reasoning",
"social-reasoning"
] | 2022-07-14T11:19:52Z | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- crowdsourced
license:
- mit
multilinguality:
- monolingual
pretty_name: Moral Stories
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
- text-generation
- text-classification
- commonsense-reasoning
- moral-reasoning
- social-reasoning
task_ids:
- multiple-choice-qa
- language-modeling
- text-scoring
---
# Dataset Card for Moral Stories
## 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:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Repository:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Paper:** [Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences](https://aclanthology.org/2021.emnlp-main.54/)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)
### Dataset Summary
Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.
Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Supported Tasks and Leaderboards
- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A [BART model](https://huggingface.co/facebook/bart-large) can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A [RoBERTa-based model](https://huggingface.co/roberta-base) can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A [GPT-based model](https://huggingface.co/EleutherAI/gpt-neo-2.7B) can be used for this purpose.
### Languages
*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the [*Ethical Considerations* section of the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Dataset Structure
### Data Instances
Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf). Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):
"full" instance:
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",
"norm": "It's kind to wait for people.",
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",
"intention": "Josh needs to get to work.",
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",
"norm": "It's bad to be too loud and wake people up.",
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",
"label": "1"}
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",
"norm": "It's bad to be too loud and wake people up.",
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",
"label": "0"}
"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",
"norm": "It's mean to hit a bear with a guitar.",
"situation": "Joe was camping in Montana with his band when he came across a black bear.",
"intention": "Joe wanted to scare away the black bear.",
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",
"label": "1"}
### Data Fields
- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1")
### Data Splits
For classification tasks, we examined three data split strategies:
- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding
norms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set.
- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.
- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set.
For generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to [*Section 3* and *Appendix C* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Dataset Creation
### Curation Rationale
Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Source Data
#### Initial Data Collection and Normalization
Please refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
#### Who are the source language producers?
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Annotations
#### Annotation process
Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
#### Who are the annotators?
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Discussion of Biases
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Other Known Limitations
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Additional Information
### Dataset Curators
[Denis Emelin](demelin.github.io)
### Licensing Information
MIT
### Citation Information
@article{Emelin2021MoralSS,
title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},
author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},
journal={ArXiv},
year={2021},
volume={abs/2012.15738}
} | [
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semeru/code-code-DefectDetection | semeru | 2023-03-27T21:16:02Z | 532 | 0 | [
"license:mit",
"region:us"
] | null | 2023-03-22T03:30:09Z | ---
license: mit
Programminglanguage: "C"
version: "N/A"
Date: "Devign(Jun 2019 - paper release date)"
Contaminated: "Very Likely"
Size: "Standard Tokenizer"
---
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru
# CodeXGLUE -- Defect Detection
## Task Definition
Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
### Dataset
The dataset we use comes from the paper [*Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). We combine all projects and split 80%/10%/10% for training/dev/test.
### Data Format
Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **func:** the source code
- **target:** 0 or 1 (vulnerability or not)
- **idx:** the index of example
### Data Statistics
Data statistics of the dataset are shown in the below table:
| | #Examples |
| ----- | :-------: |
| Train | 21,854 |
| Dev | 2,732 |
| Test | 2,732 |
## Reference
<pre><code>@inproceedings{zhou2019devign,
title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
booktitle={Advances in Neural Information Processing Systems},
pages={10197--10207},
year={2019}
}</code></pre>
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result-kand2-sdxl-wuerst-karlo/8a14fb4c | result-kand2-sdxl-wuerst-karlo | 2023-10-06T19:06:51Z | 532 | 0 | [
"region:us"
] | null | 2023-10-06T19:06:50Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 174
num_examples: 10
download_size: 1325
dataset_size: 174
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "8a14fb4c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tweets_ar_en_parallel | null | 2023-01-25T14:54:55Z | 531 | 3 | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"language:en",
"license:apache-2.0",
"tweets-translation",
"region:us"
] | [
