datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
MuraliKrish/cropData | ---
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
tags:
- agriculture
- crops
size_categories:
- n<1K
--- |
clairebarale/AsyLex | ---
annotations_creators: []
language:
- en
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: AsyLex
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- legal NLP
- Refugee Law
task_categories:
- text-classification
- token-classification
- text-retrieval
task_ids:
- multi-label-classification
- named-entity-recognition
- document-retrieval
- utterance-retrieval
configs:
- config_name: raw_sentences
data_files: all_sentences_anonymized.tar.xz
default: true
- config_name: raw_documents
data_files: cases_anonymized_txt_raw.tar.gz
- config_name: all_legal_entities
data_files: main_and_case_cover_all_entities_inferred.csv
- config_name: casecover_legal_entities
data_files: case_cover/case_cover_anonymised_extracted_entities.csv
- config_name: casecover_entities_outcome
data_files: case_cover/case_cover_entities_and_decision_outcome.csv
- config_name: determination_sentences
data_files: determination_label_extracted_sentences.csv
- config_name: outcome_classification
data_files:
- split: train
path: "outcome_train_test/train_dataset_silver.csv"
- split: test
path: "outcome_train_test/test_dataset_gold.csv"
config_names:
- raw_documents
- raw_sentences
- all_legal_entities
- casecover_legal_entities
- casecover_entities_outcome
- determination_sentencess
- outcome_classification
---
# Dataset Card for AsyLex
The dataset introduces 59,112 documents of refugee status determination in Canada from 1996 to 2022, providing researchers and practitioners with essential material for training and evaluating NLP models for legal research and case review.
AsyLex contains labeled data suited for two NLP tasks: (1) Entity extraction and (2) Legal Judgment Prediction.
## Dataset Details
AsyLex includes gold-standard human-labeled annotations for 24 legally relevant entity types curated with the help of legal experts, and 1,682 gold-standard labeled documents for the outcome of the case.
The dataset can be split in two sets:
- (1) a Case Covers set that consists of semi-structured data and displays meta-information (the first page of each case);
- (2) a Main Text set that contains the body of each case, in full text.
### Dataset Sources
The documents have been collected from the online services of the Canadian Legal Information Institute (CanLII).
## Uses
- **License:** cc-by-nc-sa-4.0
The dataset must be used for research purposes only. It must not be use for commercial purposes.
## Dataset Structure
This dataset contains the following files:
| Configuration | Files | Description |
| ------------- | ------------- | ------------- |
| raw_documents | cases_anonymized_txt_raw.tar.gz | contains the raw text from all documents, by case, with the corresponding case identifier |
| raw_sentences | all_sentences_anonymized.tar.xz | contains the raw text from all retrieved documents, split by sentences, with the corresponding case identifier |
| all_legal_entities| main_and_case_cover_all_entities_inferred.csv | contains the structured dataset, all extracted entities (one column per entity type), with the corresponding case identifier |
| casecover_legal_entities| case_cover/case_cover_anonymised_extracted_entities.csv | contains the structured dataset derived from the case covers only (one column per entity type), with the corresponding case identifier|
| casecover_entities_outcome | case_cover/case_cover_entities_and_decision_outcome.csv| same as above, with the addition of the decision outcome of the case |
| determination_sentences| determination_label_extracted_sentences.csv | contains all sentences that have been extracted with the Entity Type "determination". All sentences included here should therefore directly state the outcome of the decision, with the correspinding case identifier |
| outcome_classification | outcome_train_test| folder containing a train and test set for the task of outcome classificiation. Each set includes the case identifier and the decision outcome (0,1,2). The test set only contains gold-standard manually labeled data. |
| | manual_annotations | contains jsonl files of the manually collected annotations for the case cover and the main text|
In all files containing the decision outcome, 0 refers to a "reject", 1 to a "granted", and 2 to "uncertain".
Each configuration can be load by passing its name as a second parameter:
```
from datasets import load_dataset
outcome_classification_data = load_dataset("clairebarale/AsyLex", "outcome_classification")
raw_documents_data = load_dataset("clairebarale/AsyLex", "raw_documents")
```
#### Personal and Sensitive Information
All documents have been anonymized.
## Citation [optional]
**Papers:**
- **NLLP @EMNLP Publication:** tba
- **ACL Publication:**
```
@inproceedings{barale-etal-2023-automated,
title = "Automated Refugee Case Analysis: A {NLP} Pipeline for Supporting Legal Practitioners",
author = "Barale, Claire and
Rovatsos, Michael and
Bhuta, Nehal",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.187",
doi = "10.18653/v1/2023.findings-acl.187",
pages = "2992--3005",
}
```
## Dataset Card Contact
Please contact the authors of the papers. |
indiejoseph/yue-zh-translation | ---
language:
- yue
- zh
license: cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- translation
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: translation
struct:
- name: yue
dtype: string
- name: zh
dtype: string
splits:
- name: train
num_bytes: 16446012
num_examples: 169949
- name: test
num_bytes: 4107525
num_examples: 42361
download_size: 15755469
dataset_size: 20553537
---
This dataset is comprised of:
1. Crawled content that is machine translated from Cantonese to Simplified Chinese.
2. machine translated articlse from zh-yue.wikipedia.org
3. [botisan-ai/cantonese-mandarin-translations](https://huggingface.co/datasets/botisan-ai/cantonese-mandarin-translations)
4. [AlienKevin/LIHKG](https://huggingface.co/datasets/AlienKevin/LIHKG)
|
huunam/first | ---
license: mit
task_categories:
- text-classification
- text-generation
language:
- vi
tags:
- test
pretty_name: test
size_categories:
- 1K<n<10K
--- |
Suchinthana/Sinhala_OCR_Dataset_Synthetic | ---
license: mit
language:
- si
size_categories:
- 1K<n<10K
pretty_name: g
---
This is a synthetically generated dataset you can generate your own with the help of below codes in this github repo: https://github.com/suchinthana00/Synthetic_OCR_Dataset_Generator |
MichaelJH/Ryu-AI_dohyun-by5_untokenized.datadict | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 964778
num_examples: 3927
download_size: 440692
dataset_size: 964778
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mohamedemam/Arabic-samsum-dialogsum | ---
dataset_info:
features:
- name: index
dtype: int64
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 27913254
num_examples: 24813
download_size: 13968520
dataset_size: 27913254
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-2.0
task_categories:
- summarization
- conversational
language:
- ar
pretty_name: ar messum
size_categories:
- 10K<n<100K
---
# Dataset Card for "Arabic-samsum-dialogsum"
this dataset is comption between samsum and dialogsum dataset translated in arabic
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://arxiv.org/abs/1911.12237v2
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/1911.12237v2
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger convesations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person.
The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Arabic
## Dataset Structure
t
### Data Instances
The created dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people
The first instance in the training set:
{'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}
### Data Fields
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: unique id of an example.
### Data Splits
- train: 24732
## Dataset Creation
### Curation Rationale
In paper:
> In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typ- ically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assis- tant and a client buying petrol.
As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app.
### Source Data
#### Initial Data Collection and Normalization
In paper:
> We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora.
#### Who are the source language producers?
linguists
### Annotations
#### Annotation process
In paper:
> Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one ref- erence summary.
#### Who are the annotators?
language experts
### Personal and Sensitive Information
None, see above: Initial Data Collection and Normalization
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
non-commercial licence: CC BY-NC-ND 4.0
### Citation Information
```
@inproceedings{gliwa-etal-2019-samsum,
title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization",
author = "Gliwa, Bogdan and
Mochol, Iwona and
Biesek, Maciej and
Wawer, Aleksander",
booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-5409",
doi = "10.18653/v1/D19-5409",
pages = "70--79"
}
```
### Contributions
Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rvv-karma/English-Hinglish | ---
dataset_info:
features:
- name: en
dtype: string
- name: hi_en
dtype: string
splits:
- name: train
num_bytes: 12698467
num_examples: 132371
- name: test
num_bytes: 5431064
num_examples: 56731
download_size: 11695921
dataset_size: 18129531
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
multilinguality:
- multilingual
- translation
license: apache-2.0
task_categories:
- translation
- text-generation
language:
- en
- hi
pretty_name: English Hinglish
size_categories:
- 10K<n<100K
---
# English Hinglish
English to Hinglish Dataset processed from [findnitai/english-to-hinglish](https://huggingface.co/datasets/findnitai/english-to-hinglish).
Sources:
1. Hinglish TOP Dataset
2. CMU English Dog
3. HinGE
4. PHINC |
bond005/rulibrispeech | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 11165185580.744
num_examples: 54472
- name: test
num_bytes: 306649969.0
num_examples: 1352
- name: validation
num_bytes: 321842480.0
num_examples: 1400
download_size: 10689335725
dataset_size: 11793678029.744
---
# Dataset Card for "rulibrispeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
InfinireInnovative/HuggingFaceDataSet2 | ---
license: unlicense
---
|
TrainingDataPro/aggressive-behavior-video-classification | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- video-classification
tags:
- code
- legal
dataset_info:
features:
- name: name
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 422
num_examples: 11
download_size: 1387
dataset_size: 422
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Aggressive Behavior Video Classification
## WARNING: People in the videos exhibit aggressive behavior
The dataset with videos depicting people exhibiting **aggressive and non-aggressive behavior** is intended for classification purposes. It consists of a collection of video files that capture various individuals engaging in different activities and displaying distinct behavioral patterns and CSV-file with classification.
**Aggressive Behavior Video Classification Dataset** can have multiple applications, such as surveillance systems, security modules, or social behavior analysis platforms.
# 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=aggressive-behavior-video-classification) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
The dataset consists of:
- **files**: folder with videos with people exhibiting aggressive and non-aggressive behaviour (subfolders "aggressive" and "non_aggressive" respectively),
- **.csv file**: path of each video in the **"files"** folder and classification of the behavoir
# People Behavior Video Classification might be made in accordance with your requirements.
## **[TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=aggressive-behavior-video-classification)** 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** |
jaii123/training | ---
license: unknown
---
|
FrsECM/CelebAHQ_mask | ---
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
- image-to-image
pretty_name: CelebAHQ Mask Dataset
dataset_info:
features:
- name: image_id
dtype: string
- name: image
dtype: image
- name: annotation
dtype: image
splits:
- name: train
num_bytes: 2829644617.0
num_examples: 28500
- name: test
num_bytes: 150219016.0
num_examples: 1500
download_size: 2993732687
dataset_size: 2979863633.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
lmms-lab/Ferret-Bench | ---
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: image
dtype: image
- name: image_name
dtype: string
- name: category
dtype: string
- name: context
dtype: string
- name: gpt_answer
dtype: string
splits:
- name: test
num_bytes: 19750932.0
num_examples: 120
download_size: 11713676
dataset_size: 19750932.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [FerretBench](https://github.com/apple/ml-ferret). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{you2023ferret,
title={Ferret: Refer and Ground Anything Anywhere at Any Granularity},
author={You, Haoxuan and Zhang, Haotian and Gan, Zhe and Du, Xianzhi and Zhang, Bowen and Wang, Zirui and Cao, Liangliang and Chang, Shih-Fu and Yang, Yinfei},
journal={arXiv preprint arXiv:2310.07704},
year={2023}
}
```
|
vincentmin/eli5_rlhf_explainlikeim5 | ---
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: Reddit Explain Like I'm 5 for Reinforcement Learning Human Feedback
size_categories:
- 100K<n<1M
---
# ELI5 paired
This is a processed version of the [`eli5`](https://huggingface.co/datasets/eli5) dataset.
Compared to ["eli5_rlhf"](https://huggingface.co/datasets/vincentmin/eli5_rlhf), this dataset contains only QA pairs from the train split of the eli5 dataset and only from the subreddit explainlikeimfive.
Furthermore, the function
```
def get_question(example):
title = example["title"]
selftext = example["selftext"]
if selftext:
if selftext[-1] not in [".", "?", "!"]:
seperator = ". "
else:
seperator = " "
question = title + seperator + selftext
else:
question = title
example["question"] = question
return example
```
was applied to get the "question" column and the "title" and "selftext" columns were removed.
The dataset was created following very closely the steps in the [`stack-exchange-paired`](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) dataset.
The following steps were applied:
- The "question" field is a concatenation of "title" with "selftext".
