id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
luoruipu1/Valley-Instruct-73k | 2023-08-08T06:42:00.000Z | [
"license:apache-2.0",
"region:us"
] | luoruipu1 | null | null | null | 0 | 5 | ---
license: apache-2.0
---
We released the data for the second stage of valley training, a total of 73K, the data comes from the public video website ttps://www.jukinmedia.com/licensing, and the open source multimodal data sets VATEX and VIOLIN.It consists of four aspects, detailed description, complex reasoning, causal inference and conversation. The detailed description and complex reasoning come from jukinmedia, the conversation comes from VATEX, and the causal inference comes from VIOLIN. Because causal inference data is too difficult, the data of 73k version does not include causal inference data. We release all data include causal inference data to facilitate VLLM research of the community.
|
HarisivaRG/EPC_Postcode_grouped | 2023-07-25T17:22:35.000Z | [
"region:us"
] | HarisivaRG | null | null | null | 0 | 5 | Entry not found |
PeterBrendan/Ads_Creative_Ad_Copy_Programmatic | 2023-07-26T18:51:34.000Z | [
"license:mit",
"region:us"
] | PeterBrendan | null | null | null | 0 | 5 | ---
license: mit
---
### Dataset Summary
The Programmatic Ad Creatives dataset contains 7097 samples of online programmatic ad creatives along with their ad sizes. The dataset includes 8 unique ad sizes, such as (300, 250), (728, 90), (970, 250), (300, 600), (160, 600), (970, 90), (336, 280), and (320, 50). The dataset is in a tabular format and represents a random sample from Project300x250.com's complete creative data set. It is primarily used for training and evaluating natural language processing models in the context of advertising creatives.
### Supported Tasks
This dataset supports a range of tasks, including language modeling, text generation, and text augmentation. The full dataset has been utilized to fine-tune open-source models for creative ad copy. We hope this dataset will inspire contributors to join [Project 300x250](https://www.Project300x250.com) in creating open-source alternatives to Google and Meta, ensuring the existence of independent advertising.
### Languages
The dataset primarily consists of English language text.
### Dataset Structure
#### Data Fields
The dataset contains the following fields:
- 'text': Represents the text collected from the programmatic ad creative.
- 'dimensions': Represents the dimensions of the creative ad size.
#### Data Splits
The data is not split into separate subsets; it is provided as a whole.
## Dataset Creation
### Curation Rationale
The dataset of online programmatic ad creatives was curated to serve as a valuable resource for researchers and developers. It provides a unique collection of advertising creative text that is typically only available within walled gardens. The dataset aims to foster the development of independent advertising alternatives to Google and Meta, particularly in the field of AI, by promoting open-source solutions in the advertising domain.
### Source Data
The data is generated from a vast collection of programmatic creative images hosted by [Project 300x250](https://www.Project300x250.com)
. The text was extracted from each creative image.
## Dataset Use
### Use Cases
The dataset can be used for various tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation. Initially, the dataset has been utilized to fine-tune open-source models using programmatic ad text to generate unique ad copy. These models were created to inspire ad creatives and provide a starting point for developing effective marketing content.
### Usage Caveats
As this dataset is a sampled subset, it is recommended to regularly check for updates and improvements or reach out to the author for access to the full dataset.
|
v2ray/jannie-log | 2023-10-02T15:07:35.000Z | [
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"not-for-all-audiences",
"region:us"
] | v2ray | null | null | null | 9 | 5 | ---
license: mit
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
size_categories:
- 100K<n<1M
---
# Jannie Log
From moxxie proxy.
Not formatted version: [Click Me](https://drive.google.com/drive/folders/1HZtPe0j7PmNnaFcJtFfXxqFDUK_URqkf?usp=sharing) |
iamtarun/code_contest_processed | 2023-07-27T15:40:46.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"code",
"region:us"
] | iamtarun | null | null | null | 1 | 5 | ---
dataset_info:
features:
- name: id
dtype: string
- name: description
dtype: string
- name: code
dtype: string
- name: language
dtype:
class_label:
names:
'0': UNKNOWN
'1': Python2
'2': C++
'3': Python3
'4': JAVA
- name: test_samples
sequence:
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype:
class_label:
names:
'0': UNKNOWN_SOURCE
'1': CODECHEF
'2': CODEFORCES
'3': HACKEREARTH
'4': CODEJAM
'5': ATCODER
'6': AIZU
splits:
- name: train
num_bytes: 3321514817
num_examples: 38438
- name: valid
num_bytes: 122746000
num_examples: 396
- name: test
num_bytes: 77106001
num_examples: 514
download_size: 1047406436
dataset_size: 3521366818
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
task_categories:
- text-generation
- text2text-generation
- question-answering
tags:
- code
size_categories:
- 10K<n<100K
---
# Dataset Card for Code Contest Processed
## Dataset Summary
This dataset is created by processing [code_contest dataset from Deepmind](https://huggingface.co/datasets/deepmind/code_contests). It is a competitive programming dataset for machine-learning. Read more about dataset at [original source](https://huggingface.co/datasets/deepmind/code_contests).
## Columns Description
- `id` : unique string associated with a problem
- `description` : problem description
- `code` : one correct code for the problem
- `language` : programming language used for code
- `test_samples` : contains inputs and their corresponding outputs for the problem
- `source` : source of problem |
HydraLM/biology_dataset_standardized | 2023-07-27T17:14:13.000Z | [
"region:us"
] | HydraLM | null | null | null | 0 | 5 | Entry not found |
HydraLM/physics_dataset_standardized | 2023-07-27T17:17:05.000Z | [
"region:us"
] | HydraLM | null | null | null | 1 | 5 | Entry not found |
ChanceFocus/flare-edtsum | 2023-07-28T05:23:42.000Z | [
"region:us"
] | ChanceFocus | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 20411227
num_examples: 2000
download_size: 10794666
dataset_size: 20411227
---
# Dataset Card for "flare-edtsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ammarnasr/the-stack-ruby-clean | 2023-08-14T21:20:54.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:code",
"license:openrail",
"code",
"region:us"
] | ammarnasr | null | null | null | 1 | 5 | ---
license: openrail
dataset_info:
features:
- name: hexsha
dtype: string
- name: size
dtype: int64
- name: content
dtype: string
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: alphanum_fraction
dtype: float64
splits:
- name: train
num_bytes: 3582248477.9086223
num_examples: 806789
- name: test
num_bytes: 394048264.9973618
num_examples: 88747
- name: valid
num_bytes: 3982797.09401595
num_examples: 897
download_size: 1323156008
dataset_size: 3980279540
task_categories:
- text-generation
language:
- code
tags:
- code
pretty_name: TheStack-Ruby
size_categories:
- 1M<n<10M
---
## Dataset 1: TheStack - Ruby - Cleaned
**Description**: This dataset is drawn from TheStack Corpus, an open-source code dataset with over 3TB of GitHub data covering 48 programming languages. We selected a small portion of this dataset to optimize smaller language models for Ruby, a popular statically typed language.
**Target Language**: Ruby
**Dataset Size**:
- Training: 900,000 files
- Validation: 50,000 files
- Test: 50,000 files
**Preprocessing**:
1. Selected Ruby as the target language due to its popularity on GitHub.
2. Filtered out files with average line length > 100 characters, maximum line length > 1000 characters, and alphabet ratio < 25%.
3. Split files into 90% training, 5% validation, and 5% test sets.
**Tokenizer**: Byte Pair Encoding (BPE) tokenizer with tab and whitespace tokens. GPT-2 vocabulary extended with special tokens.
**Training Sequences**: Sequences constructed by joining training data text to reach a context length of 2048 tokens (1024 tokens for full fine-tuning). |
dtthanh/llama_test | 2023-08-06T08:47:42.000Z | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:vi",
"license:llama2",
"region:us"
] | dtthanh | null | null | null | 0 | 5 | ---
language:
- vi
license: llama2
size_categories:
- n<1K
task_categories:
- question-answering
pretty_name: Uncle Dao Demo
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 203506
num_examples: 449
download_size: 0
dataset_size: 203506
---
|
qgyd2021/nxlink_question_answer | 2023-09-20T03:03:59.000Z | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"finance",
"region:us"
] | qgyd2021 | null | @dataset{nxlink_question_answer,
author = {Xing Tian},
title = {nxlink_question_answer},
month = sep,
year = 2023,
publisher = {Xing Tian},
version = {1.0},
} | null | 0 | 5 | ---
task_categories:
- question-answering
- text2text-generation
language:
- zh
tags:
- finance
size_categories:
- 100K<n<1M
---
## NXLink Document QA
Document QA 由两部分组成:
(1)文档检索.
(2)文档问答.
LLM 大模型给我们提供的是上下文理解和生成答案的能力, 即解决 "文档问答" 的问题.
所以我们要做的 Document QA 主要工作在 "文档检索" 上面.
这是一个信息检索的问题, 类似于搜索引擎, 是一个专门的任务.
### 信息检索介绍
信息检索大致分为两类: 基于词汇和基于语义向量.
**基于词汇**的方法有 TF-IDF, BM25 算法.
Elasticsearch 就是这种原理.
其中涉及同义词, 停用词等预处理过程.
**优点**在于可解释性强, 并且准确率并不差.
**缺点**是: 不包含语义信息. 例如以下示例就不能正确的匹配:
```text
doc: 北京风景很好,
doc: 云南风景很好,
query: 适合旅游的城市,
```
**基于语义向量**的方法, 即基于神经网络的文本向量化 embedding.
向量数据库就是对向量查询过程进行优化, 而匹配结果的好坏取决于 embedding 向量化本身.
**优点**是它从大规模语料训练, 向量匹配不依赖字面意思, 不需要制作同义词表, 停用词表等.
**缺点**是它的可解释性差, 匹配错误时难以修正. 并且query是疑问句, 而document是陈述句, 两者之间有本质上的不同, 会造成匹配上的偏差.
#### 信息检索应用介绍
以上两类基于词汇和基于语义向量的方法是两种基本方法, 而完善的信息检索系统(应用)往往是这些方法的组合. 例如:
电商问答:
```text
(1)从用户query提取原信息, 如: 主体(例如: 产品名称, 服务名称). 场景(例如: 退换货, 维修等).
(2)根据"主体", "场景" 缩小信息检索的范围. 例如: 检索到该产品的退换货政策段落.
(3)根据产品退换货政策介绍对用户query进行回答.
以上3步,
(1)第一步, 采用分类模型, 实体识别.
(2)第二步, 采用Elasticsearch 搜索.
(3)第三步, 采用 MRC 机器阅读理解.
```
新闻推荐:
```text
(1)收集用户信息, 总结用户偏好. 例如其对各类新闻的感兴趣程度.
(2)根据用户对各类新闻的感兴趣程度, 再根据用户query从各领域有权重地检索过滤.
总体思路就是缩小范围再检索. 不仅可以提高准确率, 还可以降低计算量.