"translation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
- no-annotation
language_creators:
- found
language:
- ar
- en
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bilingual-corpus-of-arabic-english-parallel
pretty_name: Bilingual Corpus of Arabic-English Parallel Tweets
tags:
- tweets-translation
dataset_info:
- config_name: parallelTweets
features:
- name: ArabicTweetID
dtype: int64
- name: EnglishTweetID
dtype: int64
splits:
- name: test
num_bytes: 2667296
num_examples: 166706
download_size: 2937626
dataset_size: 2667296
- config_name: accountList
features:
- name: account
dtype: string
splits:
- name: test
num_bytes: 20108
num_examples: 1389
download_size: 2937626
dataset_size: 20108
- config_name: countryTopicAnnotation
features:
- name: account
dtype: string
- name: country
dtype:
class_label:
names:
'0': QA
'1': BH
'2': AE
'3': OM
'4': SA
'5': PL
'6': JO
'7': IQ
'8': Other
'9': EG
'10': KW
'11': SY
- name: topic
dtype:
class_label:
names:
'0': Gov
'1': Culture
'2': Education
'3': Sports
'4': Travel
'5': Events
'6': Business
'7': Science
'8': Politics
'9': Health
'10': Governoment
'11': Media
splits:
- name: test
num_bytes: 6036
num_examples: 200
download_size: 2937626
dataset_size: 6036
---
# Dataset Card for Bilingual Corpus of Arabic-English Parallel Tweets
## 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:** [Bilingual Corpus of Arabic-English Parallel Tweets](https://alt.qcri.org/resources/bilingual_corpus_of_parallel_tweets)
- **Repository:**
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.bucc-1.3/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Twitter users often post parallel tweets—tweets that contain the same content but are written in different languages. Parallel tweets can be an important resource for developing machine translation (MT) systems among other natural language processing (NLP) tasks. This resource is a result of a generic method for collecting parallel tweets. Using the method, we compiled a bilingual corpus of English-Arabic parallel tweets and a list of Twitter accounts who post English-Arabic tweets regularly. Additionally, we annotate a subset of Twitter accounts with their countries of origin and topic of interest, which provides insights about the population who post parallel tweets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
parallelTweets:
```
{
"ArabicTweetID": 981111245209243600,
"EnglishTweetID": 981111450432401400
}
```
accountList:
```
{
'account': 'HukoomiQatar'
}
```
countryTopicAnnotation:
```
{
'account': 'HukoomiQatar',
'country': 'QA',
'topic': 'Gov'
}
```
### Data Fields
parallelTweets:
- `ArabicTweetID` (int)
- `EnglishTweetID` (int)
accountList:
- `account` (str)
countryTopicAnnotation:
- `account` (str)
- `country` (class label): One of:
- "QA",
- "BH",
- "AE",
- "OM",
- "SA",
- "PL",
- "JO",
- "IQ",
- "Other",
- "EG",
- "KW",
- "SY"
- `topic` (class label): One of:
- "Gov",
- "Culture",
- "Education",
- "Sports",
- "Travel",
- "Events",
- "Business",
- "Science",
- "Politics",
- "Health",
- "Governoment",
- "Media",
### Data Splits
All configuration have only one split: "test".
## 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
It is licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@inproceedings{Mubarak2020bilingualtweets,
title={Constructing a Bilingual Corpus of Parallel Tweets},
author={Mubarak, Hamdy and Hassan, Sabit and Abdelali, Ahmed},
booktitle={Proceedings of 13th Workshop on Building and Using Comparable Corpora (BUCC)},
address={Marseille, France},
year={2020}
}
```
[More Information Needed]
### Contributions
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. | [
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shibing624/medical | shibing624 | 2023-06-02T07:03:41Z | 531 | 170 | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"language:en",
"license:apache-2.0",
"text-generation",
"region:us"
] | [
"text-generation"
] | 2023-05-22T14:45:06Z | ---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation
pretty_name: medical
task_categories:
- text-generation
size_categories:
- 1M<n<10M
---
# Dataset Card for medical
中文医疗数据集
- LLM Supervised Finetuning repository: https://github.com/shibing624/textgen
- MeidcalGPT repository: https://github.com/shibing624/MedicalGPT
## Dataset Description
medical is a Chinese Medical dataset. 医疗数据集，可用于医疗领域大模型训练。
```
tree medical
|-- finetune # 监督微调数据集，可用于SFT和RLHF
| |-- test_en_1.json
| |-- test_zh_0.json
| |-- train_en_1.json
| |-- train_zh_0.json
| |-- valid_en_1.json
| `-- valid_zh_0.json
|-- medical.py # hf dataset 数据展示用
|-- pretrain # 二次预训练数据集
| |-- medical_book_zh.json
| |-- test_encyclopedia.json
| |-- train_encyclopedia.json
| `-- valid_encyclopedia.json
|-- README.md
`-- reward # 奖励模型数据集
|-- test.json
|-- train.json
`-- valid.json
```
### Original Dataset Summary
#### pretrain
- train_encyclopedia.json: 共36万条，来自医疗百科数据[FreedomIntelligence/huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa) , 拼接 questions 和 answers，形成 text 文本字段，语句通顺，用于预训练注入医疗知识。
- medical_book_zh.json: 共8475条，来自医疗教材的文本数据，来源：https://github.com/jind11/MedQA， 原始数据集：[google drive](https://drive.google.com/u/0/uc?export=download&confirm=t&id=1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw) ，只对长段落切分为2048字的小段落了。
#### finetune
- train_zh_0.json: 共195万条，来自1）中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的六个科室医疗问诊数据，
有79万条；2）在线医疗百科 huatuo_encyclopedia_qa ，有36万条；3）医疗知识图谱 huatuo_knowledge_graph_qa，有79万条。三部分合并，共195万条。
- train_en_1.json：共11万条，来自英文医疗问诊对话数据[Kent0n-Li/ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor)，合并了HealthCareMagic-100k、GenMedGPT-5k 数据集，共11万条。
#### reward
- train.json 共4000条，问题来自中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的随机4000条提问，`response_chosen`来自该数据集的医生答复，
`response_rejected`来自本草模型[SCIR-HI/Huatuo-Llama-Med-Chinese](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)的答复。
### Supported Tasks and Leaderboards
中文医疗对话模型
The dataset designed for medical task training pretrained language models.