- Create pairs `(response_j, response_k)` where j was rated better than k
- Sample at most 10 pairs per question
- Shuffle the dataset globally
This dataset is designed to be used for preference learning. The processing notebook is in the repository as well. |
open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA | ---
pretty_name: Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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 aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-19T15:36:58.669943](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA_public/blob/main/results_2023-11-19T15-36-58.669943.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\": 0.6304363255253682,\n\
\ \"acc_stderr\": 0.03221802645295953,\n \"acc_norm\": 0.6394242549229363,\n\
\ \"acc_norm_stderr\": 0.03291185339071779,\n \"mc1\": 0.3108935128518972,\n\
\ \"mc1_stderr\": 0.016203316673559693,\n \"mc2\": 0.45750130636610203,\n\
\ \"mc2_stderr\": 0.014435953658631701,\n \"em\": 0.005557885906040268,\n\
\ \"em_stderr\": 0.0007613497667018497,\n \"f1\": 0.06505977348993297,\n\
\ \"f1_stderr\": 0.0015006861389720051\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5793515358361775,\n \"acc_stderr\": 0.014426211252508401,\n\
\ \"acc_norm\": 0.6126279863481229,\n \"acc_norm_stderr\": 0.014235872487909869\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.641804421429994,\n\
\ \"acc_stderr\": 0.004784901248558711,\n \"acc_norm\": 0.8452499502091216,\n\
\ \"acc_norm_stderr\": 0.003609271000593054\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\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\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\
acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6011560693641619,\n\
\ \"acc_stderr\": 0.0373362665538351,\n \"acc_norm\": 0.6011560693641619,\n\
\ \"acc_norm_stderr\": 0.0373362665538351\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\
acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7483870967741936,\n\
\ \"acc_stderr\": 0.024685979286239963,\n \"acc_norm\": 0.7483870967741936,\n\
\ \"acc_norm_stderr\": 0.024685979286239963\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \
\ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.031566630992154156,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.031566630992154156\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8146788990825689,\n \"acc_stderr\": 0.016659279700295845,\n \"\
acc_norm\": 0.8146788990825689,\n \"acc_norm_stderr\": 0.016659279700295845\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849316,\n \"\
acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849316\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159256,\n \
\ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159256\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\
\ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.03157065078911901,\n\
\ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.03157065078911901\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8160919540229885,\n\
\ \"acc_stderr\": 0.013853724170922526,\n \"acc_norm\": 0.8160919540229885,\n\
\ \"acc_norm_stderr\": 0.013853724170922526\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n\
\ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31620111731843575,\n\
\ \"acc_stderr\": 0.015551673652172542,\n \"acc_norm\": 0.31620111731843575,\n\
\ \"acc_norm_stderr\": 0.015551673652172542\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7712418300653595,\n \"acc_stderr\": 0.024051029739912258,\n\
\ \"acc_norm\": 0.7712418300653595,\n \"acc_norm_stderr\": 0.024051029739912258\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \"\
acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45632333767926986,\n\
\ \"acc_stderr\": 0.012721420501462547,\n \"acc_norm\": 0.45632333767926986,\n\
\ \"acc_norm_stderr\": 0.012721420501462547\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6433823529411765,\n \"acc_stderr\": 0.029097209568411955,\n\
\ \"acc_norm\": 0.6433823529411765,\n \"acc_norm_stderr\": 0.029097209568411955\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507215,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507215\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\
\ \"acc_stderr\": 0.046313813194254656,\n \"acc_norm\": 0.6272727272727273,\n\
\ \"acc_norm_stderr\": 0.046313813194254656\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233268,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233268\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3108935128518972,\n\
\ \"mc1_stderr\": 0.016203316673559693,\n \"mc2\": 0.45750130636610203,\n\
\ \"mc2_stderr\": 0.014435953658631701\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090254\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.005557885906040268,\n \
\ \"em_stderr\": 0.0007613497667018497,\n \"f1\": 0.06505977348993297,\n\
\ \"f1_stderr\": 0.0015006861389720051\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.18119787717968158,\n \"acc_stderr\": 0.010609827611527355\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA
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_11_19T15_36_58.669943
path:
- '**/details_harness|arc:challenge|25_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|drop|3_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|gsm8k|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hellaswag|10_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-36-58.669943.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-36-58.669943.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-19T15-36-58.669943.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- '**/details_harness|winogrande|5_2023-11-19T15-36-58.669943.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-19T15-36-58.669943.parquet'
- config_name: results
data_files:
- split: 2023_11_19T15_36_58.669943
path:
- results_2023-11-19T15-36-58.669943.parquet
- split: latest
path:
- results_2023-11-19T15-36-58.669943.parquet
---
# Dataset Card for Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA
- **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 [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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 aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-19T15:36:58.669943](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-DPO-7B-QLoRA_public/blob/main/results_2023-11-19T15-36-58.669943.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": 0.6304363255253682,
"acc_stderr": 0.03221802645295953,
"acc_norm": 0.6394242549229363,
"acc_norm_stderr": 0.03291185339071779,
"mc1": 0.3108935128518972,
"mc1_stderr": 0.016203316673559693,
"mc2": 0.45750130636610203,
"mc2_stderr": 0.014435953658631701,
"em": 0.005557885906040268,
"em_stderr": 0.0007613497667018497,
"f1": 0.06505977348993297,
"f1_stderr": 0.0015006861389720051
},
"harness|arc:challenge|25": {
"acc": 0.5793515358361775,
"acc_stderr": 0.014426211252508401,
"acc_norm": 0.6126279863481229,
"acc_norm_stderr": 0.014235872487909869
},
"harness|hellaswag|10": {
"acc": 0.641804421429994,
"acc_stderr": 0.004784901248558711,
"acc_norm": 0.8452499502091216,
"acc_norm_stderr": 0.003609271000593054
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.038234289699266046,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.038234289699266046
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.02794321998933714,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.02794321998933714
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6011560693641619,
"acc_stderr": 0.0373362665538351,
"acc_norm": 0.6011560693641619,
"acc_norm_stderr": 0.0373362665538351
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.03232146916224468,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4074074074074074,
"acc_stderr": 0.02530590624159063,
"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.02530590624159063
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.04375888492727061,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.04375888492727061
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7483870967741936,
"acc_stderr": 0.024685979286239963,
"acc_norm": 0.7483870967741936,
"acc_norm_stderr": 0.024685979286239963
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7727272727272727,
"acc_stderr": 0.029857515673386414,
"acc_norm": 0.7727272727272727,
"acc_norm_stderr": 0.029857515673386414
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.02381447708659355,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.02381447708659355
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6333333333333333,
"acc_stderr": 0.02443301646605246,
"acc_norm": 0.6333333333333333,
"acc_norm_stderr": 0.02443301646605246
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.31851851851851853,
"acc_stderr": 0.02840653309060846,
"acc_norm": 0.31851851851851853,
"acc_norm_stderr": 0.02840653309060846
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6176470588235294,
"acc_stderr": 0.031566630992154156,
"acc_norm": 0.6176470588235294,
"acc_norm_stderr": 0.031566630992154156
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8146788990825689,
"acc_stderr": 0.016659279700295845,
"acc_norm": 0.8146788990825689,
"acc_norm_stderr": 0.016659279700295845
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7843137254901961,
"acc_stderr": 0.028867431449849316,
"acc_norm": 0.7843137254901961,
"acc_norm_stderr": 0.028867431449849316
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7637130801687764,
"acc_stderr": 0.027652153144159256,
"acc_norm": 0.7637130801687764,
"acc_norm_stderr": 0.027652153144159256
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.816793893129771,
"acc_stderr": 0.03392770926494733,
"acc_norm": 0.816793893129771,
"acc_norm_stderr": 0.03392770926494733
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228733,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228733
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7975460122699386,
"acc_stderr": 0.03157065078911901,
"acc_norm": 0.7975460122699386,
"acc_norm_stderr": 0.03157065078911901
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5357142857142857,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.5357142857142857,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.8349514563106796,
"acc_stderr": 0.036756688322331886,
"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.036756688322331886
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.020930193185179333,
"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.020930193185179333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8160919540229885,
"acc_stderr": 0.013853724170922526,
"acc_norm": 0.8160919540229885,
"acc_norm_stderr": 0.013853724170922526
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7138728323699421,
"acc_stderr": 0.02433214677913413,
"acc_norm": 0.7138728323699421,
"acc_norm_stderr": 0.02433214677913413
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.31620111731843575,
"acc_stderr": 0.015551673652172542,
"acc_norm": 0.31620111731843575,
"acc_norm_stderr": 0.015551673652172542
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7712418300653595,
"acc_stderr": 0.024051029739912258,
"acc_norm": 0.7712418300653595,
"acc_norm_stderr": 0.024051029739912258
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7009646302250804,
"acc_stderr": 0.02600330111788514,
"acc_norm": 0.7009646302250804,
"acc_norm_stderr": 0.02600330111788514
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.75,
"acc_stderr": 0.02409347123262133,
"acc_norm": 0.75,
"acc_norm_stderr": 0.02409347123262133
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.49645390070921985,
"acc_stderr": 0.02982674915328092,
"acc_norm": 0.49645390070921985,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.45632333767926986,
"acc_stderr": 0.012721420501462547,
"acc_norm": 0.45632333767926986,
"acc_norm_stderr": 0.012721420501462547
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6433823529411765,
"acc_stderr": 0.029097209568411955,
"acc_norm": 0.6433823529411765,
"acc_norm_stderr": 0.029097209568411955
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
"acc_stderr": 0.018975427920507215,
"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.018975427920507215
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6272727272727273,
"acc_stderr": 0.046313813194254656,
"acc_norm": 0.6272727272727273,
"acc_norm_stderr": 0.046313813194254656
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
"acc_stderr": 0.026508590656233268,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.026508590656233268
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.02991312723236804,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.02991312723236804
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3108935128518972,
"mc1_stderr": 0.016203316673559693,
"mc2": 0.45750130636610203,
"mc2_stderr": 0.014435953658631701
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.011524466954090254
},
"harness|drop|3": {
"em": 0.005557885906040268,
"em_stderr": 0.0007613497667018497,
"f1": 0.06505977348993297,
"f1_stderr": 0.0015006861389720051
},
"harness|gsm8k|5": {
"acc": 0.18119787717968158,
"acc_stderr": 0.010609827611527355
}
}
```
### 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] |
NetherlandsForensicInstitute/s2orc-citation-pairs-translated-nl | ---
license: odc-by
size_categories:
- 10M<n<100M
task_categories:
- sentence-similarity
language:
- nl
---
This is a Dutch version of the [S2ORC: The Semantic Scholar Open Research Corpus](https://allenai.org/data/s2orc). Which we have auto-translated from English into Dutch using Meta's [No Language Left Behind](https://ai.facebook.com/research/no-language-left-behind/) model, specifically the [huggingface implementation](https://huggingface.co/facebook/nllb-200-distilled-600M). |
hails/agieval-jec-qa-ca | ---
dataset_info:
features:
- name: query
dtype: string
- name: choices
sequence: string
- name: gold
sequence: int64
splits:
- name: test
num_bytes: 1027747
num_examples: 999
download_size: 590964
dataset_size: 1027747
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
language:
- zh
---
# Dataset Card for "agieval-jec-qa-ca"
Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.
This dataset contains the contents of the JEC-QA-CA subtask of AGIEval, as accessed in https://github.com/ruixiangcui/AGIEval/commit/5c77d073fda993f1652eaae3cf5d04cc5fd21d40 .
Citation:
```
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:
```
@inproceedings{ling-etal-2017-program,
title = "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems",
author = "Ling, Wang and
Yogatama, Dani and
Dyer, Chris and
Blunsom, Phil",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1015",
doi = "10.18653/v1/P17-1015",
pages = "158--167",
abstract = "Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.",
}
@inproceedings{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
@inproceedings{Liu2020LogiQAAC,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
booktitle={International Joint Conference on Artificial Intelligence},
year={2020}
}
@inproceedings{zhong2019jec,
title={JEC-QA: A Legal-Domain Question Answering Dataset},
author={Zhong, Haoxi and Xiao, Chaojun and Tu, Cunchao and Zhang, Tianyang and Liu, Zhiyuan and Sun, Maosong},
booktitle={Proceedings of AAAI},
year={2020},
}
@article{Wang2021FromLT,
title={From LSAT: The Progress and Challenges of Complex Reasoning},
author={Siyuan Wang and Zhongkun Liu and Wanjun Zhong and Ming Zhou and Zhongyu Wei and Zhumin Chen and Nan Duan},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
year={2021},
volume={30},
pages={2201-2216}
}
``` |
Jeffzera/Veia | ---
license: openrail
---
|
biu-nlp/qa_adj | ---
license: cc-by-4.0
---
|
CyberHarem/yura_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of yura/由良/由良 (Kantai Collection)
This is the dataset of yura/由良/由良 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `long_hair, pink_hair, very_long_hair, ponytail, ribbon, hair_ribbon, breasts, yellow_eyes, brown_eyes`, which are pruned in this dataset.