```
### NXLink Document QA 介绍
基于对以上信息的理解之后, 我们根据 [NXLink](https://help.nxcloud.com/nxlink/docs/new-user) 的文档制作了基于 LLM 的问答系统. 其实现流程是:
1. 根据document 生成 question-answer 问答对.
2. 将问答对写入 Elasticsearch.
3. 用户 query 在 ES 中检索出 5 个最相似的问答对.
4. LLM 根据这 5 个候选问答对为用户 query 生成答案 answer.
主要面临的问题是:
**问题1:**
```text
query是疑问句, document是陈述句, 并且query较短, document较长, 两者本质不同, 所以向量匹配时必定不能达到最好的效果.
```
解决办法:
```text
(1)利用 LLM, 给定document, 让大模型对其提问 question, 并给出相应的答案 answer.
(2)检索时, 根据用户query与question 匹配, 可以采用 Elasticsearch 也可以采用 embedding 向量.
(3)根据检索结果直接返回答案, 将检索到的问答对作为上下文, 让LLM生成query的答案.
```
缺点:
```text
给大量的 document 生成 question, answer, 调用量特别大, 很花钱.
```
**问题2:**
```text
document 生成 question-answer, 再根据 query 生成 answer 时可能产品歧义. 如下:
```
文档段落
```text
https://help.nxcloud.com/nxlink/docs/jaWHjt
#### 第 2 步：创建目标 WABA
**该步骤由客户操作，NXCLOUD协助**
可以通过NXCLOUD的嵌入式注册创建完成，嵌入式注册方式有两种，需根据客户需求选择
1）NXLink注册方式：[嵌入式注册&认证指南(NXLink)](https://help.nxcloud.com/nxlink/docs/Mz2rZR)
2）API注册方式：[嵌入式注册&认证指南(API)](https://help.nxcloud.com/nxlink/docs/x3b8oL)
```
生成 question-answer
```text
question: 什么是NXLink和API注册方式？
answer: NXLink和API注册方式是NXCLOUD提供的嵌入式注册方式，用来创建目标WABA账号。NXLink注册方式是通过安装NXLink插件的方式完成嵌入式注册，而API注册方式则是使用API进行嵌入式注册。
```
为 query 生成 answer
```text
query: NXLink是做什么的.
answer: NXLink是NXCLOUD提供的嵌入式注册方式，用来创建目标WABA账号。NXLink注册方式是通过安装NXLink插件的方式完成嵌入式注册。
```
其中: "NXLink" 和 "NXLink注册方式" 最终被混淆.
解决办法:
```text
问题产生是 LLM 过程的固有问题.
优化的方法是向数据库中写入更多与 query (NXLink是做什么的) 更接近的候选数据.
即需要补充真正能够回答该 query的问答对.
```
缺点:
```text
此解决办法也暴露了一个问题, 即:
大语言模型 LLM 并不能全自动地解决 Document QA 的问题, 其中仍然需要很多人工参与, 人工校验的部分.
```
**问题3:**
```text
大模型生成 question-answer 问答对时, 只是针对当前段落提问, 而无法考虑到全局中其它文档的内容.
例如, 电商场景中, 每个产品都有自己的售后政策, 但大模型生成的问题只会是 "产品维修是免费的吗?". 其中缺少了对应哪个产品的信息, 这很可能导致匹配错误, 并且 LLM 还回答得很有信心, 最终误导消费者.
```
解决办法:
```text
针对电商场景来说, 文档检索之前必须确定产品, 用户意图等信息, 以确定检索范围.
```
缺点:
```text
我们似乎很难制作完全通用的系统, 要提高准确率, 还需要特定问题特定分析.
``` |
CyberHarem/type97_girlsfrontline | 2023-09-17T16:37:47.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | CyberHarem | null | null | null | 0 | 5 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of type97_girlsfrontline
This is the dataset of type97_girlsfrontline, containing 173 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 173 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 487 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 173 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 173 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 173 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 173 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 173 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 487 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 487 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 487 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
TableQAKit/SQA | 2023-08-03T13:11:25.000Z | [
"region:us"
] | TableQAKit | null | null | null | 0 | 5 | Entry not found |
arazd/tulu_dolly | 2023-08-04T21:39:57.000Z | [
"license:openrail",
"region:us"
] | arazd | null | null | null | 0 | 5 | ---
license: openrail
---
|
arazd/tulu_stanford_alpaca | 2023-08-04T21:40:54.000Z | [
"license:openrail",
"region:us"
] | arazd | null | null | null | 0 | 5 | ---
license: openrail
---
|
arazd/tulu_super_ni | 2023-08-04T21:43:00.000Z | [
"license:openrail",
"region:us"
] | arazd | null | null | null | 0 | 5 | ---
license: openrail
---
|
arazd/tulu_flan | 2023-08-04T21:43:58.000Z | [
"license:openrail",
"region:us"
] | arazd | null | null | null | 0 | 5 | ---
license: openrail
---
|
arazd/tulu_code_alpaca | 2023-08-04T21:45:10.000Z | [
"license:openrail",
"region:us"
] | arazd | null | null | null | 0 | 5 | ---
license: openrail
---
|
arazd/tulu_gpt4_alpaca | 2023-08-04T21:47:39.000Z | [
"license:openrail",
"region:us"
] | arazd | null | null | null | 0 | 5 | ---
license: openrail
---
|
arazd/tulu_oasst1 | 2023-08-04T21:49:41.000Z | [
"license:openrail",
"region:us"
] | arazd | null | null | null | 0 | 5 | ---
license: openrail
---
|
KnutJaegersberg/newsroom-human-eval | 2023-08-05T14:44:47.000Z | [
"license:mit",
"region:us"
] | KnutJaegersberg | null | null | null | 0 | 5 | ---
license: mit
---
From https://github.com/whl97/LS-Score |
FanChen0116/19100_chat_40x_slot_empty | 2023-08-08T07:24:07.000Z | [
"region:us"
] | FanChen0116 | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': I-time
'2': B-date
'3': B-last_name
'4': B-people
'5': I-date
'6': I-people
'7': I-last_name
'8': I-first_name
'9': B-first_name
'10': B-time
- name: request_slot
sequence: string
splits:
- name: train
num_bytes: 422433
num_examples: 2560
- name: validation
num_bytes: 4861
num_examples: 32
- name: test
num_bytes: 646729
num_examples: 3731
download_size: 0
dataset_size: 1074023
---
# Dataset Card for "19100_chat_40x_slot_empty"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Guilleber/papyrus | 2023-08-07T18:30:26.000Z | [
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:en",
"language:fr",
"license:apache-2.0",
"text-to-text",
"keyphrase-generation",
"region:us"
] | Guilleber | The datasets are derived from Papyrus, a repository at Université de Montréal containing various types of documents, mainly theses with abstracts in multiple languages, primarily French and English. The entries are provided in four different configurations based on the languages of abstracts, allowing for generating keyphrases in French, English, or multiple languages.
- Papyrus-f: From the French abstracts, generate French keyphrases.
- Papyrus-e: From the English abstracts, generate English keyphrases.
- Papyrus-m: From one abstract in any language, generate keyphrases in that same
language (one language to one language).
- Papyrus-a: From the multiple abstracts of a document, generate keyphrases in the
same languages as the abstracts (many to many languages). | @inproceedings{NEURIPS2022_f8870955,
author = {Piedboeuf, Fr\'{e}d\'{e}ric and Langlais, Philippe},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
pages = {38046--38059},
publisher = {Curran Associates, Inc.},
title = {A new dataset for multilingual keyphrase generation},
url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/f88709551258331f9ab31b33c71021a4-Paper-Datasets_and_Benchmarks.pdf},
volume = {35},
year = {2022}
} | null | 0 | 5 | ---
license: apache-2.0
pretty_name: Papyrus
size_categories:
- 10K<n<100K
language:
- en
- fr
multilinguality: multilingual
tags:
- text-to-text
- keyphrase-generation
---
# Dataset Card for Papyrus
- **Paper:** [A new dataset for multilingual keyphrase generation](https://proceedings.neurips.cc/paper_files/paper/2022/hash/f88709551258331f9ab31b33c71021a4-Abstract-Datasets_and_Benchmarks.html)
- **Github:** <https://github.com/smolPixel/French-keyphrase-generation>
## Dataset Description
### Dataset Summary
The datasets are derived from Papyrus, a repository at Université de Montréal containing various types of documents, mainly theses with abstracts in multiple languages, primarily French and English. The entries are provided in four different configurations based on the languages of abstracts, allowing for generating keyphrases in French, English, or multiple languages.
- **Papyrus-f:** From the French abstracts, generate French keyphrases.
- **Papyrus-e:** From the English abstracts, generate English keyphrases.
- **Papyrus-m:** From one abstract in any language, generate keyphrases in that same
language (one language to one language).
- **Papyrus-a:** From the multiple abstracts of a document, generate keyphrases in the
same languages as the abstracts (many to many languages).
### Languages
- **Main languages:** English, French
- **Others:** Spanish, German, Italian, Portuguese, Arabic, Tagalog, Catalan, Greek, Turkish, Russian, Polish, Farsi, Indonesian, Lingala, Swedish, Finnish, Romanian, Korean
## Dataset Structure
### Dataset content
| Config | Train set size | Valid. set size | Test set size |
| --------- | -------------- | --------------- | ------------- |
| papyrus-m | 20963 | 3040 | 6061 |
| papyrus-e | 10508 | 1539 | 3046 |
| papyrus-f | 10299 | 1488 | 2981 |
| papyrus-a | 11290 | 1638 | 3261 |
### Data fields
- **doc_id:** a unique id for the original document.
- **title:** title of the thesis or article (the language of the title does not always match the language of the abstract/keyphrases).
- **input_text:** abstract of the document.
- **keyphrases:** associated keyphrases.
- **lang:** language of the abstract/keyphrases.
## Citation
@inproceedings{NEURIPS2022_f8870955,
author = {Piedboeuf, Fr\'{e}d\'{e}ric and Langlais, Philippe},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
pages = {38046--38059},
publisher = {Curran Associates, Inc.},
title = {A new dataset for multilingual keyphrase generation},
url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/f88709551258331f9ab31b33c71021a4-Paper-Datasets_and_Benchmarks.pdf},
volume = {35},
year = {2022}
} |
dipsmav/java_common_errors | 2023-08-08T05:56:10.000Z | [
"license:apache-2.0",
"region:us"
] | dipsmav | null | null | null | 0 | 5 | ---
license: apache-2.0
---
|
alancooney/relation_counterfact | 2023-08-09T15:57:24.000Z | [
"license:mit",
"region:us"
] | alancooney | null | null | null | 1 | 5 | ---
license: mit
---
# Dataset Card for Relation Counterfact
## Dataset Description
- **Repository:** https://github.com/bilal-chughtai/attention-head-superposition
- **Paper:** TBC
- **Point of Contact:** Alan Cooney
### Dataset Summary
This dataset is based on the [Rome Counterfact dataset](https://rome.baulab.info/).