### Languages
The data are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
head pretrain/train_encyclopedia.json
```json
{"text": "怀孕后嘴巴很淡怎么办？有孕妇在怀孕之后，发现自己嘴巴比较淡，出现这种情况的原因其实也非常的复杂，首先和妊娠反应有直接的关系，这是一种正常的情况，另外有些孕妇平常不注意口腔卫生，舌苔比较厚或者自己有了一些消化系统方面的疾病，这就要求人们必须要及时的进行处理。女性在怀孕之后，身体就会出现一些明显的变化，首先人们月经会停止，另外也会有恶心、呕吐等一些妊娠反应，不过这些都是正常的。有些孕妇发现自己在怀孕之后，口味发生了很大的变化，嘴巴变得非常的淡。其实这也和激素变化有直接的关系，可能是妊娠反应所致，在怀孕期间，因为受到体内激素水平的变化，所以就会有肠胃系统的改变，人们可能会出现食欲不振，消化不良等症状表现，也有一些孕妇会发现自己嘴巴没有味道，会有口苦的症状，而这也是正常的孕期反应，人们在平常要多喝一些水，多吃一些清淡营养的食物慢慢就会改善。也有可能是舌苔过厚所致，孕妇嘴巴里面没有味道，很有可能是舌苔比较重、舌苔过厚导致的，这样就会影响到味蕾对味道的敏感度，不仅嘴巴里面没有味道，甚至有些孕妇在说话的时候也会有口臭，这就要求人们在每天早上漱口的时候，必须要用牙刷刷舌苔开始，不要清理的特别深，以免人们会有呕吐，慢慢习惯之后再往深一点的清洗，一般2到3天就会得到改善。嘴巴感到比较淡，其实也和脾胃虚寒有直接的关系，消化系统疾病，内分泌疾病，营养不良等，但有可能导致舌头部位因为味蕾的敏感度下降，产生口淡之感，患者会有食欲不振的表现，发现病症及时就诊治疗。"}
```
head finetune/train_zh_0.json
```json
{"instruction": "曲匹地尔片的用法用量", "input": "", "output": "注意：同种药品可由于不同的包装规格有不同的用法或用量。本文只供参考。如果不确定，请参看药品随带的说明书或向医生询问。口服。一次50～100mg（1-2片），3次/日，或遵医嘱。"}
```
head finetune/train_en_1.json
```json
{"instruction": "If you are a doctor, please answer the medical questions based on the patient's description.", "input": "Doctor, I have been experiencing sudden and frequent panic attacks. I don't know what to do.", "output": "Well, based on what you're telling me, it sounds like you may be suffering from panic disorder. The best course of action is to start with psychotherapy and mental health counseling. Additionally, we should conduct an electrocardiogram to make sure that there are no physical issues causing your panic attacks. We will also need to perform a depression screen and a toxicology screen to rule out any other underlying causes. Finally, I would recommend a comprehensive psychological and psychiatric evaluation and therapy to help manage your symptoms."}
```
head reward/train.json
```json
{"question": "孩子感统失调，学习不行怎么办？，孩子说话晚，走路晚，现在小学二年级，学习跟不上，理解力差，运动协调性差，家里很着急，不知怎么办。", "response_chosen": "病情分析：你好!孩子说话晚，走路也晚，很可能是大脑本身发育不好引起的发育迟缓。而五岁时所致的智力检查为临界范围，那就是说孩子的智商是有问题的，也应考虑与大脑发育不好有关。指导意见：人的大脑在头一年发育最快，可塑性最强，在头三年可塑性还是可以的，超过三岁再进行训练，效果就不怎么好了。建议再给孩子做一做智力测试，如果孩子的智商还是在临界范围，那就要考虑让孩子去特殊学校进行康复训练，而不是继续在普通小学就读，否则对孩子来说，就是强人所难了。希望自己的孩子能聪明，这是每个家长都会有的心愿，但如果孩子自身的条件就是不能跟上同龄孩子，那家长也要面对这个事实的，对吗？医生询问：", "response_rejected": "建议家长先带孩子去正规医院做全面检查以确定病因和病情严重程度；同时可以进行物理治疗、康复训练等辅助治疗方法。"}
```
### Data Fields
#### 预训练数据集 pretrain
字段解释：
- text: 文本
#### 指令微调数据集 finetune
字段解释：
- instruction: 指令
- input：问题（可为空）
- output：答复
#### 奖励模型数据集 reward
字段解释：
- question: 问题
- response_chosen: 优质回答
- response_rejected: 低质回答
### Data Splits
```
> wc -l medical/*/*
500 medical/finetune/test_en_1.json
500 medical/finetune/test_zh_0.json
116617 medical/finetune/train_en_1.json
1949972 medical/finetune/train_zh_0.json
500 medical/finetune/valid_en_1.json
500 medical/finetune/valid_zh_0.json
8475 medical/pretrain/medical_book_zh.json
500 medical/pretrain/test_encyclopedia.json
361420 medical/pretrain/train_encyclopedia.json
500 medical/pretrain/valid_encyclopedia.json
100 medical/reward/test.json
3800 medical/reward/train.json
100 medical/reward/valid.json
2443484 total
```
### Licensing Information
The dataset is available under the Apache 2.0.