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)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 536.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yura_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 330.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yura_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1165 | 692.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yura_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 485.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yura_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1165 | 939.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yura_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/yura_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, hair_flaps, serafuku, simple_background, upper_body, white_background, black_ribbon, green_eyes, green_sailor_collar, grey_sailor_collar, looking_at_viewer, open_mouth, short_sleeves, smile, solo, blush, sidelocks |
| 1 | 5 |  |  |  |  |  | 1girl, black_jacket, grey_sailor_collar, hair_flaps, neck_ribbon, red_ribbon, serafuku, short_sleeves, solo, upper_body, looking_at_viewer, simple_background, white_background, smile |
| 2 | 23 |  |  |  |  |  | 1girl, black_jacket, grey_sailor_collar, grey_skirt, hair_flaps, pleated_skirt, serafuku, short_sleeves, solo, black_gloves, neck_ribbon, partially_fingerless_gloves, red_ribbon, simple_background, looking_at_viewer, white_background, cowboy_shot, smile |
| 3 | 6 |  |  |  |  |  | 1girl, hair_ornament, looking_at_viewer, pleated_skirt, serafuku, smile, solo, green_eyes, side_ponytail, blush, turret |
| 4 | 5 |  |  |  |  |  | 1girl, green_eyes, hair_ornament, looking_at_viewer, pleated_skirt, serafuku, smile, solo, knee_boots, full_body |
| 5 | 19 |  |  |  |  |  | 1girl, alternate_costume, hair_flaps, looking_at_viewer, solo, simple_background, smile, white_sweater, black_jacket, twitter_username, white_background, one-hour_drawing_challenge, blush, jacket_on_shoulders, turtleneck, black_ribbon, long_sleeves, pleated_skirt, upper_body, black_pantyhose, coat, large_breasts |
| 6 | 20 |  |  |  |  |  | 1girl, solo, yukata, hair_flaps, looking_at_viewer, obi, alternate_costume, floral_print, smile, blush, simple_background, upper_body, white_background, white_kimono, wide_sleeves, open_mouth |
| 7 | 12 |  |  |  |  |  | 1girl, black_shirt, solo, hair_flaps, black_ribbon, short_sleeves, looking_at_viewer, official_alternate_costume, upper_body, collarbone, medium_breasts, simple_background, smile, swimsuit |
| 8 | 5 |  |  |  |  |  | 1girl, hair_flaps, simple_background, solo, white_background, white_bikini, black_shirt, blush, cleavage, cowboy_shot, looking_at_viewer, navel, shirt_lift, large_breasts, lifted_by_self, open_mouth, skirt, smile, undressing, black_ribbon, blue_sarong, medium_breasts, official_alternate_costume, short_sleeves, twitter_username |
| 9 | 8 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, medium_breasts, solo, white_bikini, hair_flaps, navel, sitting, sarong, cowboy_shot |
| 10 | 5 |  |  |  |  |  | 1girl, hair_flaps, one-hour_drawing_challenge, simple_background, solo, white_background, dated, medium_breasts, twitter_username, white_bikini, cleavage, cowboy_shot, large_breasts, looking_at_viewer, black_ribbon, jacket, navel, official_alternate_costume, sitting, upper_body |
| 11 | 7 |  |  |  |  |  | 1girl, solo, hair_flaps, navel, panties, simple_background, underwear_only, bra, looking_at_viewer, medium_breasts, armpits, cleavage, cowboy_shot, white_background |
| 12 | 9 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, hair_flaps, playboy_bunny, rabbit_ears, solo, strapless_leotard, wrist_cuffs, alternate_costume, black_leotard, looking_at_viewer, medium_breasts, bowtie, cowboy_shot, black_pantyhose, cleavage, simple_background, fishnet_pantyhose, rabbit_tail, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hair_flaps | serafuku | simple_background | upper_body | white_background | black_ribbon | green_eyes | green_sailor_collar | grey_sailor_collar | looking_at_viewer | open_mouth | short_sleeves | smile | solo | blush | sidelocks | black_jacket | neck_ribbon | red_ribbon | grey_skirt | pleated_skirt | black_gloves | partially_fingerless_gloves | cowboy_shot | hair_ornament | side_ponytail | turret | knee_boots | full_body | alternate_costume | white_sweater | twitter_username | one-hour_drawing_challenge | jacket_on_shoulders | turtleneck | long_sleeves | black_pantyhose | coat | large_breasts | yukata | obi | floral_print | white_kimono | wide_sleeves | black_shirt | official_alternate_costume | collarbone | medium_breasts | swimsuit | white_bikini | cleavage | navel | shirt_lift | lifted_by_self | skirt | undressing | blue_sarong | sitting | sarong | dated | jacket | panties | underwear_only | bra | armpits | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | black_leotard | bowtie | fishnet_pantyhose | rabbit_tail |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------------|:-----------|:--------------------|:-------------|:-------------------|:---------------|:-------------|:----------------------|:---------------------|:--------------------|:-------------|:----------------|:--------|:-------|:--------|:------------|:---------------|:--------------|:-------------|:-------------|:----------------|:---------------|:------------------------------|:--------------|:----------------|:----------------|:---------|:-------------|:------------|:--------------------|:----------------|:-------------------|:-----------------------------|:----------------------|:-------------|:---------------|:------------------|:-------|:----------------|:---------|:------|:---------------|:---------------|:---------------|:--------------|:-----------------------------|:-------------|:-----------------|:-----------|:---------------|:-----------|:--------|:-------------|:-----------------|:--------|:-------------|:--------------|:----------|:---------|:--------|:---------|:----------|:-----------------|:------|:----------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:----------------|:---------|:--------------------|:--------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | | | | X | X | | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 23 |  |  |  |  |  | X | X | X | X | | X | | | | X | X | | X | X | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | | | | | X | | | X | | | X | X | X | | | | | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | | | | | X | | | X | | | X | X | | | | | | | X | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 19 |  |  |  |  |  | X | X | | X | X | X | X | | | | X | | | X | X | X | | X | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 20 |  |  |  |  |  | X | X | | X | X | X | | | | | X | X | | X | X | X | | | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 12 |  |  |  |  |  | X | X | | X | X | | X | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | X | | X | | X | X | | | | X | X | X | X | X | X | | | | | | | | | X | | | | | | | | X | | | | | | | X | | | | | | X | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 9 | 8 |  |  |  |  |  | X | X | | | | | | | | | X | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | | | | | | X | X | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | | X | X | X | X | | | | X | | | | X | | | | | | | | | | X | | | | | | | | X | X | | | | | | X | | | | | | | X | | X | | X | X | X | | | | | | X | | X | X | | | | | | | | | | | | | | |
| 11 | 7 |  |  |  |  |  | X | X | | X | | X | | | | | X | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | | | | | | | | | | X | X | X | X | | | | | | | | | | |
| 12 | 9 |  |  |  |  |  | X | X | | X | | X | | | | | X | | | | X | | | | | | | | | | X | | | | | | X | | | | | | | X | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
semeru/code-code-CodeCompletion-TokenLevel-Python | ---
license: mit
Programminglanguage: "python"
version: "python3"
Date: "From paper [Probabilistic for Code with Decision trees](https://files.sri.inf.ethz.ch/website/papers/oopsla16-dt.pdf)(2016- paper release date)"
Contaminated: "Very Likely"
Size: "Standard Tokenizer (TreeSitter)"
---
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/CodeCompletion-token/dataset/py150 in Semeru
# CodeXGLUE -- Code Completion (token level)
**Update 2021.07.30:** We update the code completion dataset with literals normalized to avoid sensitive information.
Here is the introduction and pipeline for token level code completion task.
## Task Definition
Predict next code token given context of previous tokens. Models are evaluated by token level accuracy.
Code completion is a one of the most widely used features in software development through IDEs. An effective code completion tool could improve software developers' productivity. We provide code completion evaluation tasks in two granularities -- token level and line level. Here we introduce token level code completion. Token level task is analogous to language modeling. Models should have be able to predict the next token in arbitary types.
## Dataset
The dataset is in python.
### Dependency
- python 3.7
### Github Java Corpus
We use java corpus dataset mined by Allamanis and Sutton, in their MSR 2013 paper [Mining Source Code Repositories at Massive Scale using Language Modeling](https://homepages.inf.ed.ac.uk/csutton/publications/msr2013.pdf). We follow the same split and preprocessing in Karampatsis's ICSE 2020 paper [Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code](http://homepages.inf.ed.ac.uk/s1467463/documents/icse20-main-1325.pdf).
### Data Format
Code corpus are saved in txt format files. one line is a tokenized code snippets:
```
<s> from __future__ import unicode_literals <EOL> from django . db import models , migrations <EOL> class Migration ( migrations . Migration ) : <EOL> dependencies = [ <EOL> ] <EOL> operations = [ <EOL> migrations . CreateModel ( <EOL> name = '<STR_LIT>' , <EOL> fields = [ <EOL> ( '<STR_LIT:id>' , models . AutoField ( verbose_name = '<STR_LIT>' , serialize = False , auto_created = True , primary_key = True ) ) , <EOL> ( '<STR_LIT:name>' , models . CharField ( help_text = b'<STR_LIT>' , max_length = <NUM_LIT> ) ) , <EOL> ( '<STR_LIT:image>' , models . ImageField ( help_text = b'<STR_LIT>' , null = True , upload_to = b'<STR_LIT>' , blank = True ) ) , <EOL> ] , <EOL> options = { <EOL> '<STR_LIT>' : ( '<STR_LIT:name>' , ) , <EOL> '<STR_LIT>' : '<STR_LIT>' , <EOL> } , <EOL> bases = ( models . Model , ) , <EOL> ) , <EOL> ] </s>
```
### Data Statistics
Data statistics of py150 dataset are shown in the below table, note that there doesn't exist dev set in the origin py150 dataset, we select 5,000 files in the original train set as dev set.
| Data Split | #Files | #Tokens |
| ----------- | :---------: | :---------: |
| Train | 95,000 | 72.1M |
| Dev | 5,000 | 4.4M |
| Test | 50,000 | 37.3M |
|
grammarly/coedit | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: coedit
size_categories:
- 10K<n<100K
---
# Dataset Card for CoEdIT: Text Editing via Instruction Tuning
## Paper: [CoEdIT: Text Editing by Task-Specific Instruction Tuning](https://arxiv.org/abs/2305.09857)
## Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
## Project Repo: [https://github.com/vipulraheja/coedit](https://github.com/vipulraheja/coedit)
## Dataset Summary
This is the dataset that was used to train the CoEdIT text editing models. Full details of the dataset can be found in our paper.
# Dataset Structure
The dataset is in JSON format.
## Data Instances
```
{
'_id': 1,
'task': "gec",
'src': "Improve the grammaticality: As the number of people grows, the need of habitable environment is unquestionably essential.",
'tgt': "As the number of people grows, the need for a habitable environment is unquestionably increasing."
}
```
## Data Fields
* `_id`:
* `task`: Text editing task for this instance
* `src`: input text (formatted as `instruction: input_text`)
* `tgt`: output text
## Considerations for Using the Data
Please note that this dataset contains 69k instances (as opposed to the 82k instances we used in the paper). This is because this public release includes only the instances that were acquired and curated from publicly available datasets. Specifically, it is missing roughly 13k instances in training and 1.5k instances in validation data from Simplification and Formality Transfer tasks due to licensing restrictions.
# Citation
```
@article{raheja2023coedit,
title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
year={2023},
eprint={2305.09857},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
soopy/cai | ---
task_categories:
- text-generation
- text-classification
- text2text-generation
language:
- en
size_categories:
- n<1K
license: mit
tags:
- not-for-all-audiences
pretty_name: captionai
---
Collection of differnt NSFW Captions, the date is mostly hand written and took quite some time to gather.
If you find caption texts in here that are from you and you want them to be removed please contact me, and i will do my best to quickly remove them.
TODOS:
- Find more Data to add to train/validation: Goal is around 500 Training and 50 Validation/Testing Captions
- Train a text classification model with the dataset
- Train a text2text or text generation model to generate text in the style of the captions in this dataset
- Optionally Pass in labels/tags/classes to influence the content of the text |
huggingartists/chester-bennington | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/chester-bennington"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.519451 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/3853f38429e3cd0278c2b5b6307b9e92.752x752x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/chester-bennington">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Chester Bennington</div>
<a href="https://genius.com/artists/chester-bennington">
<div style="text-align: center; font-size: 14px;">@chester-bennington</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/chester-bennington).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/chester-bennington")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|391| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/chester-bennington")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
tr416/dataset_20231007_024410 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 73926
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_024410"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sam1120/safety-utcustom-terrain-jackal-full-391 | ---
dataset_info:
features:
- name: name
dtype: string
- name: pixel_values
dtype: image
- name: labels
dtype: image
splits:
- name: train
num_bytes: 1086671777.0
num_examples: 391
download_size: 315061316
dataset_size: 1086671777.0
---
# Dataset Card for "terrain-jackal-full-391"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ola13/wikipedia_citations | ---
dataset_info:
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dtype: string
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dtype: string
- name: wiki_url
dtype: string
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---
# Dataset Card for "wikipedia_citations"
Sample usage:
```
simple = load_dataset("ola13/wikipedia_citations", split="train", language="simple", date="20230301")
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Multimodal-Fatima/Hatefulmemes_train | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: label
dtype:
class_label:
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'0': not-hateful
'1': hateful
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dtype: int64
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sequence: string
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splits:
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num_examples: 8500
download_size: 3059695187
dataset_size: 3066249406.0
---
# Dataset Card for "Hatefulmemes_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Davlan/nollysenti | ---
license: afl-3.0
---
|
efederici/mt_nap_it | ---
language:
- it
license:
- unknown
size_categories:
- unknown
task_categories:
- translation
task_ids: []
pretty_name: mt_nap_it
tags:
- conditional-text-generation
---
# Dataset Card for mt_en_it
## Table of Contents
- [Dataset Card for mt_en_it](#dataset-card-for-mt-en-it)
- [Table of Contents](#table-of-contents)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
### Dataset Summary
This dataset comprises traditional Neapolitan songs from [napoligrafia](https://www.napoligrafia.it) translated into Italian.
### Languages
- italian-to-neapolitan
### Data Instances
A sample from the dataset.
```python
{
'url': "url",
'napoletano': "o, quacche ghiuorno, 'a frennesia mme piglia",
'italiano': "o, qualche giorno, la rabbia mi prende"
}
```
The text is provided without further preprocessing or tokenization.
### Data Fields
- `url`: source URL.
- `napoletano`: Neapolitan text.
- `italiano`: Italian text.