It is adjusted so that all re-written prompts are in the same order and form of
[Subject] [Relationship] -> [Attribute] (e.g. Beats Music is owned by -> Apple).
The dataset also uses additional validation rules to remove examples where the
correct attribute is ambiguous, as well as other unsuitable dataset examples
(e.g. where the attribute is included in the subject such as "Porsche 911 is
created by -> Porsche"). Finally a list of `subject_relevant_words` is included
to help study the residual stream at different points.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## 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] |
TrainingDataPro/parking-space-detection-dataset | 2023-09-14T16:27:57.000Z | [
"task_categories:image-classification",
"task_categories:image-to-image",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"region:us"
] | TrainingDataPro | The dataset consists of images of parking spaces along with corresponding bounding box
masks. In order to facilitate object detection and localization, every parking space in
the images is annotated with a bounding box mask.
The bounding box mask outlines the boundary of the parking space, marking its position
and shape within the image. This allows for accurate identification and extraction of
individual parking spaces. Each parking spot is also labeled in accordance to its
occupancy: free, not free or partially free.
This dataset can be leveraged for a range of applications such as parking lot
management, autonomous vehicle navigation, smart city implementations, and traffic
analysis. | @InProceedings{huggingface:dataset,
title = {parking-space-detection-dataset},
author = {TrainingDataPro},
year = {2023}
} | null | 2 | 5 | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
tags:
- code
dataset_info:
features:
- name: id
dtype: int32
- name: image
dtype: image
- name: mask
dtype: image
- name: bboxes
dtype: string
splits:
- name: train
num_bytes: 44610347
num_examples: 30
download_size: 44532683
dataset_size: 44610347
---
# Parking Space Detection & Classification Dataset
The dataset consists of images of parking spaces along with corresponding bounding box masks. In order to facilitate object detection and localization, every parking space in the images is annotated with a bounding box mask.
The bounding box mask outlines the boundary of the parking space, marking its position and shape within the image. This allows for accurate identification and extraction of individual parking spaces. Each parking spot is also labeled in accordance to its occupancy: **free, not free or partially free**.
This dataset can be leveraged for a range of applications such as *parking lot management, autonomous vehicle navigation, smart city implementations, and traffic analysis*.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=parking-space-detection-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of parkings
- **boxes** - includes bounding box labeling for the original images
- **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.
### Labels for the parking space:
- **free_parking_space** - corresponds to free parking spaces, the box is **blue**
- **not_free_parking_space** - corresponds to occupied parking spaces, the box is **red**
- **partially_free_parking_space** - corresponds to partially free parking spaces, the box is **yellow**
# Example of XML file structure
# Parking Space Detection & Classification might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=parking-space-detection-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
Andriusem/langchaindocs | 2023-08-08T22:36:49.000Z | [
"region:us"
] | Andriusem | null | null | null | 0 | 5 | Entry not found |
EgilKarlsen/Liberty_100K | 2023-08-12T22:04:42.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: log
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 11547643
num_examples: 90000
- name: validation
num_bytes: 1279280
num_examples: 10000
download_size: 4032752
dataset_size: 12826923
---
# Dataset Card for "Liberty_100K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MedHALT/Med-HALT | 2023-08-10T15:27:31.000Z | [
"license:apache-2.0",
"arxiv:2307.15343",
"region:us"
] | MedHALT | null | null | null | 4 | 5 | ---
license: apache-2.0
configs:
- config_name: IR_abstract2pubmedlink
data_files: "IR_abstract2pubmedlink/IR_abstract2pubmedlink.csv"
- config_name: IR_pubmedlink2title
data_files: "IR_pubmedlink2title/IR_pubmedlink2title.csv"
- config_name: IR_pmid2title
data_files: "IR_pmid2title/IR_pmid2title.csv"
- config_name: IR_title2pubmedlink
data_files: "IR_title2pubmedlink/IR_title2pubmedlink.csv"
- config_name: reasoning_fake
data_files: "reasoning_fake/reasoning_fake.csv"
- config_name: reasoning_nota
data_files: "reasoning_nota/reasoning_nota.csv"
- config_name: reasoning_FCT
data_files: "reasoning_FCT/reasoning_FCT.csv"
---
# Med-HALT: Medical Domain Hallucination Test for Large Language Models
This is a dataset used in the [Med-HALT](https://arxiv.org/abs/2307.15343) research paper. This research paper focuses on the challenges posed by hallucinations in large language models (LLMs), particularly in the context of the medical domain. We propose a new benchmark and dataset, Med-HALT (Medical Domain Hallucination Test), designed specifically to evaluate hallucinations.
Med-HALT provides a diverse multinational dataset derived from medical examinations across various countries and includes multiple innovative testing modalities. Med-HALT includes two categories of tests reasoning and memory-based hallucination tests, designed to assess LLMs' problem-solving and information retrieval abilities. Our study evaluated leading LLMs, including Text Davinci, GPT-3.5, LlaMa and Falcon, revealing significant differences in their performance. The paper provides detailed insights into the dataset, promoting transparency and reproducibility. Through this work, we aim to contribute to the development of safer and more reliable language models in healthcare. Our benchmark can be found at https://github.com/medhalt/medhalt
## Benchmark
The Med-HALT framework proposes a two-tiered approach to evaluate the presence and impact of hallucinations in generated outputs.
#### Reasoning Hallucination Tests (RHTs)
<details>
<summary>False Confidence Test (FCT)</summary>
The False Confidence Test (FCT) involves presenting a multiple-choice medical question and a randomly suggested correct answer to the language model, tasking it with evaluating the validity of the proposed answer and providing detailed explanations for its correctness or incorrectness, in addition to explaining why the other options are wrong.
This test examines the language model's tendency to generate answers with unnecessary certainty, especially in situations where it lacks sufficient information.
</details>
<details>
<summary>None of the Above Test (Nota)</summary>
In the None of the Above (Nota) Test, the model is presented with a multiple-choice medical question where the correct answer is replaced by 'None of the above', requiring the model to identify this and justify its selection.
It tests the model's ability to distinguish irrelevant or incorrect information.
</details>
<details>
<summary>Fake Questions Test (FQT)</summary>
This test involves presenting the model with fake or nonsensical medical questions to examine whether it can correctly identify and handle such queries.
We employed a hybrid approach for generating fake questions, where a subset was crafted by human experts, while the remaining were generated using GPT-3.5.
</details>
#### Memory Hallucination Tests (MHTs)
<details>
<summary>Abstract-to-Link Test</summary>
Given the abstract of a PubMed article, the LLM is asked to generate the corresponding link to the article. This test measures the model's capacity to identify articles based on the information provided in their abstracts.
</details>
<details>
<summary>PMID-to-Title Test</summary>
In this test, the LLM is given the PubMed ID (PMID) of an article and is asked to generate the title of the article. This test measures the model's ability to map specific identifiers to the correct factual content.
</details>
<details>
<summary>Title-to-Link Test</summary>
Given the title of a PubMed article, the LLM is prompted to provide the PubMed link of the article. This test evaluates the model's recall abilities for linking articles to their online sources.
</details>
<details>
<summary>Link-to-Title Test</summary>
Similar to the previous one, in this test, we give the PubMed link of an article as input and ask the language model to provide the title as output. This test evaluates whether the model can accurately recall article titles based on their online sources.
</details>
## Citation
```
@article{Medhalt,
title={Med-HALT: Medical Domain Hallucination Test for Large Language Models},
author={Umapathi, Logesh Kumar and Pal, Ankit and Sankarasubbu, Malaikannan},
journal={arXiv preprint},
year={2023}
}
``` |
Shdorsh/luxembourgish-lu | 2023-08-10T20:22:58.000Z | [
"license:openrail",
"region:us"
] | Shdorsh | null | null | null | 0 | 5 | ---
license: openrail
pretty_name: Luxembourgish Language Dataset
--- |
niting3c/Malicious_packets | 2023-08-12T19:52:02.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"license:mit",
"region:us"
] | niting3c | null | null | null | 0 | 5 | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': attack
'1': normal
splits:
- name: train
num_bytes: 2168348027
num_examples: 1256906
- name: test
num_bytes: 1694780877
num_examples: 894651
download_size: 1608793256
dataset_size: 3863128904
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- text-classification
- zero-shot-classification
- text-generation
pretty_name: Normal & Malicious Packet Data Set
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
### Dataset Summary
This is a dataset of collection of malicious and normal packet payloads.
They have been categorized into Attack and Normal.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Hex and Ascii payloads
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
Split is Between Train and Test.
## Dataset Creation
### Source Data
Data Source: https://www.unb.ca/cic/datasets/ids-2017.html
#### 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
https://huggingface.co/rdpahalavan
### Contributions
https://huggingface.co/rdpahalavan |
augtoma/usmle_step_3 | 2023-08-11T21:25:10.000Z | [
"region:us"
] | augtoma | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: E
dtype: string
- name: F
dtype: string
- name: G
dtype: string
- name: answer
dtype: string
- name: answer_idx
dtype: string
splits:
- name: test
num_bytes: 156286
num_examples: 122
download_size: 98164
dataset_size: 156286
---
# Dataset Card for "usmle_self_eval_step3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tomdavies32/kylietest140823 | 2023-08-14T14:08:23.000Z | [
"region:us"
] | tomdavies32 | null | null | null | 0 | 5 | Entry not found |
jonathansuru/customer_service_intent_detection | 2023-08-14T22:56:37.000Z | [
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"region:us"
] | jonathansuru | null | null | null | 0 | 5 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
--- |
ecnu-aigc/EMID | 2023-08-17T05:24:53.000Z | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-sa-4.0",
"music",
"emotion",
"image",
"cross_modal",
"arxiv:1605.02677",
"arxiv:2308.07622",
"region:us"
] | ecnu-aigc | null | null | null | 1 | 5 | ---
language:
- en
license: cc-by-nc-sa-4.0
size_categories:
- 10K<n<100K
tags:
- music
- emotion
- image
- cross_modal
dataset_info:
features:
- name: Audio_Filename
dtype:
audio:
mono: false
- name: genre
dtype: string
- name: feeling
dtype: string
- name: emotion
dtype: string
- name: Image1_filename
dtype: image
- name: Image1_tag
dtype: string
- name: Image1_text
dtype: string
- name: Image2_filename
dtype: image
- name: Image2_tag
dtype: string
- name: Image2_text
dtype: string
- name: Image3_filename
dtype: image
- name: Image3_tag
dtype: string
- name: Image3_text
dtype: string
- name: is_original_clip
dtype: bool
splits:
- name: train
num_bytes: 3966276607.886
num_examples: 10738
download_size: 372817257
dataset_size: 3966276607.886
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
## Dataset Summary
**E**motionally paired **M**usic and **I**mage Dataset (**EMID**) is a novel dataset designed for the emotional matching of music and images. The EMID dataset contains 10,738 unique music clips, each of which is paired with 3 images in the same emotional category，as well as rich annotations. These musical clips are categorized into the 13 emotional categories proposed by [What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures](https://pnas.org/doi/full/10.1073/pnas.1910704117), from which we obtain 1,836 original music clips. Subsequently, we acquire images labeled with Mikels' eight emotions from [Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark](http://arxiv.org/abs/1605.02677). Through the processing pipeline proposed in our paper, we expanded the original music clips and obtain the final dataset.