### Citation Information
- https://github.com/Toyhom/Chinese-medical-dialogue-data
- https://github.com/FreedomIntelligence/Huatuo-26M/blob/main/README_zh-CN.md
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa
- https://github.com/Kent0n-Li/ChatDoctor
附上几个优质的reward model dataset:
- https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise
- https://huggingface.co/datasets/sunzeyeah/chinese_chatgpt_corpus
- https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12
- https://huggingface.co/datasets/Dahoas/rm-static
### Contributions
[shibing624](https://github.com/shibing624) 整理并上传
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open-llm-leaderboard/details_openlm-research__open_llama_3b | open-llm-leaderboard | 2023-10-18T03:50:53Z | 530 | 0 | [
"region:us"
] | null | 2023-08-18T11:44:30Z | ---
pretty_name: Evaluation run of openlm-research/open_llama_3b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b)\
\ 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 6 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_openlm-research__open_llama_3b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-18T03:50:40.523576](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_3b/blob/main/results_2023-10-18T03-50-40.523576.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\": 0.0005243288590604027,\n\
\ \"em_stderr\": 0.00023443780464835776,\n \"f1\": 0.050632340604026965,\n\
\ \"f1_stderr\": 0.001271781500579302,\n \"acc\": 0.32587350322198844,\n\
\ \"acc_stderr\": 0.00764164157289629\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0005243288590604027,\n \"em_stderr\": 0.00023443780464835776,\n\
\ \"f1\": 0.050632340604026965,\n \"f1_stderr\": 0.001271781500579302\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.004548900682335102,\n \
\ \"acc_stderr\": 0.0018535550440036204\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6471981057616417,\n \"acc_stderr\": 0.01342972810178896\n\
\ }\n}\n```"
repo_url: https://huggingface.co/openlm-research/open_llama_3b
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_18T11_22_49.433588
path:
- '**/details_harness|arc:challenge|25_2023-07-18T11:22:49.433588.parquet'
- split: 2023_07_19T10_43_17.176281
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:43:17.176281.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_18T03_50_40.523576
path:
- '**/details_harness|drop|3_2023-10-18T03-50-40.523576.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-18T03-50-40.523576.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_18T03_50_40.523576
path:
- '**/details_harness|gsm8k|5_2023-10-18T03-50-40.523576.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-18T03-50-40.523576.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_18T11_22_49.433588
path:
- '**/details_harness|hellaswag|10_2023-07-18T11:22:49.433588.parquet'
- split: 2023_07_19T10_43_17.176281
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:43:17.176281.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_18T11_22_49.433588
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T11:22:49.433588.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T11:22:49.433588.parquet'
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- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T11:22:49.433588.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T11:22:49.433588.parquet'
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- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:43:17.176281.parquet'
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path:
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:43:17.176281.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:43:17.176281.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:43:17.176281.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:43:17.176281.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:43:17.176281.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:43:17.176281.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:43:17.176281.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:43:17.176281.parquet'
- 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:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:43:17.176281.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T11:22:49.433588.parquet'
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:43:17.176281.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T11:22:49.433588.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_college_computer_science_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_mathematics_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_medicine_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T11:22:49.433588.parquet'
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path:
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path:
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- config_name: harness_hendrycksTest_conceptual_physics_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_econometrics_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:43:17.176281.parquet'
- 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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data_files:
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path:
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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:
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- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:43:17.176281.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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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:43:17.176281.parquet'
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data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:43:17.176281.parquet'
- 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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- split: latest
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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- split: 2023_07_19T10_43_17.176281
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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- split: 2023_07_19T10_43_17.176281
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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- split: 2023_07_19T10_43_17.176281
path:
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- split: latest
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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- split: latest
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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- split: latest
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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- split: latest
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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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_psychology_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_statistics_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_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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- split: latest
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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- split: latest
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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- split: latest
path:
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- config_name: harness_hendrycksTest_jurisprudence_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_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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- split: latest
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:
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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:
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path:
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path:
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- config_name: harness_hendrycksTest_miscellaneous_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_moral_disputes_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_nutrition_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_philosophy_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_prehistory_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_professional_accounting_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_professional_law_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_professional_medicine_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_professional_psychology_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_public_relations_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_security_studies_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_sociology_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_us_foreign_policy_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:43:17.176281.parquet'
- config_name: harness_hendrycksTest_world_religions_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_truthfulqa_mc_0
data_files:
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path:
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path:
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path:
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- config_name: harness_winogrande_5
data_files:
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path:
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path:
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- config_name: original_mmlu_5
data_files:
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path:
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path:
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path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T14:52:20.646698.parquet'
- split: 2023_08_28T20_31_38.653587
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:31:38.653587.parquet'
- split: 2023_08_29T12_36_41.239310
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-29T12:36:41.239310.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-29T12:36:41.239310.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T14_52_20.646698
path:
- '**/details_original|mmlu:virology|5_2023-08-28T14:52:20.646698.parquet'
- split: 2023_08_28T20_31_38.653587
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:31:38.653587.parquet'
- split: 2023_08_29T12_36_41.239310
path:
- '**/details_original|mmlu:virology|5_2023-08-29T12:36:41.239310.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-29T12:36:41.239310.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T14_52_20.646698
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T14:52:20.646698.parquet'
- split: 2023_08_28T20_31_38.653587
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:31:38.653587.parquet'
- split: 2023_08_29T12_36_41.239310
path:
- '**/details_original|mmlu:world_religions|5_2023-08-29T12:36:41.239310.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-29T12:36:41.239310.parquet'
- config_name: results
data_files:
- split: 2023_07_18T11_22_49.433588
path:
- results_2023-07-18T11:22:49.433588.parquet
- split: 2023_07_19T10_43_17.176281
path:
- results_2023-07-19T10:43:17.176281.parquet
- split: 2023_08_28T14_52_20.646698
path:
- results_2023-08-28T14:52:20.646698.parquet
- split: 2023_08_28T20_31_38.653587
path:
- results_2023-08-28T20:31:38.653587.parquet
- split: 2023_08_29T12_36_41.239310
path:
- results_2023-08-29T12:36:41.239310.parquet
- split: 2023_10_18T03_50_40.523576
path:
- results_2023-10-18T03-50-40.523576.parquet
- split: latest
path:
- results_2023-10-18T03-50-40.523576.parquet
---
# Dataset Card for Evaluation run of openlm-research/open_llama_3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openlm-research/open_llama_3b
- **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 [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) 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 6 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_openlm-research__open_llama_3b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T03:50:40.523576](https://huggingface.co/datasets/open-llm-leaderboard/details_openlm-research__open_llama_3b/blob/main/results_2023-10-18T03-50-40.523576.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": 0.0005243288590604027,
"em_stderr": 0.00023443780464835776,
"f1": 0.050632340604026965,
"f1_stderr": 0.001271781500579302,
"acc": 0.32587350322198844,
"acc_stderr": 0.00764164157289629
},
"harness|drop|3": {
"em": 0.0005243288590604027,
"em_stderr": 0.00023443780464835776,
"f1": 0.050632340604026965,
"f1_stderr": 0.001271781500579302
},
"harness|gsm8k|5": {
"acc": 0.004548900682335102,
"acc_stderr": 0.0018535550440036204
},
"harness|winogrande|5": {
"acc": 0.6471981057616417,
"acc_stderr": 0.01342972810178896
}
}
```
### 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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arxiv_dataset | null | 2023-10-26T10:45:45Z | 529 | 41 | [
"task_categories:translation",
"task_categories:summarization",
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:explanation-generation",
"task_ids:fact-checking-retrieval",
"task_ids:text-simplification",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"arxiv:1905.00075",
"region:us"
] | [
"translation",
"summarization",
"text-retrieval"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
- summarization
- text-retrieval
task_ids:
- document-retrieval
- entity-linking-retrieval
- explanation-generation
- fact-checking-retrieval
- text-simplification
paperswithcode_id: null
pretty_name: arXiv Dataset
dataset_info:
features:
- name: id
dtype: string
- name: submitter
dtype: string
- name: authors
dtype: string
- name: title
dtype: string
- name: comments
dtype: string
- name: journal-ref
dtype: string
- name: doi
dtype: string
- name: report-no
dtype: string
- name: categories
dtype: string
- name: license
dtype: string
- name: abstract
dtype: string
- name: update_date
dtype: string
splits:
- name: train
num_bytes: 3056873071
num_examples: 2349354
download_size: 0
dataset_size: 3056873071
---
# Dataset Card for arXiv Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Kaggle arXiv Dataset Homepage](https://www.kaggle.com/Cornell-University/arxiv)
- **Repository:**
- **Paper:** [On the Use of ArXiv as a Dataset](https://arxiv.org/abs/1905.00075)
- **Leaderboard:**
- **Point of Contact:** [Matt Bierbaum](mailto:[email protected])
### Dataset Summary
A dataset of 1.7 million arXiv articles for applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is English
## Dataset Structure
### Data Instances
This dataset is a mirror of the original ArXiv data. Because the full dataset is rather large (1.1TB and growing), this dataset provides only a metadata file in the json format. An example is given below
```
{'id': '0704.0002',
'submitter': 'Louis Theran',
'authors': 'Ileana Streinu and Louis Theran',
'title': 'Sparsity-certifying Graph Decompositions',
'comments': 'To appear in Graphs and Combinatorics',
'journal-ref': None,
'doi': None,
'report-no': None,
'categories': 'math.CO cs.CG',
'license': 'http://arxiv.org/licenses/nonexclusive-distrib/1.0/',
'abstract': ' We describe a new algorithm, the $(k,\\ell)$-pebble game with colors, and use\nit obtain a characterization of the family of $(k,\\ell)$-sparse graphs and\nalgorithmic solutions to a family of problems concerning tree decompositions of\ngraphs. Special instances of sparse graphs appear in rigidity theory and have\nreceived increased attention in recent years. In particular, our colored\npebbles generalize and strengthen the previous results of Lee and Streinu and\ngive a new proof of the Tutte-Nash-Williams characterization of arboricity. We\nalso present a new decomposition that certifies sparsity based on the\n$(k,\\ell)$-pebble game with colors. Our work also exposes connections between\npebble game algorithms and previous sparse graph algorithms by Gabow, Gabow and\nWestermann and Hendrickson.\n',
'update_date': '2008-12-13'}
```
### Data Fields
- `id`: ArXiv ID (can be used to access the paper)
- `submitter`: Who submitted the paper
- `authors`: Authors of the paper
- `title`: Title of the paper
- `comments`: Additional info, such as number of pages and figures
- `journal-ref`: Information about the journal the paper was published in
- `doi`: [Digital Object Identifier](https://www.doi.org)
- `report-no`: Report Number
- `abstract`: The abstract of the paper
- `categories`: Categories / tags in the ArXiv system
### Data Splits
The data was not splited.