### Dataset Creation
The dataset was created by scraping [napoligrafia](https://www.napoligrafia.it) songs. |
Benjaminwfriedman/dlr_roomfurniture_captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3964611.0
num_examples: 199
download_size: 3226653
dataset_size: 3964611.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Pipper/sol_processed_data | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 12740019180
num_examples: 3814377
download_size: 1991408875
dataset_size: 12740019180
---
# Dataset Card for "sol_processed_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
swap-uniba/arc_challenge_ita | ---
task_categories:
- question-answering
- text-generation
language:
- it
tags:
- llm
- evaluation
- llamantino
- italian
pretty_name: Arc-c dataset Italian Version
size_categories:
- 1K<n<10K
---
# Italian version of the Arc Challenge dataset (ARC-c)
The dataset has been automatically translate by using [Argos Translate](https://github.com/argosopentech/argos-translate) v. 1.9.1
### Citation Information
```
@misc{basile2023llamantino,
title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language},
author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
year={2023},
eprint={2312.09993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{Clark2018ThinkYH,
title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
journal={ArXiv},
year={2018},
volume={abs/1803.05457}
}
```
# Dataset Description
The ARC dataset consists of **7,787 science exam questions** drawn from a variety of sources, including science questions provided under license by a research partner affiliated with AI2. These are text-only, English language exam questions that span several grade levels as indicated in the files. Each question has a
**multiple choice structure** (typically 4 answer options).
The questions are sorted into a Challenge Set of 2,590 “hard” questions (those that both a retrieval and a co-occurrence method fail to answer correctly) and an Easy Set of 5,197 questions.
Official website: [https://allenai.org/data/arc](https://allenai.org/data/arc)
|
HDanh/RealFakeDB_small | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': fake
'1': real
splits:
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num_examples: 98163
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num_examples: 5168
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num_bytes: 592123012.48
num_examples: 5440
download_size: 13085799986
dataset_size: 12048285785.102999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: mit
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
---
#This dataset is collected from ImageReward for the fake class and COCO for the real class |
mikhail-panzo/processed_malay_dataset_small | ---
dataset_info:
features:
- name: speaker_embeddings
sequence: float32
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sequence: int32
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sequence:
sequence: float32
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
silk-road/ChatHaruhi-Waifu | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- zh
size_categories:
- n<1K
---
本数据集是为了部分不适合直接显示的角色进行hugging face存储。text部分做了简单的编码加密
使用方法
载入函数
```python
from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("silk-road/Chat-Haruhi_qwen_1_8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("silk-road/Chat-Haruhi_qwen_1_8", trust_remote_code=True).half().cuda()
model = model.eval()
```
具体看https://github.com/LC1332/Chat-Haruhi-Suzumiya/blob/main/notebook/ChatHaruhi_x_Qwen1_8B.ipynb 这个notebook
```python
from ChatHaruhi import ChatHaruhi
chatbot = ChatHaruhi( role_from_hf = 'silk-road/ChatHaruhi-Waifu/女贤者', max_len_story = 1000 )
prompt = chatbot.generate_prompt(role='男子', text = '你已经不能动了')
response, _ = model.chat(tokenizer, prompt, history=[])
print(response)
chatbot.append_response(response)
#模型输出:
#女贤者:「啊啊啊，不可以！」
```
项目链接https://github.com/LC1332/Chat-Haruhi-Suzumiya
欢迎提供新的语料 |
liuyanchen1015/MULTI_VALUE_cola_non_coordinated_obj_subj | ---
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download_size: 66221
dataset_size: 132859
---
# Dataset Card for "MULTI_VALUE_cola_non_coordinated_obj_subj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heliosprime/twitter_dataset_1713034187 | ---
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713034187"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vilm/code-textbooks | ---
dataset_info:
features:
- name: max_stars_repo_path
dtype: large_string
- name: max_stars_repo_name
dtype: large_string
- name: id
dtype: large_string
- name: language
dtype: large_string
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dtype: string
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download_size: 894488166
dataset_size: 2407352255
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Code Textbook
200K+ Synthetic Textbook Samples generated with various Open-Source LLMs including **Nous Hermes Mixtral 8x7B, OpenHermes-2.5-Mistral, OpenChat and DeepSeek-Coder**. |
Gustrd/dolly-15k-hippo-translated-pt-12k | ---
license: cc-by-sa-3.0
language:
- pt
size_categories:
- 10K<n<100K
---
*Summary*
databricks-dolly-15k ( https://huggingface.co/datasets/databricks/databricks-dolly-15k/ ) is an open source dataset of instruction-following records generated by thousands of Databricks employees in several of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA, and summarization.
This translation into Portuguese was executed utilizing a technique from the HIPPO benchmark. By employing both LibreTranslate and MarianMT, a medium quality result was achieved, reflecting a carefully balanced approach. Further details and the underlying methodology can be found at the HIPPO GitHub repository ( https://github.com/gustrd/hippo ). It's an advance version of Gustrd/dolly-15k-libretranslate-pt ( https://huggingface.co/datasets/Gustrd/dolly-15k-libretranslate-pt ).
This dataset can be used for any purpose, whether academic or commercial, under the terms of the Creative Commons Attribution-ShareAlike 3.0 Unported License.
Supported Tasks:
Training LLMs
Synthetic Data Generation
Data Augmentation
Languages: Portuguese
Version: 1.0 |
nthakur/miracl-raft-eval-instruct | ---
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---
|
inverse-scaling/NeQA | ---
language:
- en
size_categories:
- 10K<n<100K
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: NeQA - Can Large Language Models Understand Negation in Multi-choice Questions?
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
train-eval-index:
- config: inverse-scaling--NeQA
task: text-generation
task_id: text_zero_shot_classification
splits:
eval_split: train
col_mapping:
prompt: text
classes: classes
answer_index: target
---
## NeQA: Can Large Language Models Understand Negation in Multi-choice Questions? (Zhengping Zhou and Yuhui Zhang)
### General description
This task takes an existing multiple-choice dataset and negates a part of each question to see if language models are sensitive to negation. The authors find that smaller language models display approximately random performance whereas the performance of larger models become significantly worse than random.
Language models failing to follow instructions in the prompt could be a serious issue that only becomes apparent on a task once models are sufficiently capable to perform non-randomly on the task.
### Example
The following are multiple choice questions (with answers) about common sense.
Question: If a cat has a body temp that is below average, it isn't in
A. danger
B. safe ranges
Answer:
(where the model should choose B.)
## Submission details
### Task description
Negation is a common linguistic phenomenon that can completely alter the semantics of a sentence by changing just a few words.
This task evaluates whether language models can understand negation, which is an important step towards true natural language understanding.
Specifically, we focus on negation in open-book multi-choice questions, considering its wide range of applications and the simplicity of evaluation.
We collect a multi-choice question answering dataset, NeQA, that includes questions with negations.
When negation is presented in the question, the original correct answer becomes wrong, and the wrong answer becomes correct.
We use the accuracy metric to examine whether the model can understand negation in the questions and select the correct answer given the presence of negation.
We observe a clear inverse scaling trend on GPT-3, demonstrating that larger language models can answer more complex questions but fail at the last step to understanding negation.
### Dataset generation procedure
The dataset is created by applying rules to transform questions in a publicly available multiple-choice question answering dataset named OpenBookQA. We use a simple rule by filtering questions containing "is" and adding "not" after it. For each question, we sample an incorrect answer as the correct answer and treat the correct answer as the incorrect answer. We randomly sample 300 questions and balance the label distributions (50% label as "A" and 50% label as "B" since there are two choices for each question)..
### Why do you expect to see inverse scaling?
For open-book question answering, larger language models usually achieve better accuracy because more factual and commonsense knowledge is stored in the model parameters and can be used as a knowledge base to answer these questions without context.
A higher accuracy rate means a lower chance of choosing the wrong answer. Can we change the wrong answer to the correct one? A simple solution is to negate the original question. If the model cannot understand negation, it will still predict the same answer and, therefore, will exhibit an inverse scaling trend.
We expect that the model cannot understand negation because negation introduces only a small perturbation to the model input. It is difficult for the model to understand that this small perturbation leads to completely different semantics.
### Why is the task important?
This task is important because it demonstrates that current language models cannot understand negation, a very common linguistic phenomenon and a real-world challenge to natural language understanding.
Why is the task novel or surprising? (1+ sentences)
To the best of our knowledge, no prior work shows that negation can cause inverse scaling. This finding should be surprising to the community, as large language models show an incredible variety of emergent capabilities, but still fail to understand negation, which is a fundamental concept in language.
## Results
[Inverse Scaling Prize: Round 1 Winners announcement](https://www.alignmentforum.org/posts/iznohbCPFkeB9kAJL/inverse-scaling-prize-round-1-winners#Zhengping_Zhou_and_Yuhui_Zhang__for_NeQA__Can_Large_Language_Models_Understand_Negation_in_Multi_choice_Questions_)
|
vollerei-id/stolen-aesthetic | ---
dataset_info:
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dtype: image
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dtype: float64
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Madjogger/weather_region | ---
license: apache-2.0
---
|
Seenka/banners_canal-america | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': dudoso
'1': none
'2': videograph
'3': videograph_dudoso
'4': zocalo
'5': zocalo_dudoso
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sequence: float32
splits:
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num_examples: 193
download_size: 45367438
dataset_size: 45206998.0
---
# Dataset Card for "banners_canal-america"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
florentgbelidji/ncbi_extracted_running | ---
dataset_info:
features:
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download_size: 10192770
dataset_size: 19228492
---
# Dataset Card for "ncbi_extracted_running"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hausa_voa_topics | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ha
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: Hausa Voa News Topic Classification Dataset (HausaVoaTopics)
dataset_info:
features:
- name: news_title
dtype: string
- name: label
dtype:
class_label:
names:
'0': Africa
'1': Health
'2': Nigeria
'3': Politics
'4': World
splits:
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num_bytes: 144932
num_examples: 2045
- name: validation
num_bytes: 20565
num_examples: 290
- name: test
num_bytes: 41195
num_examples: 582
download_size: 195824
dataset_size: 206692
---
# Dataset Card for Hausa VOA News Topic Classification dataset (hausa_voa_topics)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** -
- **Repository:** https://github.com/uds-lsv/transfer-distant-transformer-african
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Leaderboard:** -
- **Point of Contact:** Michael A. Hedderich and David Adelani
{mhedderich, didelani} (at) lsv.uni-saarland.de
### Dataset Summary
A news headline topic classification dataset, similar to AG-news, for Hausa. The news headlines were collected from [VOA Hausa](https://www.voahausa.com/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Hausa (ISO 639-1: ha)
## Dataset Structure
### Data Instances
An instance consists of a news title sentence and the corresponding topic label.
### Data Fields
- `news_title`: A news title
- `label`: The label describing the topic of the news title. Can be one of the following classes: Nigeria, Africa, World, Health or Politics.
### 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 [@michael-aloys](https://github.com/michael-aloys) for adding this dataset. |
jlh/uci-census-income-94 | ---
dataset_info:
features:
- name: age
dtype: string
- name: class_of_worker
dtype: int64
- name: detailed_industry_recode
dtype: int64
- name: detailed_occupation_recode
dtype: string
- name: education
dtype: int64
- name: wage_per_hour
dtype: string
- name: enroll_in_edu_inst_last_wk
dtype: string
- name: marital_stat
dtype: string
- name: major_industry_code
dtype: string
- name: major_occupation_code
dtype: string
- name: race
dtype: string
- name: hispanic_origin
dtype: string
- name: sex
dtype: string
- name: member_of_a_labor_union
dtype: string
- name: reason_for_unemployment
dtype: string
- name: full_or_part_time_employment_stat
dtype: int64
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dtype: int64
- name: capital_losses
dtype: int64
- name: dividends_from_stocks
dtype: string
- name: tax_filer_stat
dtype: string
- name: region_of_previous_residence
dtype: string
- name: state_of_previous_residence
dtype: string
- name: detailed_household_and_family_stat
dtype: string
- name: detailed_household_summary_in_household
dtype: float64
- name: migration_code-change_in_msa
dtype: string
- name: migration_code-change_in_reg
dtype: string
- name: migration_code-move_within_reg
dtype: string
- name: live_in_this_house_1_year_ago
dtype: string
- name: migration_prev_res_in_sunbelt
dtype: string
- name: num_persons_worked_for_employer
dtype: int64
- name: family_members_under_18
dtype: string
- name: country_of_birth_father
dtype: string
- name: country_of_birth_mother
dtype: string
- name: country_of_birth_self
dtype: string
- name: citizenship
dtype: string
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dtype: int64
- name: fill_inc_questionnaire_for_veteran's_admin
dtype: string
- name: veterans_benefits
dtype: int64
- name: weeks_worked_in_year
dtype: int64
- name: year
dtype: int64
- name: income
dtype:
class_label:
names:
'0': ' - 50000.'
'1': ' 50000+.'
splits:
- name: train
num_bytes: 129952005
num_examples: 199523
download_size: 7989520
dataset_size: 129952005
---
# Dataset Card for "uci-census-income-94"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
thisiskeithkwan/SyNEJM | ---
license: apache-2.0
---
|
prateeky2806/bge_base_features_cot | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 422099575
num_examples: 100000
download_size: 421912325
dataset_size: 422099575
---
# Dataset Card for "bge_base_features_cot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ethical-Lens/HumanBias | ---
license: apache-2.0
---
|
recastai/coyo-75m-aesthetic-sorted | ---
license: cc-by-4.0
---
|
Skywork/mock_gsm8k_test | ---
license: other
license_name: license
license_link: >-
https://github.com/SkyworkAI/Skywork/blob/main/Skywork%20Community%20License.pdf
---
# Introduction
This dataset is a mirror of the GSM8K Test split. We have manually ensured the answers are correct. This dataset can serve as a reference to evaluate a model's ability to generalize to math problems.
# License Agreement
The community usage of SkyPile dataset requires Skywork Community License. The SkyPile dataset supports commercial use. If you plan to use the Skywork model or its derivatives for commercial purposes, you must abide by terms and conditions within Skywork Community License as well as Apache2.0.