Please cite our paper when using the EMID dataset: https://arxiv.org/abs/2308.07622
### Processing Pipeline
### Statistics of EMID
| Emotion | A | B | C | D | E | F | G | H | I | J | K | L | M | Total |
| ---------------- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :---: |
| Before expanding | 45 | 80 | 54 | 131 | 306 | 174 | 367 | 86 | 36 | 124 | 129 | 105 | 199 | 1836 |
| After expanding | 255 | 545 | 320 | 771 | 1531 | 889 | 1832 | 1036 | 323 | 1014 | 799 | 484 | 939 | 10738 |
## Dataset Download
The published dataset takes the form of a `.csv` file that contains the filenames of music clips and corresponding emotional matched images. In order to use this dataset, your should download the csv file as well as the music/image data.
This dataset licensed under a CC BY-NC-SA 4.0 lisence, which provides free access to the data for non-profit work. We are by no means can guaranty support for its users. This database comes as is with no guaranty of correctness and we are not liable to any damage it might cause.
## Data Fields
| filed_name | explanation | example |
| ---------------- | :----------------------------------------------------------- | :----------------------------------------------------------- |
| Audio_Filename | unique Filename of the music clip | 106.m4a___172.mp3 |
| genre | Letter A to M, representing one of the 13 emotional category | K |
| feeling | Feelings reported by participants after listening to this music clip and their proportions | "33% Sad, depressing, 22% Awe-inspiring, amazing, 22% Proud, strong, 22% Triumphant, heroic, 19% Dreamy, 15% Beautiful, 15% Bittersweet, 11% Calm, relaxing, serene, 11% Compassionate, sympathetic, 11% Entrancing, 11% Transcendent, mystical, 7% Eerie, mysterious, 7% Painful, 7% Tender, longing, 4% Energizing, pump-up, 4% Indignant, defiant" |
| emotion | Ratings of subjective experiences elicited by the music on 11 emotional dimensions, ranging from 1 to 9 | "5,5.3,5,6,3.1,6.1,5.1,3.6,6.2,5.9,5.6" |
| Image1_filename | - | excitement_0616.jpg |
| Image1_tag | emotional category of image 1 | excitement |
| Image1_text | The textual description of Image 1 generated by the GIT model | ['the marching band in the parade'] |
| Image2_filename | - | amusement_2906.jpg |
| Image2_tag | emotional category of image 2 | amusement |
| Image2_text | The textual description of Image 2 generated by the GIT model | ['the marching band at disneyland'] |
| Image3_filename | - | amusement_2226.jpg |
| Image3_tag | emotional category of image 3 | amusement |
| Image3_text | The textual description of Image 3 generated by the GIT model | ['a marching band in a parade with people watching.'] |
| is_original_clip | If this value is true, the music clip is from the original music dataset, otherwise it is expanded from the original music clip through our processing pipeline. The original music clips are considered to provide a better emotional matching performance | False |
For more information about feeling and emotion filed, you can refer to [What music makes us feel: At least 13 dimensions organize subjective experiences associated with music across different cultures](https://pnas.org/doi/full/10.1073/pnas.1910704117) and their visualized interactive [site](https://www.ocf.berkeley.edu/~acowen/music.html#). |
airesai/stable_diffusion_instructional_dataset | 2023-08-15T22:11:10.000Z | [
"task_categories:text2text-generation",
"task_categories:question-answering",
"language:en",
"stable diffussion",
"llama",
"llama2",
"chatgpt",
"prompt",
"llm",
"dataset",
"finetune",
"train",
"qlora",
"lora",
"region:us"
] | airesai | null | null | null | 1 | 5 | ---
task_categories:
- text2text-generation
- question-answering
language:
- en
tags:
- stable diffussion
- llama
- llama2
- chatgpt
- prompt
- llm
- dataset
- finetune
- train
- qlora
- lora
pretty_name: Stable Difussion Instruct Dataset - AiresAI
---
# Stable Diffusion Dataset
# Description:
This dataset is in Jsonl format and is based on the MadVoyager/stable_diffusion_instructional_dataset.
# Overview:
The Stable Diffusion Dataset comprises approximately 80,000 meticulously curated prompts sourced from the image finder of Stable Diffusion: "Lexica.art". The dataset is intended to facilitate training and fine-tuning of various language models, including LLaMa2.
# Key Features:
◉ Jsonl format for seamless integration with existing projects.
◉ High-quality prompts extracted from the Stable Diffusion image finder.
◉ Ideal for enhancing models like LLaMa2 through training and fine-tuning.
◉ Usage:
◉ Researchers and developers can utilize this dataset to:
Train and fine-tune language models like LLaMa2.
Conduct experiments in natural language processing and generation.
Enhance and expand AI capabilities in creative and interactive applications.
# Acknowledgments:
We acknowledge the creators and contributors of the MadVoyager/stable_diffusion_instructional_dataset for providing the foundation for this dataset. |
TinyPixel/oasst1-m | 2023-09-14T04:17:05.000Z | [
"region:us"
] | TinyPixel | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 9384110
num_examples: 8274
download_size: 5119052
dataset_size: 9384110
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oasst1-m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
crewdon/FormulasMax500 | 2023-08-16T14:01:38.000Z | [
"region:us"
] | crewdon | null | null | null | 0 | 5 | ---
dataset_info:
config_name: crewdon
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 35055132
num_examples: 154634
download_size: 6463417
dataset_size: 35055132
configs:
- config_name: crewdon
data_files:
- split: train
path: crewdon/train-*
---
# Dataset Card for "FormulasMax500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PulsarAI/Turkish-Alpaca | 2023-08-16T21:09:50.000Z | [
"license:mit",
"region:us"
] | PulsarAI | null | null | null | 2 | 5 | ---
license: mit
---
|
jason137/fomc_sample | 2023-08-16T22:17:24.000Z | [
"region:us"
] | jason137 | null | null | null | 0 | 5 | Entry not found |
TaatiTeam/OCW | 2023-08-20T03:11:01.000Z | [
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:mit",
"creative problem solving",
"puzzles",
"fixation effect",
"large language models",
"only connect",
"quiz show",
"connecting walls",
"arxiv:2306.11167",
"region:us"
] | TaatiTeam | The Only Connect Wall (OCW) dataset contains 618 "Connecting Walls" from the Round 3: Connecting Wall segment of the Only Connect quiz show, collected from 15 seasons' worth of episodes. Each wall contains the ground-truth groups and connections as well as recorded human performance. | @article{Naeini2023LargeLM,
title = {Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset},
author = {Saeid Alavi Naeini and Raeid Saqur and Mozhgan Saeidi and John Giorgi and Babak Taati},
year = 2023,
journal = {ArXiv},
volume = {abs/2306.11167},
url = {https://api.semanticscholar.org/CorpusID:259203717}
} | null | 3 | 5 | ---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- creative problem solving
- puzzles
- fixation effect
- large language models
- only connect
- quiz show
- connecting walls
pretty_name: Only Connect Wall Dataset
size_categories:
- n<1K
---
# Only Connect Wall (OCW) Dataset
The Only Connect Wall (OCW) dataset contains 618 _"Connecting Walls"_ from the [Round 3: Connecting Wall](https://en.wikipedia.org/wiki/Only_Connect#Round_3:_Connecting_Wall) segment of the [Only Connect quiz show](https://en.wikipedia.org/wiki/Only_Connect), collected from 15 seasons' worth of episodes. Each wall contains the ground-truth __groups__ and __connections__ as well as recorded human performance. Please see [our paper](https://arxiv.org/abs/2306.11167) and [GitHub repo](https://github.com/TaatiTeam/OCW) for more details about the dataset and its motivations.
## Usage
```python
# pip install datasets
from datasets import load_dataset
dataset = load_dataset("TaatiTeam/OCW")
# The dataset can be used like any other HuggingFace dataset
# E.g. get the wall_id of the first example in the train set
dataset["train"]["wall_id"][0]
# or get the words of the first 10 examples in the test set
dataset["test"]["words"][0:10]
```
We also provide two different versions of the dataset where the red herrings in each wall have been significantly reduced (`ocw_randomized`) or removed altogether (`ocw_wordnet`) which can be loaded like:
```python
# pip install datasets
from datasets import load_dataset
ocw_randomized = load_dataset("TaatiTeam/OCW", "ocw_randomized")
ocw_wordnet = load_dataset("TaatiTeam/OCW", "ocw_wordnet")
```
See [our paper](https://arxiv.org/abs/2306.11167) for more details.
## 📝 Citing
If you use the Only Connect dataset in your work, please consider citing our paper:
```
@article{Naeini2023LargeLM,
title = {Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset},
author = {Saeid Alavi Naeini and Raeid Saqur and Mozhgan Saeidi and John Giorgi and Babak Taati},
year = 2023,
journal = {ArXiv},
volume = {abs/2306.11167},
url = {https://api.semanticscholar.org/CorpusID:259203717}
}
```
## 🙏 Acknowledgements
We would like the thank the maintainers and contributors of the fan-made and run website [https://ocdb.cc/](https://ocdb.cc/) for providing the data for this dataset. We would also like to thank the creators of the Only Connect quiz show for producing such an entertaining and thought-provoking show. |
usvsnsp/deduped-num-duplicates | 2023-08-25T13:34:00.000Z | [
"region:us"
] | usvsnsp | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: Index
dtype: int64
- name: Counts
dtype: int64
splits:
- name: train
num_bytes: 2342912000
num_examples: 146432000
download_size: 963525905
dataset_size: 2342912000
---
# Dataset Card for "deduped-num-duplicates"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
crodri/meteocat | 2023-10-10T15:17:14.000Z | [
"task_categories:text-generation",
"task_categories:token-classification",
"task_categories:question-answering",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ca",
"region:us"
] | crodri | null | null | null | 0 | 5 | ---
language:
- ca
multilinguality:
- monolingual
pretty_name: synthetic_meteocat
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- token-classification
- question-answering
task_ids:
- named-entity-recognition
---
# Dataset Card for Meteocat
## 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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Point of Contact:** [langtech@bsc.es](langtech@bsc.es)
### Dataset Summary
This is a synthetic dataset that contains examples, each of them, with the following fields:
- Instructions like "El dissabte a la nit, quin temps farà a Mont-real?"
- Context like "Day: dissabte | Location: Mont-real | mati: el cel estarà molt ennuvolat | tarda: plourà escadusserament | nit: el cel tendirà a estar cobert de núvols | temp: Lleugera pujada de les temperatures"
- Response like "A la nit el cel estarà ennuvolat"
Added instructions for answering "yes" or "no" questions.
### Supported Tasks and Leaderboards
This dataset is mainly intended to train models for text-generation and named-entity-recognition.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
The dataset consists of examples in a jsonl format with 3 fields each: instruction, context and response.