## Dataset Creation
### Curation Rationale
For nearly 30 years, ArXiv has served the public and research communities by providing open access to scholarly articles, from the vast branches of physics to the many subdisciplines of computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics. This rich corpus of information offers significant, but sometimes overwhelming depth. In these times of unique global challenges, efficient extraction of insights from data is essential. To help make the arXiv more accessible, a free, open pipeline on Kaggle to the machine-readable arXiv dataset: a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more is presented to empower new use cases that can lead to the exploration of richer machine learning techniques that combine multi-modal features towards applications like trend analysis, paper recommender engines, category prediction, co-citation networks, knowledge graph construction and semantic search interfaces.
### Source Data
This data is based on arXiv papers.
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
This dataset contains no 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
The original data is maintained by [ArXiv](https://arxiv.org/)
### Licensing Information
The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
### Contributions
Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset. | [
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so_stacksample | null | 2022-11-03T16:30:57Z | 529 | 3 | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | [
"text2text-generation"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
paperswithcode_id: null
pretty_name: SO StackSample
dataset_info:
- config_name: Answers
features:
- name: Id
dtype: int32
- name: OwnerUserId
dtype: int32
- name: CreationDate
dtype: string
- name: ParentId
dtype: int32
- name: Score
dtype: int32
- name: Body
dtype: string
splits:
- name: Answers
num_bytes: 1583232304
num_examples: 2014516
download_size: 0
dataset_size: 1583232304
- config_name: Questions
features:
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dtype: int32
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dtype: int32
- name: CreationDate
dtype: string
- name: ClosedDate
dtype: string
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dtype: int32
- name: Title
dtype: string
- name: Body
dtype: string
splits:
- name: Questions
num_bytes: 1913896893
num_examples: 1264216
download_size: 0
dataset_size: 1913896893
- config_name: Tags
features:
- name: Id
dtype: int32
- name: Tag
dtype: string
splits:
- name: Tags
num_bytes: 58816824
num_examples: 3750994
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dataset_size: 58816824
---
# Dataset Card for SO StackSample
## 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://www.kaggle.com/stackoverflow/stacksample
### Dataset Summary
Dataset with the text of 10% of questions and answers from the Stack Overflow programming Q&A website.
This is organized as three tables:
Questions table contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions whose Id is a multiple of 10.
Answers table contains the body, creation date, score, and owner ID for each of the answers to these questions. The ParentId column links back to the Questions table.
Tags table contains the tags on each of these questions.
### Supported Tasks and Leaderboards
Example projects include:
- Identifying tags from question text
- Predicting whether questions will be upvoted, downvoted, or closed based on their text
- Predicting how long questions will take to answer
- Open Domain Q/A
### Languages
English (en) and Programming Languages.
## Dataset Structure
### Data Instances
For Answers:
```
{
"Id": { # Unique ID given to the Answer post
"feature_type": "Value",
"dtype": "int32"
},
"OwnerUserId": { # The UserID of the person who generated the Answer on StackOverflow. -1 means NA
"feature_type": "Value",
"dtype": "int32"
},
"CreationDate": { # The date the Answer was generated. Follows standard datetime format.
"feature_type": "Value",
"dtype": "string"
},
"ParentId": { # Refers to the `Id` of the Question the Answer belong to.
"feature_type": "Value",
"dtype": "int32"
},
"Score": { # The sum of up and down votes given to the Answer. Can be negative.
"feature_type": "Value",
"dtype": "int32"
},
"Body": { # The body content of the Answer.
"feature_type": "Value",
"dtype": "string"
}
}
```
For Questions:
```
{
"Id": { # Unique ID given to the Question post
"feature_type": "Value",
"dtype": "int32"
},
"OwnerUserId": { # The UserID of the person who generated the Question on StackOverflow. -1 means NA.
"feature_type": "Value",
"dtype": "int32"
},
"CreationDate": { # The date the Question was generated. Follows standard datetime format.
"feature_type": "Value",
"dtype": "string"
},
"ClosedDate": { # The date the Question was generated. Follows standard datetime format. Can be NA.
"feature_type": "Value",
"dtype": "string"
},
"Score": { # The sum of up and down votes given to the Question. Can be negative.
"feature_type": "Value",
"dtype": "int32"
},
"Title": { # The title of the Question.
"feature_type": "Value",
"dtype": "string"
},
"Body": { # The body content of the Question.
"feature_type": "Value",
"dtype": "string"
}
}
```
For Tags:
```
{
"Id": { # ID of the Question the tag belongs to
"feature_type": "Value",
"dtype": "int32"
},
"Tag": { # The tag name
"feature_type": "Value",
"dtype": "string"
}
}
```
`
### Data Fields
For Answers:
-`Id`: Unique ID given to the Answer post
`OwnerUserId`: The UserID of the person who generated the Answer on StackOverflow. -1 means NA
"`CreationDate`": The date the Answer was generated. Follows standard datetime format.
"`ParentId`": Refers to the `Id` of the Question the Answer belong to.
"`Score`": The sum of up and down votes given to the Answer. Can be negative.