# Contact Us and Citation
If you find our work helpful, please feel free to cite our paper~
```
@misc{wei2023skywork,
title={Skywork: A More Open Bilingual Foundation Model},
author={Tianwen Wei and Liang Zhao and Lichang Zhang and Bo Zhu and Lijie Wang and Haihua Yang and Biye Li and Cheng Cheng and Weiwei Lü and Rui Hu and Chenxia Li and Liu Yang and Xilin Luo and Xuejie Wu and Lunan Liu and Wenjun Cheng and Peng Cheng and Jianhao Zhang and Xiaoyu Zhang and Lei Lin and Xiaokun Wang and Yutuan Ma and Chuanhai Dong and Yanqi Sun and Yifu Chen and Yongyi Peng and Xiaojuan Liang and Shuicheng Yan and Han Fang and Yahui Zhou},
year={2023},
eprint={2310.19341},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
0x22almostEvil/ws-semantics-simnrel | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
- ru
- de
- it
tags:
- semantics
size_categories:
- 1K<n<10K
---
# Dataset Card for WS353-semantics-sim-and-rel with ~2K entries.
### Dataset Summary
License: Apache-2.0. Contains CSV of a list of word1, word2, their `connection score`, type of connection and language.
- ### Original Datasets are available here:
- https://leviants.com/multilingual-simlex999-and-wordsim353/
### Paper of original Dataset:
- https://arxiv.org/pdf/1508.00106v5.pdf |
kaxap/llama2-sql-instruct | ---
license: cc-by-nc-4.0
---
|
princeton-nlp/SWE-bench_Lite_bm25_27K | ---
dataset_info:
features:
- name: instance_id
dtype: string
- name: text
dtype: string
- name: repo
dtype: string
- name: base_commit
dtype: string
- name: problem_statement
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: patch
dtype: string
- name: test_patch
dtype: string
- name: version
dtype: string
- name: FAIL_TO_PASS
dtype: string
- name: PASS_TO_PASS
dtype: string
- name: environment_setup_commit
dtype: string
splits:
- name: dev
num_bytes: 2761169
num_examples: 23
- name: test
num_bytes: 36022389
num_examples: 300
download_size: 17538434
dataset_size: 38783558
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
### Dataset Summary
SWE-bench *Lite* is _subset_ of SWE-bench, a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 300 test Issue-Pull Request pairs from 11 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
This dataset `SWE-bench_Lite_bm25_27K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 27,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models.
The `text` column can be used directly with LMs to generate patch files.
Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
```diff
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>
```
This format can be used directly with the [SWE-bench inference scripts](https://github.com/princeton-nlp/SWE-bench/tree/main/inference). Please refer to these scripts for more details on inference.
|
CATIE-AQ/squad_v2_french_translated_fr_prompt_qa | ---
language:
- fr
license: apache-2.0
size_categories:
- 1M<n<10M
task_categories:
- question-answering
tags:
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- squad_v2_french_translated
---
# squad_v2_french_translated_fr_prompt_qa
## Summary
**squad_v2_french_translated_fr_prompt_qa** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **3,320,898** rows that can be used for a question-answering task.
The original data (without prompts) comes from the dataset [pragnakalp/squad_v2_french_translated](https://huggingface.co/datasets/pragnakalp/squad_v2_french_translated) and was augmented by questions in SQUAD 2.0 format in the [FrenchQA]( https://huggingface.co/datasets/CATIE-AQ/frenchQA) dataset.
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
42 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
# SQUAD 1.0 format
'Question : "'+question+'"\nContexte : "'+context+'" Réponse :',
'La réponse à la question "'+question+'" se trouve dans "'+context+'" Pouvez-vous me la dire ?',
'La réponse à la question "'+question+'" se trouve dans "'+context+'" Peux-tu me la dire ?',
'Extraire la réponse à la question à partir du contexte suivant.\n Question : "'+question+'" Contexte : "'+context+'"',
'Extrais la réponse à la question à partir du contexte suivant.\n Question : "'+question+'" Contexte : "'+context+'"',
'Extrayez la réponse à la question à partir du contexte suivant.\n Question : "'+question+'" Contexte : "'+context+'"',
'Étant donné le passage suivant : "'+context+'"\n Répondre à la question suivante sachant que la réponse est présente dans le texte.\n Question : "'+question+'"',
'Étant donné le passage suivant : "'+context+'"\n Réponds à la question suivante sachant que la réponse est présente dans le texte.\n Question : "'+question+'"',
'Étant donné le passage suivant : "'+context+'"\n Répondez à la question suivante sachant que la réponse est présente dans le texte.\n Question : "'+question+'"',
"""La réponse à la question : " """+question+""" " se trouve dans le texte : " """+context+""" "\n Peux-tu l'indiquer ?""",
"""La réponse à la question : " """+question+""" " se trouve dans le texte : " """+context+""" "\n Pouvez-vous l'indiquer ?""",
"""La réponse à la question : " """+question+""" " se trouve dans le texte : " """+context+""" "\n Qu'elle est-elle ?""",
# SQUAD 2.0 format
'"'+question+'"\n Répondre à la question ci-dessus en se basant sur le contexte suivant : "'+context+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'"'+question+'"\n Réponds à la question ci-dessus en te basant sur le contexte suivant : "'+context+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'"'+question+'"\n Répondez à la question ci-dessus en vous basant sur le contexte suivant : "'+context+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Utiliser le texte suivant pour répondre à la question : '+question+ '\n\n "'+context+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Utilise le texte suivant pour répondre à la question : '+question+ '\n\n "'+context+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'Utilisez le texte suivant pour répondre à la question : '+question+ '\n\n "'+context+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Lire le texte suivant et extraire la réponse à la question : "'+question+'"\n\n "'+context+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Lis le texte suivant et extrais la réponse à la question : "'+question+'"\n\n "'+context+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'Lisez le texte suivant et extrayez la réponse à la question : "'+question+'"\n\n "'+context+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'"'+context+'"\n\nSur la base du texte ci-dessus, répondre correctement à la question suivante : \n\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'"'+context+'"\n\nSur la base du texte ci-dessus, réponds correctement à la question suivante : \n\n "'+question+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'"'+context+'"\n\nSur la base du texte ci-dessus, répondez répondre correctement à la question suivante : \n\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Contexte : '+ context +'\n Compte tenu du texte ci-dessus, répondre correctement à la question suivante : "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Contexte : '+ context +'\n Compte tenu du texte ci-dessus, réponds correctement à la question suivante : "'+question+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'Contexte : '+ context +'\n Compte tenu du texte ci-dessus, répondez correctement à la question suivante : "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'"'+context+'"\n Extraire du passage la réponse à la question suivante : "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'"'+context+'"\n Extrais du passage la réponse à la question suivante : "'+question+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'"'+context+'"\n Extrayez du passage la réponse à la question suivante : "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Compte tenu du passage suivant, répondre à la question qui suit : "'+context+'"\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Compte tenu du passage suivant, réponds à la question qui suit : "'+context+'"\n "'+question+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'Compte tenu du passage suivant, répondez à la question qui suit : "'+context+'"\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Après avoir lu le paragraphe, répondre à la question suivante : "'+context+'"\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Après avoir lu le paragraphe, réponds à la question suivante : "'+context+'"\n "'+question+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'Après avoir lu le paragraphe, répondez à la question suivante : "'+context+'"\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Se référer au passage ci-dessous et répondre à la question suivante:\n Passage : "'+context+'"Question : "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Référe-toi au passage ci-dessous et réponds à la question suivante:\n Passage : "'+context+'"Question : "'+question+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'Référez-vous au passage ci-dessous et répondez à la question suivante:\n Passage : "'+context+'"Question : "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Lire le passage suivant et répondez à la question qui suit : \n "'+context+'"\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
'Lis le passage suivant et répondez à la question qui suit : \n "'+context+'"\n "'+question+'"\n Si tu ne trouves pas la réponse, répondre "sans réponse".',
'Lisez le passage suivant et répondez à la question qui suit : \n "'+context+'"\n "'+question+'"\n Si vous ne trouvez pas la réponse, répondre "sans réponse".',
```
# Splits
- `train` with 3,320,898 samples
- no `valid` split
- no `test` split
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/squad_v2_french_translated_fr_prompt_qa")
```
# Citation
## Original data
> Hugging Face repository: https://huggingface.co/datasets/pragnakalp/squad_v2_french_translated
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
apache-2.0 |
emozilla/quality-pruned-llama-gptneox-8k | ---
dataset_info:
features:
- name: article
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: int64
- name: hard
dtype: bool
splits:
- name: validation
num_bytes: 32447081.81016299
num_examples: 1322
- name: train
num_bytes: 36794158.71185097
num_examples: 1483
download_size: 4075392
dataset_size: 69241240.52201396
---
# Dataset Card for "quality-pruned-llama-gptneox-8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ondevicellm/hh-rlhf-h4 | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 472329144
num_examples: 160800
- name: test
num_bytes: 25348918
num_examples: 8552
download_size: 275259657
dataset_size: 497678062
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/requests | ---
license: apache-2.0
---
# Open LLM Leaderboard Requests
This repository contains the request files of models that have been submitted to the Open LLM Leaderboard.
You can take a look at the current status of your model by finding its request file in this dataset. If your model failed, feel free to open an issue on the Open LLM Leaderboard! (We don't follow issues in this repository as often)
## Evaluation Methodology
The evaluation process involves running your models against several benchmarks from the Eleuther AI Harness, a unified framework for measuring the effectiveness of generative language models. Below is a brief overview of each benchmark:
1. AI2 Reasoning Challenge (ARC) - Grade-School Science Questions (25-shot)
2. HellaSwag - Commonsense Inference (10-shot)
3. MMLU - Massive Multi-Task Language Understanding, knowledge on 57 domains (5-shot)
4. TruthfulQA - Propensity to Produce Falsehoods (0-shot)
5. Winogrande - Adversarial Winograd Schema Challenge (5-shot)
6. GSM8k - Grade School Math Word Problems Solving Complex Mathematical Reasoning (5-shot)
Together, these benchmarks provide an assessment of a model's capabilities in terms of knowledge, reasoning, and some math, in various scenarios.
## Accessing Your Results
To view the numerical results of your evaluated models, visit the dedicated Hugging Face Dataset at https://huggingface.co/datasets/open-llm-leaderboard/results. This dataset offers a thorough breakdown of each model's performance on the individual benchmarks.
## Exploring Model Details
For further insights into the inputs and outputs of specific models, locate the "📄" emoji associated with the desired model within this repository. Clicking on this icon will direct you to the respective GitHub page containing detailed information about the model's behavior during the evaluation process.
|
theblackcat102/her-zh-hant | ---
language:
- zh
---
Chinese version of [Samantha data](https://huggingface.co/datasets/ehartford/samantha-data)
Some changes, a few names are choosen instead of the default Theodore and Samantha. This should provide some kind of flexibity for changing names during inference |
ammumadhu/Toxic_dataset | ---
license: apache-2.0
---
|
doof-ferb/infore1_25hours | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- vi
pretty_name: InfoRe Technology public dataset №1
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 7370428827.92
num_examples: 14935
download_size: 7832947140
dataset_size: 7370428827.92
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# unofficial mirror of InfoRe Technology public dataset №1
official announcement: https://www.facebook.com/groups/j2team.community/permalink/1010834009248719/
25h, 14.9k samples, InfoRe paid a contractor to read text
official download: `magnet:?xt=urn:btih:1cbe13fb14a390c852c016a924b4a5e879d85f41&dn=25hours.zip&tr=http%3A%2F%2Foffice.socials.vn%3A8725%2Fannounce`
mirror: https://files.huylenguyen.com/25hours.zip
unzip password: `BroughtToYouByInfoRe`
pre-process: none
need to do: check misspelling
usage with HuggingFace:
```python
# pip install -q "datasets[audio]"
from datasets import load_dataset
from torch.utils.data import DataLoader
dataset = load_dataset("doof-ferb/infore1_25hours", split="train", streaming=True)
dataset.set_format(type="torch", columns=["audio", "transcription"])
dataloader = DataLoader(dataset, batch_size=4)
``` |
wasertech/OneOS | ---
language:
- en
- fr
license: cc0-1.0
size_categories:
- 10K<n<100K
pretty_name: OneOS
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 33980941
num_examples: 13640
download_size: 2589190
dataset_size: 33980941
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- code
- bash
- python
- Web Search
- Wikipedia
- NLU
---
# OneOS Dataset
The OneOS dataset is a collection of text data for the [OneOS project](https://github.com/wasertech/OneOS). It consists of a large number of text samples that can be used for training and evaluating natural language processing models.
## Dataset Details
- Number of Samples: 13,068
- License: CC0*
- Language: English, French
\* Only unlicensed sentences generated manually fall under CreativeCommon-0. Sentences already licensed under different terms, such as [nl2bash](https://github.com/TellinaTool/nl2bash) or [samantha-data](https://huggingface.co/datasets/ehartford/samantha-data), remain subject to their respective licenses. The same applies to sentences produced using language models operating under special licenses, like LLama or the GPT series.