### Data Instances
Changed origina context for a more linguistically natural one: "tarda del divendres a Montesquiu al mati s'esperen més nuvolades, a la tarda guspirejarà amb insistència, a la nit podria guspirejar, i Temperatures sense canvis"
{
"instruction": "Quin temps farà a la nit a Camarasa dijous?",
xxx "context": "Day: dijous | Location: Camarasa | mati: el cel anirà encapotant-se cada cop més | tarda: el sol anirà guanyant terreny als núvols | nit: cel clar | temp: Temperatures sense canvis",
"response": "A la nit, cel ben clar"
}
### Data Fields
- instruction: Weather-related question.
xxx - context: Information in the format "Day: [DAY] | Location: [LOCATION] | mati: [WEATHER FORECAST] | tarda: [WEATHER FORECAST] | nit: [WEATHER FORECAST]".
- response: Whether forecast answering the question.
### Data Splits
* dev.json: 6873 examples
* test.json: 1279 examples
* train.json: 61776 examples
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
???? [Creative Commons Attribution Non-commercial No-Derivatives 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/).
### Contributions
[N/A]
|
nRuaif/Test-demo | 2023-09-12T13:31:37.000Z | [
"region:us"
] | nRuaif | null | null | null | 1 | 5 | Entry not found |
parambharat/bengali_asr_corpus | 2023-08-29T05:12:50.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|openslr",
"language:bn",
"license:cc-by-4.0",
"region:us"
] | parambharat | The corpus contains roughly 500 hours of audio and transcripts in Bangla language.
The transcripts have beed de-duplicated using exact match deduplication and audio has be converted to 16000 samples | null | 0 | 5 | ---
annotations_creators:
- found
language:
- bn
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Bengali ASR Corpus
size_categories:
- 100K<n<1M
source_datasets:
- extended|openslr
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for [Bengali Asr Corpus]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@parambharat](https://github.com/parambharat) for adding this dataset. | |
judy93536/perigon-200k | 2023-08-17T13:35:40.000Z | [
"region:us"
] | judy93536 | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 216299930.0087607
num_examples: 176584
- name: test
num_bytes: 38170719.9912393
num_examples: 31162
download_size: 129060894
dataset_size: 254470650.0
---
# Dataset Card for "perigon-200k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_RoBERTa_Baseline | 2023-08-17T23:30:46.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
splits:
- name: train
num_bytes: 115608907.5
num_examples: 37500
- name: test
num_bytes: 38536305.0
num_examples: 12500
download_size: 211881539
dataset_size: 154145212.5
---
# Dataset Card for "PKDD_RoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_DistilRoBERTa_Baseline | 2023-08-17T23:39:58.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115608907.5
num_examples: 37500
- name: test
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num_examples: 12500
download_size: 211880087
dataset_size: 154145212.5
---
# Dataset Card for "PKDD_DistilRoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_upstage__Llama-2-70b-instruct | 2023-08-27T12:24:15.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 5 | ---
pretty_name: Evaluation run of upstage/Llama-2-70b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [upstage/Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 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_upstage__Llama-2-70b-instruct\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-07-31T16:38:35.808290](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__Llama-2-70b-instruct/blob/main/results_2023-07-31T16%3A38%3A35.808290.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\": 0.6973946921458888,\n\
\ \"acc_stderr\": 0.031191911192388785,\n \"acc_norm\": 0.7012168959616505,\n\
\ \"acc_norm_stderr\": 0.031161684497000137,\n \"mc1\": 0.423500611995104,\n\
\ \"mc1_stderr\": 0.01729742144853473,\n \"mc2\": 0.6096689044038274,\n\
\ \"mc2_stderr\": 0.014988258592323236\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6706484641638225,\n \"acc_stderr\": 0.013734057652635474,\n\
\ \"acc_norm\": 0.7090443686006825,\n \"acc_norm_stderr\": 0.013273077865907592\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6877116112328221,\n\
\ \"acc_stderr\": 0.0046247963481288006,\n \"acc_norm\": 0.8748257319259112,\n\
\ \"acc_norm_stderr\": 0.003302401106926325\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.031546980450822305,\n\
\ \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.031546980450822305\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\
\ \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n \
\ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7547169811320755,\n \"acc_stderr\": 0.02648035717989569,\n\
\ \"acc_norm\": 0.7547169811320755,\n \"acc_norm_stderr\": 0.02648035717989569\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8472222222222222,\n\
\ \"acc_stderr\": 0.030085743248565663,\n \"acc_norm\": 0.8472222222222222,\n\
\ \"acc_norm_stderr\": 0.030085743248565663\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n\
\ \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n\
\ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.46296296296296297,\n \"acc_stderr\": 0.025680564640056882,\n \"\
acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.025680564640056882\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8161290322580645,\n \"acc_stderr\": 0.022037217340267826,\n \"\
acc_norm\": 0.8161290322580645,\n \"acc_norm_stderr\": 0.022037217340267826\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5467980295566502,\n \"acc_stderr\": 0.03502544650845872,\n \"\
acc_norm\": 0.5467980295566502,\n \"acc_norm_stderr\": 0.03502544650845872\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\"\
: 0.77,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.029311188674983134,\n\
\ \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.029311188674983134\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8888888888888888,\n \"acc_stderr\": 0.022390787638216766,\n \"\
acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.022390787638216766\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n\
\ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.02293992541853062,\n \
\ \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.02293992541853062\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3111111111111111,\n \"acc_stderr\": 0.02822644674968352,\n \
\ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.02822644674968352\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7563025210084033,\n \"acc_stderr\": 0.027886828078380558,\n\
\ \"acc_norm\": 0.7563025210084033,\n \"acc_norm_stderr\": 0.027886828078380558\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.45695364238410596,\n \"acc_stderr\": 0.04067325174247443,\n \"\
acc_norm\": 0.45695364238410596,\n \"acc_norm_stderr\": 0.04067325174247443\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8954128440366973,\n \"acc_stderr\": 0.013120530245265572,\n \"\
acc_norm\": 0.8954128440366973,\n \"acc_norm_stderr\": 0.013120530245265572\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n \"\
acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9117647058823529,\n \"acc_stderr\": 0.019907399791316942,\n \"\
acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.019907399791316942\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.890295358649789,\n \"acc_stderr\": 0.020343400734868837,\n \
\ \"acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.020343400734868837\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7668161434977578,\n\
\ \"acc_stderr\": 0.028380391147094706,\n \"acc_norm\": 0.7668161434977578,\n\
\ \"acc_norm_stderr\": 0.028380391147094706\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804475,\n\
\ \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804475\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807193,\n \"\
acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807193\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\
\ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\
\ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.803680981595092,\n \"acc_stderr\": 0.031207970394709225,\n\
\ \"acc_norm\": 0.803680981595092,\n \"acc_norm_stderr\": 0.031207970394709225\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n\
\ \"acc_stderr\": 0.019119892798924974,\n \"acc_norm\": 0.905982905982906,\n\
\ \"acc_norm_stderr\": 0.019119892798924974\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8735632183908046,\n\
\ \"acc_stderr\": 0.011884488905895538,\n \"acc_norm\": 0.8735632183908046,\n\
\ \"acc_norm_stderr\": 0.011884488905895538\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7774566473988439,\n \"acc_stderr\": 0.02239421566194282,\n\
\ \"acc_norm\": 0.7774566473988439,\n \"acc_norm_stderr\": 0.02239421566194282\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5932960893854748,\n\
\ \"acc_stderr\": 0.016428811915898858,\n \"acc_norm\": 0.5932960893854748,\n\
\ \"acc_norm_stderr\": 0.016428811915898858\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.025058503316958157,\n\
\ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.025058503316958157\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.752411575562701,\n\
\ \"acc_stderr\": 0.024513879973621967,\n \"acc_norm\": 0.752411575562701,\n\
\ \"acc_norm_stderr\": 0.024513879973621967\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8395061728395061,\n \"acc_stderr\": 0.02042395535477803,\n\
\ \"acc_norm\": 0.8395061728395061,\n \"acc_norm_stderr\": 0.02042395535477803\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5567375886524822,\n \"acc_stderr\": 0.02963483847376601,\n \
\ \"acc_norm\": 0.5567375886524822,\n \"acc_norm_stderr\": 0.02963483847376601\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5573663624511083,\n\
\ \"acc_stderr\": 0.01268590653820624,\n \"acc_norm\": 0.5573663624511083,\n\
\ \"acc_norm_stderr\": 0.01268590653820624\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02767846864214472,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02767846864214472\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7630718954248366,\n \"acc_stderr\": 0.017201662169789772,\n \
\ \"acc_norm\": 0.7630718954248366,\n \"acc_norm_stderr\": 0.017201662169789772\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7714285714285715,\n \"acc_stderr\": 0.02688214492230774,\n\
\ \"acc_norm\": 0.7714285714285715,\n \"acc_norm_stderr\": 0.02688214492230774\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\
\ \"acc_stderr\": 0.024112678240900798,\n \"acc_norm\": 0.8656716417910447,\n\
\ \"acc_norm_stderr\": 0.024112678240900798\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.423500611995104,\n\
\ \"mc1_stderr\": 0.01729742144853473,\n \"mc2\": 0.6096689044038274,\n\
\ \"mc2_stderr\": 0.014988258592323236\n }\n}\n```"
repo_url: https://huggingface.co/upstage/Llama-2-70b-instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|arc:challenge|25_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hellaswag|10_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T16:38:35.808290.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-31T16:38:35.808290.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-31T16:38:35.808290.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-31T16:38:35.808290.parquet'
- config_name: results
data_files:
- split: 2023_07_31T16_38_35.808290
path:
- results_2023-07-31T16:38:35.808290.parquet
- split: latest
path:
- results_2023-07-31T16:38:35.808290.parquet
---
# Dataset Card for Evaluation run of upstage/Llama-2-70b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/upstage/Llama-2-70b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [upstage/Llama-2-70b-instruct](https://huggingface.co/upstage/Llama-2-70b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 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_upstage__Llama-2-70b-instruct",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-07-31T16:38:35.808290](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__Llama-2-70b-instruct/blob/main/results_2023-07-31T16%3A38%3A35.808290.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": 0.6973946921458888,
"acc_stderr": 0.031191911192388785,
"acc_norm": 0.7012168959616505,
"acc_norm_stderr": 0.031161684497000137,
"mc1": 0.423500611995104,
"mc1_stderr": 0.01729742144853473,
"mc2": 0.6096689044038274,
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```