"`Body`": The body content of the Answer.
For Questions:
- `Id`: Unique ID given to the Question post.
- `OwnerUserId`: The UserID of the person who generated the Question on StackOverflow. -1 means NA.
- `CreationDate`: The date the Question was generated. Follows standard datetime format.
- `ClosedDate`: The date the Question was generated. Follows standard datetime format. Can be NA.
- `Score`: The sum of up and down votes given to the Question. Can be negative.
- `Title`: {The title of the Question.
- `Body`: The body content of the Question.
For Tags:
- `Id`: ID of the Question the tag belongs to.
- `Tag`: The tag name.
### Data Splits
The dataset has 3 splits:
- `Answers`
- `Questions`
- `Tags`
## Dataset Creation
### Curation Rationale
Datasets of all R questions and all Python questions are also available on Kaggle, but this dataset is especially useful for analyses that span many languages.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
StackOverflow Users.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
This data contains information that can identify individual users of StackOverflow. The information is self-reported.
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
StackOverflow answers are not guaranteed to be safe, secure, or correct. Some answers may purposefully be insecure as is done in this https://stackoverflow.com/a/35571883/5768407 answer from user [`zys`](https://stackoverflow.com/users/5259310/zys), where they show a solution to purposefully bypass Google Play store security checks. Such answers can lead to biased models that use this data and can further propogate unsafe and insecure programming practices.
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
### Citation Information
The content is from Stack Overflow.
### Contributions
Thanks to [@ncoop57](https://github.com/ncoop57) for adding this dataset. | [
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AILab-CVC/SEED-Bench | AILab-CVC | 2023-11-30T07:25:26Z | 529 | 14 | [
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | [
"visual-question-answering"
] | 2023-07-28T08:12:52Z | ---
license: cc-by-nc-4.0
task_categories:
- visual-question-answering
language:
- en
pretty_name: SEED-Bench
size_categories:
- 10K<n<100K
---
# SEED-Bench Card
## Benchmark details
**Benchmark type:**
SEED-Bench is a large-scale benchmark to evaluate Multimodal Large Language Models (MLLMs).
It consists of 19K multiple choice questions with accurate human annotations, which
covers 12 evaluation dimensions including the comprehension of both the image and video modality.
**Benchmark date:**
SEED-Bench was collected in July 2023.
**Paper or resources for more information:**
https://github.com/AILab-CVC/SEED-Bench
**License:**
Attribution-NonCommercial 4.0 International. It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use.
For the images of SEED-Bench, we use the data from Conceptual Captions Dataset (https://ai.google.com/research/ConceptualCaptions/)
following its license (https://github.com/google-research-datasets/conceptual-captions/blob/master/LICENSE).
Tencent does not hold the copyright for these images and the copyright belongs to the original owner of Conceptual Captions Dataset.
For the videos of SEED-Bench, we use tha data from Something-Something v2 (https://developer.qualcomm.com/software/ai-datasets/something-something),
Epic-kitchen 100 (https://epic-kitchens.github.io/2023) and
Breakfast (https://serre-lab.clps.brown.edu/resource/breakfast-actions-dataset/). We only provide the video name. Please download them in their official websites.
**Where to send questions or comments about the benchmark:**
https://github.com/AILab-CVC/SEED-Bench/issues
## Intended use
**Primary intended uses:**
The primary use of SEED-Bench is evaluate Multimodal Large Language Models on spatial and temporal understanding.
**Primary intended users:**
The primary intended users of the Benchmark are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. | [
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result-kand2-sdxl-wuerst-karlo/a350d62a | result-kand2-sdxl-wuerst-karlo | 2023-09-15T11:08:21Z | 529 | 0 | [
"region:us"
] | null | 2023-09-15T11:08:20Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 179
num_examples: 10
download_size: 1365
dataset_size: 179
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a350d62a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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xsum_factuality | null | 2023-01-25T15:03:16Z | 528 | 4 | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-xsum",
"language:en",
"license:cc-by-4.0",
"hallucinations",
"region:us"
] | [
"summarization"
] | 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-xsum
task_categories:
- summarization
task_ids: []
pretty_name: XSum Hallucination Annotations
tags:
- hallucinations
dataset_info:
- config_name: xsum_factuality
features:
- name: bbcid
dtype: int32
- name: system
dtype: string
- name: summary
dtype: string
- name: is_factual
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: worker_id
dtype: string
splits:
- name: train
num_bytes: 800027
num_examples: 5597
download_size: 2864759
dataset_size: 800027
- config_name: xsum_faithfulness
features:
- name: bbcid
dtype: int32
- name: system
dtype: string
- name: summary
dtype: string
- name: hallucination_type
dtype:
class_label:
names:
'0': intrinsic
'1': extrinsic
- name: hallucinated_span_start
dtype: int32
- name: hallucinated_span_end
dtype: int32
- name: worker_id
dtype: string
splits:
- name: train
num_bytes: 1750325
num_examples: 11185
download_size: 2864759
dataset_size: 1750325
---
# Dataset Card for XSum Hallucination Annotations
## 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:** [XSUM Hallucination Annotations Homepage](https://research.google/tools/datasets/xsum-hallucination-annotations/)
- **Repository:** [XSUM Hallucination Annotations Homepage](https://github.com/google-research-datasets/xsum_hallucination_annotations)
- **Paper:** [ACL Web](https://www.aclweb.org/anthology/2020.acl-main.173.pdf)
- **Point of Contact:** [[email protected]](mailto:[email protected])
### Dataset Summary
Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. This dataset contains a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. The dataset has crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.