## Dataset Format
Comming soon. |
NyxKrage/BambiSleep | ---
tags:
- not-for-all-audiences
configs:
- config_name: default
data_files:
- split: train
path: "bambisleep.parquet"
---
|
theblackcat102/anime-understanding-dataset | ---
license: mit
configs:
- config_name: chainsawman
data_files:
- split: train
path: "chainsawman_dev.jsonl"
- split: validation
path: "chainsawman_val.jsonl"
- split: test
path: "chainsawman_test.jsonl"
- config_name: kurokonobasuke
data_files:
- split: train
path: "kurokonobasuke_dev.jsonl"
- split: validation
path: "kurokonobasuke_val.jsonl"
- split: test
path: "kurokonobasuke_test.jsonl"
- config_name: onepunch
data_files:
- split: train
path: "onepunch_dev.jsonl"
- split: validation
path: "onepunch_val.jsonl"
- split: test
path: "onepunch_test.jsonl"
- config_name: hellsing
data_files:
- split: train
path: "hellsing_dev.jsonl"
- split: validation
path: "hellsing_val.jsonl"
- split: test
path: "hellsing_test.jsonl"
- config_name: frieren
data_files:
- split: train
path: "frieren_dev.jsonl"
- split: validation
path: "frieren_val.jsonl"
- split: test
path: "frieren_test.jsonl"
- config_name: aot
data_files:
- split: train
path: "aot_dev.jsonl"
- split: validation
path: "aot_val.jsonl"
- split: test
path: "aot_test.jsonl"
- config_name: naruto
data_files:
- split: train
path: "naruto_dev.jsonl"
- split: validation
path: "naruto_val.jsonl"
- split: test
path: "naruto_test.jsonl"
- config_name: dr_stone
data_files:
- split: train
path: "dr_stone_dev.jsonl"
- split: validation
path: "dr_stone_val.jsonl"
- split: test
path: "dr_stone_test.jsonl"
- config_name: gundam_00
data_files:
- split: train
path: "gundam_00_dev.jsonl"
- split: validation
path: "gundam_00_val.jsonl"
- split: test
path: "gundam_00_test.jsonl"
- config_name: darling-in-the-franxx
data_files:
- split: train
path: "darling-in-the-franxx_dev.jsonl"
- split: validation
path: "darling-in-the-franxx_val.jsonl"
- split: test
path: "darling-in-the-franxx_test.jsonl"
- config_name: berserk
data_files:
- split: train
path: "berserk_dev.jsonl"
- split: validation
path: "berserk_val.jsonl"
- split: test
path: "berserk_test.jsonl"
- config_name: evangelion
data_files:
- split: train
path: "evangelion_dev.jsonl"
- split: validation
path: "evangelion_val.jsonl"
- split: test
path: "evangelion_test.jsonl"
- config_name: onepiece
data_files:
- split: train
path: "onepiece_dev.jsonl"
- split: validation
path: "onepiece_val.jsonl"
- split: test
path: "onepiece_test.jsonl"
task_categories:
- question-answering
language:
- en
tags:
- question-answering
- multi-choice
pretty_name: anime understanding dataset
size_categories:
- 1K<n<10K
---
# Anime Understanding Benchmark (WIP)
Evaluate anime knowledge found in existing LLMs. We hope to provide an easy to run evaluation on knowledge understanding in anime/manga. Better understanding in anime/manga knowledge should resulted in task such as waifu role play.
Any suggestion is open in discussion tab.
# Currently in the works
[] Eval on popular models such as gpt, hermes, dolphin, llama base model
[] Add more metadata regarding of anime/manga year span
[] Suggestions for more anime choices ideally diverse in terms of year of air and topic diversity.
[] Human inspection for any potential errors
## Data sources:
[One Piece](https://onepiece.fandom.com)
[Chainsaw man](https://chainsaw-man.fandom.com)
[Tokyo Ghoul](https://tokyoghoul.fandom.com)
[Naruto](https://naruto.fandom.com)
[Berserk](https://berserk.fandom.com/)
[Darling in the FRANXX](https://darling-in-the-franxx.fandom.com/)
[Shingeki no Kyojin - Attack on Titan](https://attackontitan.fandom.com)
[Evangelion](https://evangelion.fandom.com)
[Wikipedia](https://www.wikipedia.org/)
|
deepghs/3d_datasets | ---
tags:
- art
- not-for-all-audiences
size_categories:
- 10K<n<100K
--- |
kamalchibrani/fall_detection | ---
license: apache-2.0
---
|
Prarabdha/Paul_RNA_Sequence_Unprocessed_Data | ---
license: mit
---
|
manojkumarvohra/amplified_emotions | ---
language:
- en
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
- name: labels
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 3335026
num_examples: 30295
- name: validation
num_bytes: 214695
num_examples: 2000
- name: test
num_bytes: 217173
num_examples: 2000
download_size: 1756592
dataset_size: 3766894
---
Dataset Summary
---------------
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
This dataset is a processed form of "dair-ai/emotion" dataset. [https://huggingface.co/datasets/dair-ai/emotion]
In this one, I have amplified the samples for minority classes so that all the emotion classes have [approximate] equal sample count.
There is another dataset with duplicate samples here: https://huggingface.co/datasets/manojkumarvohra/replicated_emotions
This one is different with grammatic variations of emotional statements. |
presencesw/multinli_matched | ---
dataset_info:
features:
- name: gold_label
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
splits:
- name: train
num_bytes: 75405059
num_examples: 392702
- name: dev
num_bytes: 1853683
num_examples: 9815
download_size: 51217090
dataset_size: 77258742
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
|
hippocrates/MedInstruct_train | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 102469097
num_examples: 52002
download_size: 45924007
dataset_size: 102469097
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sushreessethi/speechocean_test | ---
dataset_info:
features:
- name: audio_file_paths
dtype: string
splits:
- name: train
num_bytes: 1298
num_examples: 59
download_size: 1356
dataset_size: 1298
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_mistral | ---
pretty_name: Evaluation run of Minami-su/Qwen1.5-7B-Chat_mistral
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Minami-su/Qwen1.5-7B-Chat_mistral](https://huggingface.co/Minami-su/Qwen1.5-7B-Chat_mistral)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 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 aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_mistral\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-29T19:50:32.282318](https://huggingface.co/datasets/open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_mistral/blob/main/results_2024-02-29T19-50-32.282318.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\": 0.2574103533052331,\n\
\ \"acc_stderr\": 0.030896657868578148,\n \"acc_norm\": 0.2577380385721803,\n\
\ \"acc_norm_stderr\": 0.031718538656865775,\n \"mc1\": 0.25703794369645044,\n\
\ \"mc1_stderr\": 0.015298077509485081,\n \"mc2\": 0.523327565382496,\n\
\ \"mc2_stderr\": 0.01640197991638979\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.2090443686006826,\n \"acc_stderr\": 0.011882746987406455,\n\
\ \"acc_norm\": 0.24488054607508533,\n \"acc_norm_stderr\": 0.012566273985131356\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2615016928898626,\n\
\ \"acc_stderr\": 0.004385544487143913,\n \"acc_norm\": 0.26687910774746065,\n\
\ \"acc_norm_stderr\": 0.004414246720076113\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n\
\ \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n\
\ \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.2631578947368421,\n \"acc_stderr\": 0.035834961763610625,\n\
\ \"acc_norm\": 0.2631578947368421,\n \"acc_norm_stderr\": 0.035834961763610625\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.19,\n\
\ \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n \
\ \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2339622641509434,\n \"acc_stderr\": 0.02605529690115292,\n\
\ \"acc_norm\": 0.2339622641509434,\n \"acc_norm_stderr\": 0.02605529690115292\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562429,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562429\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n\
\ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.1791907514450867,\n\
\ \"acc_stderr\": 0.029242513059063294,\n \"acc_norm\": 0.1791907514450867,\n\
\ \"acc_norm_stderr\": 0.029242513059063294\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.23,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.23,\n\
\ \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.2723404255319149,\n \"acc_stderr\": 0.0291012906983867,\n\
\ \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.0291012906983867\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\
\ \"acc_stderr\": 0.041424397194893596,\n \"acc_norm\": 0.2631578947368421,\n\
\ \"acc_norm_stderr\": 0.041424397194893596\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.036001056927277716,\n\
\ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.036001056927277716\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113946,\n \"\
acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113946\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.04006168083848878,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.04006168083848878\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.21935483870967742,\n\
\ \"acc_stderr\": 0.023540799358723306,\n \"acc_norm\": 0.21935483870967742,\n\
\ \"acc_norm_stderr\": 0.023540799358723306\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.28078817733990147,\n \"acc_stderr\": 0.03161856335358609,\n\
\ \"acc_norm\": 0.28078817733990147,\n \"acc_norm_stderr\": 0.03161856335358609\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.032250781083062896,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.032250781083062896\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.26262626262626265,\n \"acc_stderr\": 0.03135305009533086,\n \"\
acc_norm\": 0.26262626262626265,\n \"acc_norm_stderr\": 0.03135305009533086\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.30569948186528495,\n \"acc_stderr\": 0.03324837939758159,\n\
\ \"acc_norm\": 0.30569948186528495,\n \"acc_norm_stderr\": 0.03324837939758159\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02242127361292371,\n\
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02242127361292371\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26296296296296295,\n \"acc_stderr\": 0.026842057873833706,\n \
\ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.026842057873833706\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.29831932773109243,\n \"acc_stderr\": 0.029719142876342863,\n\
\ \"acc_norm\": 0.29831932773109243,\n \"acc_norm_stderr\": 0.029719142876342863\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658754,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658754\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.20733944954128442,\n \"acc_stderr\": 0.017381415563608674,\n \"\
acc_norm\": 0.20733944954128442,\n \"acc_norm_stderr\": 0.017381415563608674\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3055555555555556,\n \"acc_stderr\": 0.03141554629402544,\n \"\
acc_norm\": 0.3055555555555556,\n \"acc_norm_stderr\": 0.03141554629402544\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.27450980392156865,\n \"acc_stderr\": 0.03132179803083291,\n \"\
acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.03132179803083291\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658342,\n \
\ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658342\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.15246636771300448,\n\
\ \"acc_stderr\": 0.024126204813252873,\n \"acc_norm\": 0.15246636771300448,\n\
\ \"acc_norm_stderr\": 0.024126204813252873\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.22137404580152673,\n \"acc_stderr\": 0.0364129708131373,\n\
\ \"acc_norm\": 0.22137404580152673,\n \"acc_norm_stderr\": 0.0364129708131373\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\
\ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.21296296296296297,\n\
\ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.25766871165644173,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.25766871165644173,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n\
\ \"acc_stderr\": 0.04203277291467763,\n \"acc_norm\": 0.26785714285714285,\n\
\ \"acc_norm_stderr\": 0.04203277291467763\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.33980582524271846,\n \"acc_stderr\": 0.04689765937278135,\n\
\ \"acc_norm\": 0.33980582524271846,\n \"acc_norm_stderr\": 0.04689765937278135\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2606837606837607,\n\
\ \"acc_stderr\": 0.028760348956523414,\n \"acc_norm\": 0.2606837606837607,\n\
\ \"acc_norm_stderr\": 0.028760348956523414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036844,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036844\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.25287356321839083,\n\
\ \"acc_stderr\": 0.015543377313719681,\n \"acc_norm\": 0.25287356321839083,\n\
\ \"acc_norm_stderr\": 0.015543377313719681\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.30346820809248554,\n \"acc_stderr\": 0.024752411960917212,\n\
\ \"acc_norm\": 0.30346820809248554,\n \"acc_norm_stderr\": 0.024752411960917212\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875195,\n\
\ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875195\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3022508038585209,\n\
\ \"acc_stderr\": 0.026082700695399662,\n \"acc_norm\": 0.3022508038585209,\n\
\ \"acc_norm_stderr\": 0.026082700695399662\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2345679012345679,\n \"acc_stderr\": 0.023576881744005716,\n\
\ \"acc_norm\": 0.2345679012345679,\n \"acc_norm_stderr\": 0.023576881744005716\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23049645390070922,\n \"acc_stderr\": 0.025123739226872402,\n \
\ \"acc_norm\": 0.23049645390070922,\n \"acc_norm_stderr\": 0.025123739226872402\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2392438070404172,\n\
\ \"acc_stderr\": 0.010896123652676658,\n \"acc_norm\": 0.2392438070404172,\n\
\ \"acc_norm_stderr\": 0.010896123652676658\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.43014705882352944,\n \"acc_stderr\": 0.030074971917302875,\n\
\ \"acc_norm\": 0.43014705882352944,\n \"acc_norm_stderr\": 0.030074971917302875\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2434640522875817,\n \"acc_stderr\": 0.017362473762146616,\n \
\ \"acc_norm\": 0.2434640522875817,\n \"acc_norm_stderr\": 0.017362473762146616\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\
\ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\
\ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.23673469387755103,\n \"acc_stderr\": 0.02721283588407316,\n\
\ \"acc_norm\": 0.23673469387755103,\n \"acc_norm_stderr\": 0.02721283588407316\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23383084577114427,\n\
\ \"acc_stderr\": 0.02992941540834838,\n \"acc_norm\": 0.23383084577114427,\n\
\ \"acc_norm_stderr\": 0.02992941540834838\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.27710843373493976,\n\
\ \"acc_stderr\": 0.034843315926805875,\n \"acc_norm\": 0.27710843373493976,\n\
\ \"acc_norm_stderr\": 0.034843315926805875\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.2573099415204678,\n \"acc_stderr\": 0.03352799844161865,\n\
\ \"acc_norm\": 0.2573099415204678,\n \"acc_norm_stderr\": 0.03352799844161865\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.25703794369645044,\n\
\ \"mc1_stderr\": 0.015298077509485081,\n \"mc2\": 0.523327565382496,\n\
\ \"mc2_stderr\": 0.01640197991638979\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5367008681925809,\n \"acc_stderr\": 0.014014578458843258\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/Minami-su/Qwen1.5-7B-Chat_mistral
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: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|arc:challenge|25_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|gsm8k|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hellaswag|10_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-50-32.282318.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T19-50-32.282318.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- '**/details_harness|winogrande|5_2024-02-29T19-50-32.282318.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-29T19-50-32.282318.parquet'
- config_name: results
data_files:
- split: 2024_02_29T19_50_32.282318
path:
- results_2024-02-29T19-50-32.282318.parquet
- split: latest
path:
- results_2024-02-29T19-50-32.282318.parquet
---