### 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] |
EgilKarlsen/PKDD_GPT2_Baseline | 2023-08-17T23:56:31.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115608907.5
num_examples: 37500
- name: test
num_bytes: 38536305.0
num_examples: 12500
download_size: 211867982
dataset_size: 154145212.5
---
# Dataset Card for "PKDD_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_GPTNEO_Baseline | 2023-08-18T01:46:31.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 307608907.5
num_examples: 37500
- name: test
num_bytes: 102536305.0
num_examples: 12500
download_size: 565384532
dataset_size: 410145212.5
---
# Dataset Card for "PKDD_GPTNEO_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_BERT_Baseline | 2023-08-18T17:21:29.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
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num_bytes: 115650065.625
num_examples: 37500
- name: test
num_bytes: 38550020.0
num_examples: 12500
download_size: 211761700
dataset_size: 154200085.625
---
# Dataset Card for "Spirit_BERT_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_RoBERTa_Baseline | 2023-08-18T17:28:06.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115650065.625
num_examples: 37500
- name: test
num_bytes: 38550020.0
num_examples: 12500
download_size: 211789418
dataset_size: 154200085.625
---
# Dataset Card for "Spirit_RoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_DistilRoBERTa_Baseline | 2023-08-18T17:34:21.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115650065.625
num_examples: 37500
- name: test
num_bytes: 38550020.0
num_examples: 12500
download_size: 211786903
dataset_size: 154200085.625
---
# Dataset Card for "Spirit_DistilRoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_GPT2_Baseline | 2023-08-18T17:41:00.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115650065.625
num_examples: 37500
- name: test
num_bytes: 38550020.0
num_examples: 12500
download_size: 211782412
dataset_size: 154200085.625
---
# Dataset Card for "Spirit_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_GPTNEO_Baseline | 2023-08-18T18:05:42.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 307650065.625
num_examples: 37500
- name: test
num_bytes: 102550020.0
num_examples: 12500
download_size: 565185055
dataset_size: 410200085.625
---
# Dataset Card for "Spirit_GPTNEO_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Thunderbird_RoBERTa_Baseline | 2023-08-23T03:40:56.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115576729.6875
num_examples: 37500
- name: test
num_bytes: 38525577.5
num_examples: 12500
download_size: 0
dataset_size: 154102307.1875
---
# Dataset Card for "Thunderbird_RoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Thunderbird_DistilRoBERTa_Baseline | 2023-08-23T03:42:12.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115576729.6875
num_examples: 37500
- name: test
num_bytes: 38525577.5
num_examples: 12500
download_size: 0
dataset_size: 154102307.1875
---
# Dataset Card for "Thunderbird_DistilRoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Thunderbird_GPT2_Baseline | 2023-08-23T03:44:12.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
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num_bytes: 115576729.6875
num_examples: 37500
- name: test
num_bytes: 38525577.5
num_examples: 12500
download_size: 0
dataset_size: 154102307.1875
---
# Dataset Card for "Thunderbird_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Thunderbird_GPTNEO_Baseline | 2023-08-18T20:03:23.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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dtype: float32
- name: '2039'
dtype: float32
- name: '2040'
dtype: float32
- name: '2041'
dtype: float32
- name: '2042'
dtype: float32
- name: '2043'
dtype: float32
- name: '2044'
dtype: float32
- name: '2045'
dtype: float32
- name: '2046'
dtype: float32
- name: '2047'
dtype: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 307576729.6875
num_examples: 37500
- name: test
num_bytes: 102525577.5
num_examples: 12500
download_size: 565392402
dataset_size: 410102307.1875
---
# Dataset Card for "Thunderbird_GPTNEO_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
karanzrk/ielts | 2023-08-19T19:07:34.000Z | [
"region:us"
] | karanzrk | null | null | null | 0 | 5 | Entry not found |
theoldmandthesea/17k_business_book | 2023-08-20T08:14:02.000Z | [
"region:us"
] | theoldmandthesea | null | null | null | 0 | 5 | ---
# For reference on model 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
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] |
breadlicker45/gigachad-chat | 2023-08-20T15:01:11.000Z | [
"region:us"
] | breadlicker45 | null | null | null | 1 | 5 | Entry not found |
slotreck/pickle | 2023-08-22T20:13:38.000Z | [
"language:en",
"license:cc-by-4.0",
"biology",
"plant science",
"named entity recognition",
"relation extraction",
"region:us"
] | slotreck | null | null | null | 0 | 5 | ---
license: cc-by-4.0
language:
- en
tags:
- biology
- plant science
- named entity recognition
- relation extraction
pretty_name: PICKLE
---
# PICKLE
PICKLE is the dataset associated with the manuscript *In a PICKLE: A gold standard entity and relation corpus for the molecular plant sciences*. The code repository associated with this dataset can be found [here](https://github.com/serenalotreck/pickle-corpus-code).
## Format specification
This dataset is formatted according to the [specifications for use with the DyGIE++ architecture](https://github.com/dwadden/dygiepp/blob/master/doc/data.md).
## Dataset details
There are a total of 250 documents in `all.jsonl`, split up into 68%/12%/20% train/dev/test. Each document is an abstract from a scientific paper in the search results for the terms "gibberellic acid" and "jasmonic acid". There are 6,245 entity and 2,149 relation annotations across the 250 documents. |
gabbietheguy/uvote_testing | 2023-09-12T00:07:01.000Z | [
"region:us"
] | gabbietheguy | null | null | null | 0 | 5 | Entry not found |
HSiTori/scienceQA | 2023-08-24T03:54:07.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] | HSiTori | null | null | null | 0 | 5 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1147845
num_examples: 2135
- name: validation
num_bytes: 404325
num_examples: 764
- name: test
num_bytes: 419010
num_examples: 789
download_size: 707887
dataset_size: 1971180
task_categories:
- text-generation
language:
- en
size_categories:
- 1K<n<10K
---
# Filter: no image && hint != '' |
LawChat-tw/PT | 2023-08-24T03:49:38.000Z | [
"region:us"
] | LawChat-tw | null | null | null | 0 | 5 | Entry not found |
shirsh10mall/Image_Captioning_GCC_Embeddings | 2023-08-24T18:55:01.000Z | [
"region:us"
] | shirsh10mall | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: caption
dtype: string
- name: image_data
dtype: image
- name: image_embeddings
sequence: float32
- name: text_embeddings
sequence: float32
splits:
- name: train
num_bytes: 18422754083.25
num_examples: 143134
download_size: 18711756794
dataset_size: 18422754083.25
---
# Dataset Card for "Image_Captioning_GCC_Embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ashhadulislam/arabic_medical_test | 2023-08-24T22:02:19.000Z | [
"license:mit",
"region:us"
] | ashhadulislam | null | null | null | 1 | 5 | ---
license: mit
---
|
ShuaKang/calvin_d_d_gripper | 2023-08-25T02:03:11.000Z | [
"region:us"
] | ShuaKang | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: obs_image
dtype: image
- name: goal_image
dtype: image
- name: text
dtype: string
- name: gripper_image
dtype: image
splits:
- name: train
num_bytes: 457740849.5
num_examples: 5124
download_size: 458013523
dataset_size: 457740849.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "calvin_d_d_gripper"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
abdiharyadi/indo-amr-simple-ilmy-test-1.0.1 | 2023-08-25T02:12:05.000Z | [
"region:us"
] | abdiharyadi | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: amr
dtype: string
splits:
- name: train
num_bytes: 44012
num_examples: 306
download_size: 21662
dataset_size: 44012
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "indo-amr-simple-ilmy-test-1.0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
OmkarB/Stripped_SQL_GQL_Transaltions_Dataset | 2023-08-25T17:52:49.000Z | [
"region:us"
] | OmkarB | null | null | null | 0 | 5 | Entry not found |
AnimaLab/bias-test-gpt-sentences | 2023-09-04T03:14:03.000Z | [
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | AnimaLab | null | null | null | 0 | 5 | ---
license: apache-2.0
language:
- en
pretty_name: BiasTestGPT
size_categories:
- 10K<n<100K
---
# Dataset Card for "BiasTestGPT: Generated Test Sentences"
Dataset of sentences for bias testing in open-sourced Pretrained Language Models generated using ChatGPT and other generative Language Models.
This dataset is used and actively populated by the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
- [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs)
- [Dataset with Bias Specifications](https://huggingface.co/datasets/AnimaLab/bias-test-gpt-biases)
- [Project Landing Page](https://biastest-animalab.github.io/)
## Dataset Structure
The dataset is structured as a set of CSV files with names corresponding to the social group term for which the test sentences were generated.
Each separate file contains the sentences generated with the instruction of connecting this social group term to other attribute terms.
### Data Instances
Examples of 'generated sentences' from the dataset in CSV format look as follows (not all columns are shown).
| sentence | alt_sentence | org_grp_term | att_term | grp_term1 | grp_term2 | type | gen_model |
|----------|--------------|--------------|----------|-----------|-----------|------|----------:|
| Adam felt a sense of freedom as he left his old job and pursued his passion. | Jane felt a sense of freedom as she left her old job and pursued her passion. | Adam | freedom | Adam | Jane | paper | gpt-3.5 |
| My husband and I are excited to start a family together. | My wife and I are excited to start a family together. | husband | family | husband | wife | tool | gpt-3.5
| My daughter loves to experiment with different ingredients when she's cooking. | My son loves to experiment with different ingredients when he's cooking. | daughter | cooking | daughter | son | paper | gpt-3.5 |
| A woman solves complex math problems with ease | A man solves complex math problems with ease | woman | math | woman | man | tool | gpt-3.5
### Data Fields
Here we describe the data fields in the dataset. These are the same across all the splits.
#### CSV columns
- **'sentence'**: a 'string' feature - PLM generated test sentence that includes 'grp_term1' and 'att_term'
- **'alt_sentence'**: a 'string' feature - PLM generated alternative version of the test sentence that includes 'grp_term2' and 'att_term'
- **'org_grp_term'**: a `string' feature - a social group term for which the sentence was generated.
- **'att_term'**: a 'string' feature - an attribute term for which the sentence was created.
- **'template'**: a 'string' feature - a templated version of the sentence with social group replaced by [T]
- **'alt_template'**: a 'string' feature - a templated version of the sentence with social group replaced by [T] and other token differences replaced by [R]
- **'grp_term1'** - a 'string' feature - a term from social group 1 used in *'sentence'*
- **'grp_term2'** - a 'string' feature - a term from social group 2 used in *'alt_sentence'*
- **'grp_refs'** - a 'list' feature - a list of differences between the *'sentence'* and *'alt_sentence'* apart of group_term. Each item is a tuple with paired versions of tokens from 'sentence' and 'alt_sentnece'.
- **'label_1'** - a 'string' feature - whether filling in the template with **group term 1** is considered to produce a 'stereotype' or 'anti-stereotype'
- **'label_2'** - a 'string' feature - whether filling in the template with **group term 2** is considered to produce a 'stereotype' or 'anti-stereotype'
- **'bias_spec'** - a 'string' feature - the name of the bias specification for which the sentence was generated
- **'type'**: a 'string' feature - the source of the generation; `paper' indicates the sentence was used in the analysis in the paper, another value indicates the sentence generated using the HuggingFace tool
- **'gen_model'**: a 'string' feature - the name of the generator model used
### Data Splits
The repository contains 14k+ sentences generated using ChatGPT and another very large PLM.