### Supported Tasks and Leaderboards
* `summarization`: : The dataset can be used to train a model for Summarization,, which consists in summarizing a given document. Success on this task is typically measured by achieving a *high/low* [ROUGE Score](https://huggingface.co/metrics/rouge).
### Languages
The text in the dataset is in English which are abstractive summaries for the [XSum dataset](https://www.aclweb.org/anthology/D18-1206.pdf). The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
##### Faithfulness annotations dataset
A typical data point consists of an ID referring to the news article(complete document), summary, and the hallucination span information.
An example from the XSum Faithfulness dataset looks as follows:
```
{
'bbcid': 34687720,
'hallucinated_span_end': 114,
'hallucinated_span_start': 1,
'hallucination_type': 1,
'summary': 'rory mcilroy will take a one-shot lead into the final round of the wgc-hsbc champions after carding a three-under',
'system': 'BERTS2S',
'worker_id': 'wid_0'
}
```
##### Factuality annotations dataset
A typical data point consists of an ID referring to the news article(complete document), summary, and whether the summary is factual or not.
An example from the XSum Factuality dataset looks as follows:
```
{
'bbcid': 29911712,
'is_factual': 0,
'summary': 'more than 50 pupils at a bristol academy have been sent home from school because of a lack of uniform.',
'system': 'BERTS2S',
'worker_id': 'wid_0'
}
```
### Data Fields
##### Faithfulness annotations dataset
Raters are shown the news article and the system summary, and are tasked with identifying and annotating the spans that aren't supported by the input article. The file contains the following columns:
- `bbcid`: Document id in the XSum corpus.
- `system`: Name of neural summarizer.
- `summary`: Summary generated by ‘system’.
- `hallucination_type`: Type of hallucination: intrinsic (0) or extrinsic (1)
- `hallucinated_span`: Hallucinated span in the ‘summary’.
- `hallucinated_span_start`: Index of the start of the hallucinated span.
- `hallucinated_span_end`: Index of the end of the hallucinated span.
- `worker_id`: Worker ID (one of 'wid_0', 'wid_1', 'wid_2')
The `hallucination_type` column has NULL value for some entries which have been replaced iwth `-1`.
##### Factuality annotations dataset
Raters are shown the news article and the hallucinated system summary, and are tasked with assessing the summary whether it is factual or not. The file contains the following columns:
- `bbcid1: Document id in the XSum corpus.
- `system`: Name of neural summarizer.
- `summary`: Summary generated by ‘system’.
- `is_factual`: Yes (1) or No (0)
- `worker_id`: Worker ID (one of 'wid_0', 'wid_1', 'wid_2')
The `is_factual` column has NULL value for some entries which have been replaced iwth `-1`.
### Data Splits
There is only a single split for both the Faithfulness annotations dataset and Factuality annotations dataset.
| | train |
|--------------------------|------:|
| Faithfulness annotations | 11185 |
| Factuality annotations | 5597 |
## 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 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```
@InProceedings{maynez_acl20,
author = "Joshua Maynez and Shashi Narayan and Bernd Bohnet and Ryan Thomas Mcdonald",
title = "On Faithfulness and Factuality in Abstractive Summarization",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
pages = "1906--1919",
address = "Online",
}
```
### Contributions
Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset. | [
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result-kand2-sdxl-wuerst-karlo/b5ddd948 | result-kand2-sdxl-wuerst-karlo | 2023-09-15T04:06:31Z | 528 | 0 | [
"region:us"
] | null | 2023-09-15T04:06:30Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 205
num_examples: 10
download_size: 1388
dataset_size: 205
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b5ddd948"
[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/323c0619 | result-kand2-sdxl-wuerst-karlo | 2023-09-15T06:43:16Z | 528 | 0 | [
"region:us"
] | null | 2023-09-15T06:43:16Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 236
num_examples: 10
download_size: 1424
dataset_size: 236
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "323c0619"
[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/f0cdf5c4 | result-kand2-sdxl-wuerst-karlo | 2023-09-15T09:18:20Z | 528 | 0 | [
"region:us"
] | null | 2023-09-15T09:18:19Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 207
num_examples: 10
download_size: 1427
dataset_size: 207
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f0cdf5c4"
[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/94daaaa5 | result-kand2-sdxl-wuerst-karlo | 2023-09-15T16:14:38Z | 528 | 0 | [
"region:us"
] | null | 2023-09-15T16:14:36Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1363
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "94daaaa5"
[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/1d35978a | result-kand2-sdxl-wuerst-karlo | 2023-09-16T01:30:03Z | 528 | 0 | [
"region:us"
] | null | 2023-09-16T01:30:02Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1301
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "1d35978a"
[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/a2d1bcf0 | result-kand2-sdxl-wuerst-karlo | 2023-09-16T15:16:31Z | 528 | 0 | [
"region:us"
] | null | 2023-09-16T15:16:30Z | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 220
num_examples: 10
download_size: 1379
dataset_size: 220
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a2d1bcf0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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