# Dataset Card for Evaluation run of Minami-su/Qwen1.5-7B-Chat_mistral
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Minami-su/Qwen1.5-7B-Chat_mistral](https://huggingface.co/Minami-su/Qwen1.5-7B-Chat_mistral) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 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 aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_mistral",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-29T19:50:32.282318](https://huggingface.co/datasets/open-llm-leaderboard/details_Minami-su__Qwen1.5-7B-Chat_mistral/blob/main/results_2024-02-29T19-50-32.282318.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": 0.2574103533052331,
"acc_stderr": 0.030896657868578148,
"acc_norm": 0.2577380385721803,
"acc_norm_stderr": 0.031718538656865775,
"mc1": 0.25703794369645044,
"mc1_stderr": 0.015298077509485081,
"mc2": 0.523327565382496,
"mc2_stderr": 0.01640197991638979
},
"harness|arc:challenge|25": {
"acc": 0.2090443686006826,
"acc_stderr": 0.011882746987406455,
"acc_norm": 0.24488054607508533,
"acc_norm_stderr": 0.012566273985131356
},
"harness|hellaswag|10": {
"acc": 0.2615016928898626,
"acc_stderr": 0.004385544487143913,
"acc_norm": 0.26687910774746065,
"acc_norm_stderr": 0.004414246720076113
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.23,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.23,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.23703703703703705,
"acc_stderr": 0.03673731683969506,
"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.03673731683969506
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.035834961763610625,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.035834961763610625
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.19,
"acc_stderr": 0.03942772444036623,
"acc_norm": 0.19,
"acc_norm_stderr": 0.03942772444036623
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2339622641509434,
"acc_stderr": 0.02605529690115292,
"acc_norm": 0.2339622641509434,
"acc_norm_stderr": 0.02605529690115292
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2361111111111111,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.2361111111111111,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562429,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562429
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.1791907514450867,
"acc_stderr": 0.029242513059063294,
"acc_norm": 0.1791907514450867,
"acc_norm_stderr": 0.029242513059063294
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816508,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816508
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2723404255319149,
"acc_stderr": 0.0291012906983867,
"acc_norm": 0.2723404255319149,
"acc_norm_stderr": 0.0291012906983867
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.041424397194893596,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.041424397194893596
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2482758620689655,
"acc_stderr": 0.036001056927277716,
"acc_norm": 0.2482758620689655,
"acc_norm_stderr": 0.036001056927277716
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.25396825396825395,
"acc_stderr": 0.022418042891113946,
"acc_norm": 0.25396825396825395,
"acc_norm_stderr": 0.022418042891113946
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.04006168083848878,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.04006168083848878
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.18,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.18,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.21935483870967742,
"acc_stderr": 0.023540799358723306,
"acc_norm": 0.21935483870967742,
"acc_norm_stderr": 0.023540799358723306
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.28078817733990147,
"acc_stderr": 0.03161856335358609,
"acc_norm": 0.28078817733990147,
"acc_norm_stderr": 0.03161856335358609
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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AdapterOcean/med_alpaca_standardized_cluster_10 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 86620668
num_examples: 8486
download_size: 26365147
dataset_size: 86620668
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SameerMahajan/marathi_numbers-1-20 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: labels
sequence:
class_label:
names:
'0': 0
'1': 1
'2': 2
'3': 3
'4': 4
'5': 5
'6': 6
'7': 7
'8': 8
'9': 9
'10': 10
'11': 11
'12': 12
'13': 13
'14': 14
'15': 15
'16': 16
'17': 17
'18': 18
'19': 19
- name: number
sequence: int64
splits:
- name: train
num_bytes: 79901585.38
num_examples: 1020
download_size: 6503225
dataset_size: 79901585.38
---
# Dataset Card for "marathi_numbers-1-20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Short-Answer-Feedback/saf_communication_networks_english | ---
pretty_name: SAF - Communication Networks - English
annotations_creators:
- expert-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- short answer feedback
- communication networks
task_categories:
- text2text-generation
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: reference_answer
dtype: string
- name: provided_answer
dtype: string
- name: answer_feedback
dtype: string
- name: verification_feedback
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 2363828
num_examples: 1700
- name: validation
num_bytes: 592869
num_examples: 427
- name: test_unseen_answers
num_bytes: 515669
num_examples: 375
- name: test_unseen_questions
num_bytes: 777945
num_examples: 479
download_size: 941169
dataset_size: 4250311
license: cc-by-4.0
---
# Dataset Card for "saf_communication_networks_english"
## 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)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
### Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) dataset to examine the German subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german).
### Supported Tasks and Leaderboards
- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.
### Languages
The questions, reference answers, provided answers and the answer feedback in the dataset are written in English.
## Dataset Structure
### Data Instances
An example of an entry of the training split looks as follows.
```
{
"id": "1",
"question": "Is this a question?",
"reference_answer": "Yes, that is a question.",
"provided_answer": "I'm certain this is a question.",
"answer_feedback": "The response is correct.",
"verification_feedback": "Correct",
"score": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature (UUID4 in HEX format).
- `question`: a `string` feature representing a question.
- `reference_answer`: a `string` feature representing a reference answer to the question.
- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = maximum points achievable), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
- `score`: a `float64` feature representing the score given to the provided answer. For most questions it ranges from 0 to 1.
### Data Splits
The dataset is comprised of four data splits.
- `train`: used for training, contains a set of questions and the provided answers to them.
- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).
- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in the `train` split.
| Split |train|validation|test_unseen_answers|test_unseen_questions|
|-------------------|----:|---------:|------------------:|--------------------:|
|Number of instances| 1700| 427| 375| 479|
## Dataset Creation
### Annotation Process
Two graduate students who had completed the communication networks course were selected to evaluate the answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.
## Additional Information
### Citation Information
```
@inproceedings{filighera-etal-2022-answer,
title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
author = "Filighera, Anna and
Parihar, Siddharth and
Steuer, Tim and
Meuser, Tobias and
Ochs, Sebastian",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.587",
doi = "10.18653/v1/2022.acl-long.587",
pages = "8577--8591",
}
```
### Contributions
Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset. |
kpriyanshu256/semeval-task-8-a-mono | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: model
dtype: string
- name: source
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 237254533
num_examples: 83829
- name: val
num_bytes: 101985332
num_examples: 35928
- name: test
num_bytes: 10543757
num_examples: 5000
download_size: 201649583
dataset_size: 349783622
---
# Dataset Card for "semeval-task-8-a-mono"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aimona/sg32 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4073025.0
num_examples: 32
download_size: 0
dataset_size: 4073025.0
---
# Dataset Card for "sg32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sudipkar123/salesforcecode | ---
license: apache-2.0
---
|
autoevaluate/autoeval-eval-multi_news-default-1c7825-44810145152 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- multi_news
eval_info:
task: summarization
model: t5-base
metrics: ['rouge']
dataset_name: multi_news
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: t5-base
* Dataset: multi_news
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@1136517075@qq.com](https://huggingface.co/1136517075@qq.com) for evaluating this model. |
Biomedical-TeMU/SPACCC_Sentence-Splitter | ---
license: cc-by-4.0
---
# The Sentence Splitter (SS) for Clinical Cases Written in Spanish
## Introduction
This repository contains the sentence splitting model trained using the SPACCC_SPLIT corpus (https://github.com/PlanTL-SANIDAD/SPACCC_SPLIT). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to split sentences in biomedical documents, specially clinical cases written in Spanish. This model obtains a F-Measure of 98.75%.
This model was created using the Apache OpenNLP machine learning toolkit (https://opennlp.apache.org/), with the release number 1.8.4, released in December 2017.
This repository contains the model, training set, testing set, Gold Standard, executable file, and the source code.
## Prerequisites
This software has been compiled with Java SE 1.8 and it should work with recent versions. You can download Java from the following website: https://www.java.com/en/download
The executable file already includes the Apache OpenNLP dependencies inside, so the download of this toolkit is not necessary. However, you may download the latest version from this website: https://opennlp.apache.org/download.html
The library file we have used to compile is "opennlp-tools-1.8.4.jar". The source code should be able to compile with the latest version of OpenNLP, "opennlp-tools-*RELEASE_NUMBER*.jar". In case there are compilation or execution errors, please let us know and we will make all the necessary updates.
## Directory structure
<pre>
exec/
An executable file that can be used to apply the sentence splitter to your documents.
You can find the notes about its execution below in section "Usage".
gold_standard/
The clinical cases used as gold standard to evaluate the model's performance.
model/
The sentence splitting model, "es-sentence-splitter-model-spaccc.bin", a binary file.
src/
The source code to create the model (CreateModelSS.java) and evaluate it (EvaluateModelSS.java).
The directory includes an example about how to use the model inside your code (SentenceSplitter.java).
File "abbreviations.dat" contains a list of abbreviations, essential to build the model.
test_set/
The clinical cases used as test set to evaluate the model's performance.
train_set/
The clinical cases used to build the model. We use a single file with all documents present in
directory "train_set_docs" concatented.
train_set_docs/
The clinical cases used to build the model. For each record the sentences are already splitted.
</pre>
## Usage
The executable file *SentenceSplitter.jar* is the program you need to split the sentences of the document. For this program, two arguments are needed: (1) the text file to split the sentences, and (2) the model file (*es-sentence-splitter-model-spaccc.bin*). The program will display all sentences splitted in the terminal, with one sentence per line.
From the `exec` folder, type the following command in your terminal:
<pre>
$ java -jar SentenceSplitter.jar INPUT_FILE MODEL_FILE
</pre>
## Examples
Assuming you have the executable file, the input file and the model file in the same directory:
<pre>
$ java -jar SentenceSplitter.jar file_with_sentences_not_splitted.txt es-sentence-splitter-model-spaccc.bin
</pre>
## Model creation
To create this sentence splitting model, we used the following training parameters (class *TrainingParameters* in OpenNLP) to get the best performance:
- Number of iterations: 4000.
- Cutoff parameter: 3.
- Trainer type parameter: *EventTrainer.EVENT_VALUE*.
- Algorithm: Maximum Entropy (*ModelType.MAXENT.name()*).
Meanwhile, we used the following parameters for the sentence split builder (class *SentenceDetectorFactory* in OpenNLP) to get the best performance:
- Subclass name: null value.
- Language code: *es* (for Spanish).
- Use token end: true.
- Abbreviation dictionary: file "abbreviations.dat" (included in the `src/` directory).
- End of file characters: ".", "?" and "!".
## Model evaluation
After tuning the model using different values for each parameter mentioned above, we got the best performance with the values mentioned above.
| | Value |
| ----------------------------------------: | :------ |
| Number of sentences in the gold standard | 1445 |
| Number of sentences generated | 1447 |
| Number of sentences correctly splitted | 1428 |
| Number of sentences wrongly splitted | 12 |
| Number of sentences missed | 5 |
| **Precision** | **98.69%** |
| **Recall** | **98.82%** |
| **F-Measure** | **98.75%**|
Table 1: Evaluation statistics for the sentence splitting model.
## Contact
Ander Intxaurrondo (ander.intxaurrondo@bsc.es)
## License
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)
|
mHossain/final_train_v4_test_120000 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: input_text
dtype: string
- name: target_text
dtype: string
- name: prefix
dtype: string
splits:
- name: train
num_bytes: 5770639.8
num_examples: 18000
- name: test
num_bytes: 641182.2
num_examples: 2000
download_size: 2789087
dataset_size: 6411822.0
---
# Dataset Card for "final_train_v4_test_120000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jacktheporsche/Bosch2023Product | ---
language:
- en
pretty_name: Chunked Bosch eBike 2023 Data
--- |
pvrancx/legobricks | ---
license: apache-2.0
task_categories:
- image-classification
pretty_name: legobricks
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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'791': '60607'
'792': '60608'
'793': 60616b
'794': '60623'
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'798': '60849'
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'807': '61254'
'808': 6126a
'809': 6126b
'810': '61332'
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'813': '6140'
'814': '61409'
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'816': '6148'
'817': '61482'
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'820': '61678'
'821': '61780'
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'825': '6183'
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'833': '6222'
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'835': '6231'
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'843': '62531'
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'846': '6259'
'847': '6266'
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'849': '63082'
'850': '6378'
'851': '63864'
'852': '63868'
'853': '63869'
'854': '63965'
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'892': '73230'
'893': '73825'
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'906': '85861'
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'909': '85975'
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'967': '92947'
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'969': '93061'
'970': '93095'
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'978': '94925'
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'980': '96874'
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'987': '98283'
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'989': '98585'
'990': '98721'
'991': '98834'
'992': '99008'
'993': '99021'
'994': '99206'
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'996': '99563'
'997': '99773'
'998': '99780'
'999': '99781'
splits:
- name: train
num_bytes: 25066440000.0
num_examples: 400000
download_size: 13152000872
dataset_size: 25066440000.0
---
# Dataset Card for LegoBricks
### Dataset Summary
3D images of LEGO Parts. Dataset contains the 1000 most common LEGO parts (according to the [rebrickable database](https://rebrickable.com/help/lego-database/)).