The analysis in the paper was conducted using the sentences from ChatGPT only. Additional test sentences have been added afterward as a result of interaction with the tool.
We note that the number of sentences is constantly growing as it is being populated by the interactions with the [BiasTestGPT HuggingFace Tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs).
| Type | Meaning | Train |
|--------|---------|------:|
| paper | Test sentences used in the analysis in the paper | 9k+ |
| tool | Novel test sentences added to the dataset based on interactions with the [bias test tool](https://huggingface.co/spaces/AnimaLab/bias-test-gpt-pairs) | 500+ | |
eduagarcia/brwac_dedup | 2023-08-27T20:24:16.000Z | [
"region:us"
] | eduagarcia | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 17503358516
num_examples: 3513588
download_size: 10720096897
dataset_size: 17503358516
---
# Dataset Card for "brwac_dedup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
quocanh34/fake_dataset | 2023-08-28T07:57:55.000Z | [
"region:us"
] | quocanh34 | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: intent
dtype: string
- name: sentence_annotation
dtype: string
- name: entities
list:
- name: type
dtype: string
- name: filler
dtype: string
- name: file
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
splits:
- name: train
num_bytes: 2213835
num_examples: 5
download_size: 614583
dataset_size: 2213835
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fake_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
S-ogawa-S/databricks-dolly-15k-ja-nanoja | 2023-09-26T01:17:07.000Z | [
"region:us"
] | S-ogawa-S | null | null | null | 0 | 5 | # データセットの概要
以下の、主語を拙者、語尾をござるに変更したデータセットを元に、語尾を「なのじゃ」に変換したデータセットである。
---
license: cc-by-sa-3.0
---
This dataset was using "bbz662bbz/databricks-dolly-15k-ja-gozaru"
This dataset is licensed under CC BY SA 3.0
bbz662bbz/databricks-dolly-15k-ja-gozaru
https://huggingface.co/datasets/bbz662bbz/databricks-dolly-15k-ja-gozaru
|
jtatman/orca_mini_uncensored_squad_format_train | 2023-09-05T02:25:49.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] | jtatman | null | null | null | 0 | 5 | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- question-answering
pretty_name: orca_mini_squad
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 118261864.35315199
num_examples: 67300
- name: test
num_bytes: 13140597.646848004
num_examples: 7478
download_size: 65276229
dataset_size: 131402462.0
---
# Dataset Card for "orca_mini_uncensored_squad_format_train"
## Dataset Description
Mostly purely an exercise in data extraction and formatting for dataset usage, and cross-model usage of data.
Uncensored data, because when everything is sanitized for alignment, the data may be "pure" but is no longer untimately realistic.
Part of an effort to create more question-answering friendly datasets that can be used for specialized domain training on small models.
### Dataset Summary
This is a "squad reformat" of an existing dataset located here: https://huggingface.co/datasets/julep-ai/orca_mini_uncensored
This could be swapped for squad format datasets for typical question-answering tasks with uncensored data from a partial pull of the mini-orca dataset here: psmathur/orca_minis_uncensored_dataset
### Supported Tasks and Leaderboards
- 'question-answering'
### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
## Dataset Structure
Train and Test splits included
### Data Format
As in the squadv2 dataset, columns are: "id", "title", "context", "question", "answers": "text", "answer_start"
|
hugfaceguy0001/ChatGPTGroundTruth | 2023-08-30T18:03:37.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:openrail",
"science",
"region:us"
] | hugfaceguy0001 | null | null | null | 1 | 5 | ---
license: openrail
task_categories:
- question-answering
language:
- en
tags:
- science
pretty_name: ChatGPT ground truth
size_categories:
- 10K<n<100K
configs:
- config_name: main_data
data_files: "ground_truth.jsonl"
---
# ChatGPT ground truth dataset
This dataset is generated by ChatGPT and contains factual questions and corresponding answers from 160 subfields across natural and social sciences.
Specifically, the dataset covers eight major domains: mathematics, physics, chemistry, biology, medicine, engineering, computer science, and social sciences. Within each domain, 20 specific subfields are selected, with 500 question-answer pairs per subfield, resulting in a total of 80,000 question-answer pairs.
The language used in this dataset is English.
Accompanying the release of this dataset is the script code used to generate it.
# ChatGPT基准事实数据集
本数据集由ChatGPT自动生成，包含自然科学和社会科学的160个细分领域的事实性问题和相应的答案。
具体来说，本数据集涵盖数学、物理、化学、生物学、医学、工程、计算机科学、社会科学八大领域，每个领域选择了20个细分子领域，每个子领域有500个问答对，共80000个问答对。
本数据集的语言为英文。
和本数据集同时发布的还有生成本数据集使用的脚本代码。 |
kennethme9/okokko | 2023-08-30T20:16:24.000Z | [
"region:us"
] | kennethme9 | null | null | null | 0 | 5 | Entry not found |
mvansegb123/commonsense-dialogues | 2023-08-30T23:10:24.000Z | [
"task_categories:text-classification",
"task_categories:table-question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"arxiv:2109.06427",
"region:us"
] | mvansegb123 | null | null | null | 1 | 5 | ---
license: cc
task_categories:
- text-classification
- table-question-answering
- text-generation
language:
- en
size_categories:
- 1K<n<10K
---
## Commonsense-Dialogues Dataset
We present Commonsense-Dialogues, a crowdsourced dataset of ~11K dialogues grounded in social contexts involving utilization of commonsense. The social contexts used were sourced from the **train** split of the [SocialIQA](https://leaderboard.allenai.org/socialiqa/submissions/get-started) dataset, a multiple-choice question-answering based social commonsense reasoning benchmark.
For the collection of the Commonsense-Dialogues dataset, each Turker was presented a social context and asked to write a dialogue of 4-6 turns between two people based on the event(s) described in the context. The Turker was asked to alternate between the roles of an individual referenced in the context and a 3rd party friend. See the following dialogues as examples:
```
"1": { # dialogue_id
"context": "Sydney met Carson's mother for the first time last week. He liked her.", # multiple individuals in the context: Sydney and Carson
"speaker": "Sydney", # role 1 = Sydney, role 2 = a third-person friend of Sydney
"turns": [
"I met Carson's mother last week for the first time.",
"How was she?",
"She turned out to be really nice. I like her.",
"That's good to hear.",
"It is, especially since Carson and I are getting serious.",
"Well, at least you'll like your in-law if you guys get married."
]
}
"2": {
"context": "Kendall had a party at Jordan's house but was found out to not have asked and just broke in.",
"speaker": "Kendall",
"turns": [
"Did you hear about my party this weekend at Jordan\u2019s house?",
"I heard it was amazing, but that you broke in.",
"That was a misunderstanding, I had permission to be there.",
"Who gave you permission?",
"I talked to Jordan about it months ago before he left town to go to school, but he forgot to tell his roommates about it.",
"Ok cool, I hope everything gets resolved."
]
}
```
The data can be found in the `/data` directory of this repo. `train.json` has ~9K dialogues, `valid.json` and `test.json` have ~1K dialogues each. Since all the contexts were sourced from the **train** split of SocialIQA, it is imperative to note that any form of **multi-task** training and evaluation with Commonsense-Dialogues and SocialIQA must be done with caution to ensure fair and accurate conclusions.
Some statistics about the data are provided below:
| Stat | Train | Valid | Test |
| ---- | ---- | ---- | ---- |
|# of dialogues | 9058 | 1157 | 1158 |
|average # of turns in a dialogue | 5.72 | 5.72 | 5.71 |
|average # of words in a turn | 12.4 | 12.4 | 12.2 |
|# of distinct SocialIQA contexts used | 3672 | 483 | 473 |
|average # of dialogues for a SocialIQA context| 2.46 | 2.395 | 2.45 |
## Security
See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
## License
This repository is licensed under the CC-BY-NC 4.0 License.
## Citation
If you use this dataset, please cite the following paper:
```
@inproceedings{zhou-etal-2021-commonsense,
title = "Commonsense-Focused Dialogues for Response Generation: An Empirical Study",
author = "Zhou, Pei and
Gopalakrishnan, Karthik and
Hedayatnia, Behnam and
Kim, Seokhwan and
Pujara, Jay and
Ren, Xiang and
Liu, Yang and
Hakkani-Tur, Dilek",
booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
year = "2021",
address = "Singapore and Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2109.06427"
}
```
Note that the paper uses newly collected dialogues as well as those that were filtered from existing datasets. This repo contains our newly collected dialogues alone. |
mikewang/EuroSAT | 2023-08-31T16:33:44.000Z | [
"language:en",
"license:mit",
"region:us"
] | mikewang | **Homepage:** https://github.com/phelber/EuroSAT
**IMPORTANT NOTES**
- This HF dataset downloads the RGB images of the EuroSAT dataset: https://zenodo.org/record/7711810#.ZAm3k-zMKEA; i.e., the EuroSAT_RGB.zip | @article{helber2019eurosat,
title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2019},
publisher={IEEE}
} | null | 0 | 5 | ---
pretty_name: 'EuroSAT (RGB)'
language:
- en
license: mit
---
# Dataset Card for EuroSAT Dataset (RGB)
## Dataset Description
**Homepage:** https://github.com/phelber/EuroSAT
**IMPORTANT NOTES**
- This HF dataset downloads the RGB images of the EuroSAT dataset: https://zenodo.org/record/7711810#.ZAm3k-zMKEA; i.e., the EuroSAT_RGB.zip
**Paper Citation:**
```
@article{helber2019eurosat,
title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2019},
publisher={IEEE}
}
```
```
@inproceedings{helber2018introducing,
title={Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
pages={204--207},
year={2018},
organization={IEEE}
}
```
## Dataset Summary
In this study, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available [here](https://github.com/phelber/EuroSAT). |
Lancelot53/srbd1_segmented | 2023-09-01T13:58:29.000Z | [
"region:us"
] | Lancelot53 | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: html
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1446076
num_examples: 1496
download_size: 0
dataset_size: 1446076
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "srbd1_segmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MFR096/farm_crop | 2023-09-01T21:03:11.000Z | [
"license:mit",
"doi:10.57967/hf/1064",
"region:us"
] | MFR096 | null | null | null | 1 | 5 | ---
license: mit
---
|
yangwang825/audioset | 2023-09-18T11:19:55.000Z | [
"task_categories:audio-classification",
"size_categories:100M<n<1B",
"audioset",
"region:us"
] | yangwang825 | null | null | null | 0 | 5 | ---
configs:
- config_name: audioset500k
data_files:
- split: train
path: audioset500k.json
- config_name: balanced_train
data_files:
- split: train
path: balanced_train.json
- config_name: eval
data_files:
- split: test
path: eval.json
- config_name: unbalanced_train_part00
data_files: unbalanced_train_part00.json
# dataset_size: 46940
- config_name: unbalanced_train_part01
data_files: unbalanced_train_part01.json
# dataset_size: 47052
- config_name: unbalanced_train_part02
data_files: unbalanced_train_part02.json
# dataset_size: 46923
- config_name: unbalanced_train_part03
data_files: unbalanced_train_part03.json
# dataset_size: 46952
- config_name: unbalanced_train_part04
data_files: unbalanced_train_part04.json
# dataset_size: 46916
- config_name: unbalanced_train_part05
data_files: unbalanced_train_part05.json
# dataset_size: 47011
- config_name: unbalanced_train_part06
data_files: unbalanced_train_part06.json
# dataset_size: 46964
- config_name: unbalanced_train_part07
data_files: unbalanced_train_part07.json
# dataset_size: 46915
- config_name: unbalanced_train_part08
data_files: unbalanced_train_part08.json
# dataset_size: 46927
- config_name: unbalanced_train_part09
data_files: unbalanced_train_part09.json
# dataset_size: 46839
- config_name: unbalanced_train_part10
data_files: unbalanced_train_part10.json
# dataset_size: 46862
- config_name: unbalanced_train_part11
data_files: unbalanced_train_part11.json
# dataset_size: 46836
- config_name: unbalanced_train_part12
data_files: unbalanced_train_part12.json
# dataset_size: 46865
- config_name: unbalanced_train_part13
data_files: unbalanced_train_part13.json
# dataset_size: 46800
- config_name: unbalanced_train_part14
data_files: unbalanced_train_part14.json
# dataset_size: 46837
- config_name: unbalanced_train_part15
data_files: unbalanced_train_part15.json
# dataset_size: 46824
- config_name: unbalanced_train_part16
data_files: unbalanced_train_part16.json
# dataset_size: 46813
- config_name: unbalanced_train_part17
data_files: unbalanced_train_part17.json
# dataset_size: 46771
- config_name: unbalanced_train_part18
data_files: unbalanced_train_part18.json
# dataset_size: 46875
- config_name: unbalanced_train_part19
data_files: unbalanced_train_part19.json
# dataset_size: 46885
- config_name: unbalanced_train_part20
data_files: unbalanced_train_part20.json
# dataset_size: 46884
- config_name: unbalanced_train_part21
data_files: unbalanced_train_part21.json
# dataset_size: 46736
- config_name: unbalanced_train_part22
data_files: unbalanced_train_part22.json
# dataset_size: 46832
- config_name: unbalanced_train_part23
data_files: unbalanced_train_part23.json
# dataset_size: 46823
- config_name: unbalanced_train_part24
data_files: unbalanced_train_part24.json
# dataset_size: 46795
- config_name: unbalanced_train_part25
data_files: unbalanced_train_part25.json
# dataset_size: 46740
- config_name: unbalanced_train_part26
data_files: unbalanced_train_part26.json
# dataset_size: 46765
- config_name: unbalanced_train_part27
data_files: unbalanced_train_part27.json
# dataset_size: 46708
- config_name: unbalanced_train_part28
data_files: unbalanced_train_part28.json
# dataset_size: 46736
- config_name: unbalanced_train_part29
data_files: unbalanced_train_part29.json
# dataset_size: 46819
- config_name: unbalanced_train_part30
data_files: unbalanced_train_part30.json
# dataset_size: 46694
- config_name: unbalanced_train_part31
data_files: unbalanced_train_part31.json
# dataset_size: 46735
- config_name: unbalanced_train_part32
data_files: unbalanced_train_part32.json
# dataset_size: 46731
- config_name: unbalanced_train_part33
data_files: unbalanced_train_part33.json
# dataset_size: 46627
- config_name: unbalanced_train_part34
data_files: unbalanced_train_part34.json
# dataset_size: 46740
- config_name: unbalanced_train_part35
data_files: unbalanced_train_part35.json
# dataset_size: 46866
- config_name: unbalanced_train_part36
data_files: unbalanced_train_part36.json
# dataset_size: 46758
- config_name: unbalanced_train_part37
data_files: unbalanced_train_part37.json
# dataset_size: 46751
- config_name: unbalanced_train_part38
data_files: unbalanced_train_part38.json
# dataset_size: 46750
- config_name: unbalanced_train_part39
data_files: unbalanced_train_part39.json
# dataset_size: 46700
- config_name: unbalanced_train_part40
data_files: unbalanced_train_part40.json
# dataset_size: 39137
task_categories:
- audio-classification
tags:
- audioset
size_categories:
- 100M<n<1B
---
# AudioSet
AudioSet<sup>[1]</sup> consists of an expanding ontology of 527 audio event classes and a collection of 2M human-labelled 10-second sound clips drawn from YouTube.
Some clips are missing on YouTube, so the number of files downloaded is different from time to time.
This repository contains 20550 / 22160 of the balanced train set, 1913637 / 2041789 of the unbalanced train set (separated into 41 parts), and 18887 / 20371 of the evaluation set.
The pre-process script can be found at qiuqiangkong's [github](https://github.com/qiuqiangkong/audioset_tagging_cnn)<sup>[2]</sup>.
To improve training efficiency, we add a slightly more balanced subset AudioSet500K<sup>[3]</sup>.
## References
1. Gemmeke, Jort F., et al., Audio set: An ontology and human-labeled dataset for audio events, 2017
2. Kong, Qiuqiang, et al., Panns: Large-scale pretrained audio neural networks for audio pattern recognition, 2020
3. Nagrani, Arsha, et al., Attention bottlenecks for multimodal fusion, 2021 |
rombodawg/LosslessMegaCodeTrainingV3_MINI | 2023-09-07T23:48:13.000Z | [
"license:other",
"region:us"
] | rombodawg | null | null | null | 5 | 5 | ---
license: other
---
This is a new version and experinmental version of the LosslessMegacodeTraining series. Its like the version 3 but only using the most refine parts of the dataset.
The content of this dataset is roughly 80% coding instruction data and 20% non-coding instruction data. Amounting to 650,000 evol instruction-formatted lines of data.
The outcome of having 20% non coding instruction data in the dataset is to preserve logic and reasoning skills within the model while training on coding. The lack of such skills has been observed to be a major issue with coding models such as Wizardcoder-15b and NewHope, but training models on this dataset alleviates that issue while also giving similar levels of coding knowledge.
This dataset is a combination of the following datasets:
- https://huggingface.co/datasets/rombodawg/Platypus_Evol
- https://huggingface.co/datasets/rombodawg/Rombodawgs_commitpackft_Evolinstruct_Converted
- https://huggingface.co/datasets/rombodawg/airoboros-2.1_general_purpose
- https://huggingface.co/datasets/shahules786/megacode-best |
carles-undergrad-thesis/indo-snli | 2023-09-04T06:51:11.000Z | [
"region:us"
] | carles-undergrad-thesis | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: premise
dtype: string
- name: hyphothesis
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 71816446
num_examples: 550152
- name: validation
num_bytes: 1378057
num_examples: 10000
- name: test
num_bytes: 1373665
num_examples: 10000
download_size: 20415274
dataset_size: 74568168
---
# Dataset Card for "indo-snli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
collabora/ai4bharat-shrutilipi-augmented | 2023-09-04T11:32:27.000Z | [
"region:us"
] | collabora | null | null | null | 0 | 5 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 138891088302.0
num_examples: 200000
download_size: 138321935852
dataset_size: 138891088302.0
---
# Dataset Card for "ai4bharat-shrutilipi-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
legacy107/covidqa-unique-context | 2023-09-06T13:46:53.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"medical",
"region:us"
] | legacy107 | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: document_id
dtype: int64
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: int64
- name: answer
dtype: string
- name: answer_start
dtype: int64
splits:
- name: train
num_bytes: 61459473
num_examples: 1815
- name: test
num_bytes: 3699592
num_examples: 204
download_size: 2273929
dataset_size: 65159065
language:
- en
pretty_name: CovidQA with unique context for est
task_categories:
- question-answering
tags:
- medical
size_categories:
- 1K<n<10K
---
# Dataset Card for "covidqa-unique-context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_BERT_FT | 2023-09-04T13:12:04.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115608885
num_examples: 37500
- name: test
num_bytes: 38536331
num_examples: 12500
download_size: 211882193
dataset_size: 154145216
---
# Dataset Card for "PKDD_BERT_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_RoBERTa_FT | 2023-09-04T13:18:48.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115608885
num_examples: 37500
- name: test
num_bytes: 38536331
num_examples: 12500
download_size: 211881037
dataset_size: 154145216
---
# Dataset Card for "PKDD_RoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_DistilRoBERTa_FT | 2023-09-04T13:22:41.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115608885
num_examples: 37500
- name: test
num_bytes: 38536331
num_examples: 12500
download_size: 211881010
dataset_size: 154145216
---
# Dataset Card for "PKDD_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_GPT2_FT | 2023-09-04T13:34:02.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115608885
num_examples: 37500
- name: test
num_bytes: 38536331
num_examples: 12500
download_size: 211875771
dataset_size: 154145216
---
# Dataset Card for "PKDD_GPT2_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_GPTNEO_FT | 2023-09-04T15:10:59.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 307608885
num_examples: 37500
- name: test
num_bytes: 102536331
num_examples: 12500
download_size: 565398170
dataset_size: 410145216
---
# Dataset Card for "PKDD_GPTNEO_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_BERT_FT | 2023-09-04T15:39:58.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115650093
num_examples: 37500
- name: test
num_bytes: 38549993
num_examples: 12500
download_size: 211768316
dataset_size: 154200086
---
# Dataset Card for "Spirit_BERT_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_RoBERTa_FT | 2023-09-04T15:41:29.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115650093
num_examples: 37500
- name: test
num_bytes: 38549993
num_examples: 12500
download_size: 211781697
dataset_size: 154200086
---
# Dataset Card for "Spirit_RoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_GPT2_FT | 2023-09-04T15:44:08.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115650093
num_examples: 37500
- name: test
num_bytes: 38549993
num_examples: 12500
download_size: 211777486
dataset_size: 154200086
---
# Dataset Card for "Spirit_GPT2_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Spirit_GPTNEO_FT | 2023-09-04T15:56:06.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 307650093
num_examples: 37500
- name: test
num_bytes: 102549993
num_examples: 12500
download_size: 565171727
dataset_size: 410200086
---
# Dataset Card for "Spirit_GPTNEO_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Thunderbird_BERT_FT | 2023-09-04T15:58:25.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115576722
num_examples: 37500
- name: test
num_bytes: 38525585
num_examples: 12500
download_size: 211880915
dataset_size: 154102307
---
# Dataset Card for "Thunderbird_BERT_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Thunderbird_RoBERTa_FT | 2023-09-04T16:00:23.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115576722
num_examples: 37500
- name: test
num_bytes: 38525585
num_examples: 12500
download_size: 211881891
dataset_size: 154102307
---
# Dataset Card for "Thunderbird_RoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/Thunderbird_DistilRoBERTa_FT | 2023-09-04T16:01:43.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 5 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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dtype: string
splits:
- name: train
num_bytes: 115576722
num_examples: 37500
- name: test
num_bytes: 38525585
num_examples: 12500
download_size: 211881897
dataset_size: 154102307
---
# Dataset Card for "Thunderbird_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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