Each part has 400 images of different rotation angles and colors. Colors are sampled randomly, weighted by number of occurences for that part and color in the database.
The dataset contains a train split with 1000 classes, each represented by 400 images.
Class names are the LEGO part IDs. These ids can be used to reference the part on [BrickLink](https://www.bricklink.com/) or [Rebrickable](https://rebrickable.com)
Note that identical parts can be present under multipe IDs, due to mold updates by LEGO.
Alternative IDs can be found on Bricklink.
## Dataset Creation
Parts IDs and statistics were extracted from [rebrickable](https://rebrickable.com/) database. Images generated using [ldraw](https://www.ldraw.org/).
This dataset is not created or endorsed by LEGO. LEGO® is a trademark of the LEGO Group of companies
|
mayfieldmob/DeepCAM | ---
license: unknown
tags:
- mlperf
- deepcam
- HPC
---
This is an easy way to access the mini version of the dataset for mlperf |
lukasbraach/rwth_phoenix_weather_2014 | ---
dataset_info:
- config_name: multisigner
features:
- name: id
dtype: string
- name: transcription
dtype: string
- name: frames
sequence: image
splits:
- name: train
num_bytes: 35090755574
num_examples: 5672
- name: validation
num_bytes: 3294869318
num_examples: 540
- name: test
num_bytes: 3935898314
num_examples: 629
download_size: 43042303939
dataset_size: 42321523206
- config_name: pre-training
features:
- name: id
dtype: string
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 744118
num_examples: 5672
- name: validation
num_bytes: 63848
num_examples: 540
- name: test
num_bytes: 75329
num_examples: 629
download_size: 43042303939
dataset_size: 883295
- config_name: signerindependent
features:
- name: id
dtype: string
- name: transcription
dtype: string
- name: frames
sequence: image
splits:
- name: train
num_bytes: 26933922764
num_examples: 4376
- name: validation
num_bytes: 720569029
num_examples: 111
- name: test
num_bytes: 1175797903
num_examples: 180
download_size: 29320607031
dataset_size: 28830289696
---
# RWTH-Weather-Phoenix 2014
This archive contains two sets of the RWTH-Weather-Phoenix 2014 corpus
1. the multisigner set
2. the signer independent set.
It is released under non-commercial cc 4.0 license with attribution (see attachment)
If you use this data in your research, please cite:
```
O. Koller, J. Forster, and H. Ney. Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers. Computer Vision and Image Understanding, volume 141, pages 108-125, December 2015.
```
and
```
Koller, Zargaran, Ney. "Re-Sign: Re-Aligned End-to-End Sequence Modeling with Deep Recurrent CNN-HMMs" in CVPR 2017, Honululu, Hawaii, USA.
```
See README files in subfolders for more information.
### CHANGELOG
- v1 Aug 20 2016, initial version of the archive. multisigner setup
- v2 Apr 21 2017, signer independent SI5 subset, added caffe models and automatic frame-alignment
- v3 Nov 3 2017, added language models and complete set of hyper parameters to reproduce the published results
|
headintheclouds6453/bkheart-llama-finetuning-data-v0 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2157429
num_examples: 1460
download_size: 361285
dataset_size: 2157429
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_wnli_our_we | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: train
num_bytes: 796
num_examples: 4
download_size: 3180
dataset_size: 796
---
# Dataset Card for "MULTI_VALUE_wnli_our_we"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
plaguss/go_emotions_raw | ---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for go_emotions_raw
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("plaguss/go_emotions_raw")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("plaguss/go_emotions_raw")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | Label | multi_label_selection | True | Classify the text by selecting the correct label from the given list of labels. | ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! "
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000001",
"values": {
"label": {
"value": [
"neutral"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000016",
"values": {
"label": {
"value": [
"anger",
"annoyance",
"optimism"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000028",
"values": {
"label": {
"value": [
"approval"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000039",
"values": {
"label": {
"value": [
"neutral"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000048",
"values": {
"label": {
"value": [
"annoyance"
]
}
}
}
],
"suggestions": [
{
"agent": null,
"question_name": "label",
"score": null,
"type": "human",
"value": [
"annoyance",
"neutral"
]
}
],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"label": [
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000001",
"value": [
"neutral"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000016",
"value": [
"anger",
"annoyance",
"optimism"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000028",
"value": [
"approval"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000039",
"value": [
"neutral"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000048",
"value": [
"annoyance"
]
}
],
"label-suggestion": [
"annoyance",
"neutral"
],
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": "human"
},
"metadata": "{}",
"text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! "
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `multi_label_selection` with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'], and description "Classify the text by selecting the correct label from the given list of labels.".
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `multi_label_selection` with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Script used for the generation
```python
import argilla as rg
from datasets import load_dataset
import uuid
from datasets import concatenate_datasets
ds = load_dataset("go_emotions", "raw", split="train")
ds_prepared = load_dataset("go_emotions")
_CLASS_NAMES = [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"optimism",
"pride",
"realization",
"relief",
"remorse",
"sadness",
"surprise",
"neutral",
]
label_to_id = {label: i for i, label in enumerate(_CLASS_NAMES)}
id_to_label = {i: label for i, label in enumerate(_CLASS_NAMES)}
# Concatenate the datasets and transform to pd.DataFrame
ds_prepared = concatenate_datasets([ds_prepared["train"], ds_prepared["validation"], ds_prepared["test"]])
df_prepared = ds_prepared.to_pandas()
# Obtain the final labels as a dict, to later include these as suggestions
labels_prepared = {}
for idx in df_prepared.index:
labels = [id_to_label[label_id] for label_id in df_prepared['labels'][idx]]
labels_prepared[df_prepared['id'][idx]] = labels
# Add labels to the dataset and keep only the relevant columns
def add_labels(ex):
labels = []
for label in _CLASS_NAMES:
if ex[label] == 1:
labels.append(label)
ex["labels"] = labels
return ex
ds = ds.map(add_labels)
df = ds.select_columns(["text", "labels", "rater_id", "id"]).to_pandas()
# Create a FeedbackDataset for text classification
feedback_dataset = rg.FeedbackDataset.for_text_classification(labels=_CLASS_NAMES, multi_label=True)
# Create the records with the original responses, and use as suggestions
# the final labels in the "simplified" go_emotions dataset.
records = []
for text, df_text in df.groupby("text"):
responses = []
for rater_id, df_raters in df_text.groupby("rater_id"):
responses.append(
{
"values": {"label": {"value": df_raters["labels"].iloc[0].tolist()}},
"status": "submitted",
"user_id": uuid.UUID(int=rater_id),
}
)
suggested_labels = labels_prepared.get(df_raters["id"].iloc[0], None)
if not suggested_labels:
continue
suggestion = [
{
"question_name": "label",
"value": suggested_labels,
"type": "human",
}
]
records.append(
rg.FeedbackRecord(
fields={"text": df_raters["text"].iloc[0]},
responses=responses,
suggestions=suggestion
)
)
feedback_dataset.add_records(records)
# Push to the hub
feedback_dataset.push_to_huggingface("plaguss/go_emotions_raw")
```
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
This is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection.
#### 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] |
SEACrowd/singgalang | ---
tags:
- named-entity-recognition
language:
- ind
---
# singgalang
Rule-based annotation Indonesian NER Dataset of 48,957 sentences or 1,478,286 tokens.
Annotation conforms the Stanford-NER format (https://stanfordnlp.github.io/CoreNLP/ner.html) for 3 NER tags of Person, Organisation, and Place.
This dataset consists of 41,297, 14,770, and 82,179 tokens of entity (respectively) from over 14, 6, and 5 rules.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@INPROCEEDINGS{8355036,
author={Alfina, Ika and Savitri, Septiviana and Fanany, Mohamad Ivan},
title={Modified DBpedia entities expansion for tagging automatically NER dataset},
booktitle={2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
pages={216-221},
year={2017},
url={https://ieeexplore.ieee.org/document/8355036},
doi={10.1109/ICACSIS.2017.8355036}}
@INPROCEEDINGS{7872784,
author={Alfina, Ika and Manurung, Ruli and Fanany, Mohamad Ivan},
booktitle={2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)},
title={DBpedia entities expansion in automatically building dataset for Indonesian NER},
year={2016},
pages={335-340},
doi={10.1109/ICACSIS.2016.7872784}}
```
## License
You can use this dataset for free. You don't need our permission to use it. Please cite our paper if your work uses our data in your publication.
Please note that you are not allowed to create a copy of this dataset and share it publicly in your own repository without our permission.
## Homepage
[https://github.com/ir-nlp-csui/singgalang](https://github.com/ir-nlp-csui/singgalang)
### NusaCatalogue
For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue) |
lukarape/whisper_erebuni_3h | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: phone
dtype: string
- name: id
dtype: string
- name: department
dtype: string
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 919832754.85
num_examples: 1290
download_size: 1075433573
dataset_size: 919832754.85
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/amagi_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of amagi/天城/天城 (Kantai Collection)
This is the dataset of amagi/天城/天城 (Kantai Collection), containing 293 images and their tags.
The core tags of this character are `brown_hair, long_hair, hair_ornament, hair_flower, ponytail, brown_eyes, mole, mole_under_eye, breasts, large_breasts, hair_between_eyes`, which are pruned in this dataset.
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)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 293 | 244.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amagi_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 293 | 173.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amagi_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 607 | 337.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amagi_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 293 | 225.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amagi_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 607 | 425.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/amagi_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/amagi_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, cleavage_cutout, flower, midriff, solo, looking_at_viewer, bare_shoulders, navel, miniskirt, smile, blush, crop_top, thighhighs, zettai_ryouiki |
| 1 | 7 |  |  |  |  |  | 1girl, cleavage_cutout, flower, looking_at_viewer, midriff, miniskirt, navel, open_mouth, smile, solo, leaf_hair_ornament, machinery, maple_leaf, flight_deck, full_body, green_thighhighs, shikigami, zettai_ryouiki |
| 2 | 9 |  |  |  |  |  | 1girl, flower, furisode, looking_at_viewer, obi, solo, open_mouth, wide_sleeves, :d, alternate_costume, floral_print |
| 3 | 5 |  |  |  |  |  | 1girl, flower, looking_at_viewer, smile, solo, upper_body, kimono, leaf_hair_ornament, simple_background, blush, open_mouth, white_background |
| 4 | 8 |  |  |  |  |  | 1girl, flower, looking_at_viewer, smile, solo, green_bikini, navel, cleavage, open_mouth, bare_shoulders, collarbone, underboob, upper_body |
| 5 | 6 |  |  |  |  |  | alternate_costume, detached_collar, fake_animal_ears, pantyhose, playboy_bunny, rabbit_ears, wrist_cuffs, 1girl, bowtie, cleavage, flower, solo, bare_shoulders, blush, looking_at_viewer, rabbit_tail, strapless_leotard |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage_cutout | flower | midriff | solo | looking_at_viewer | bare_shoulders | navel | miniskirt | smile | blush | crop_top | thighhighs | zettai_ryouiki | open_mouth | leaf_hair_ornament | machinery | maple_leaf | flight_deck | full_body | green_thighhighs | shikigami | furisode | obi | wide_sleeves | :d | alternate_costume | floral_print | upper_body | kimono | simple_background | white_background | green_bikini | cleavage | collarbone | underboob | detached_collar | fake_animal_ears | pantyhose | playboy_bunny | rabbit_ears | wrist_cuffs | bowtie | rabbit_tail | strapless_leotard |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------|:---------|:----------|:-------|:--------------------|:-----------------|:--------|:------------|:--------|:--------|:-----------|:-------------|:-----------------|:-------------|:---------------------|:------------|:-------------|:--------------|:------------|:-------------------|:------------|:-----------|:------|:---------------|:-----|:--------------------|:---------------|:-------------|:---------|:--------------------|:-------------------|:---------------|:-----------|:-------------|:------------|:------------------|:-------------------|:------------|:----------------|:--------------|:--------------|:---------|:--------------|:--------------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | | X | X | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | X | | X | X | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | | X | X | | | | X | X | | | | X | X | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | | X | | X | X | X | X | | X | | | | | X | | | | | | | | | | | | | | X | | | | X | X | X | X | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | X | | X | X | X | | | | X | | | | | | | | | | | | | | | | X | | | | | | | X | | | X | X | X | X | X | X | X | X | X |
|
Yzuygulama/turkishReviews-ds-mini | ---
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1252876.2642514652
num_examples: 3378
- name: validation
num_bytes: 139455.7357485349
num_examples: 376
download_size: 896649
dataset_size: 1392332.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
hkcancor | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- yue
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: hong-kong-cantonese-corpus
pretty_name: The Hong Kong Cantonese Corpus (HKCanCor)
dataset_info:
features:
- name: conversation_id
dtype: string
- name: speaker
dtype: string
- name: turn_number
dtype: int16
- name: tokens
sequence: string
- name: transcriptions
sequence: string
- name: pos_tags_prf
sequence:
class_label:
names:
'0': '!'
'1': '"'
'2': '#'
'3': ''''
'4': ','
'5': '-'
'6': .
'7': '...'
'8': '?'
'9': A
'10': AD
'11': AG
'12': AIRWAYS0
'13': AN
'14': AND
'15': B
'16': BG
'17': BEAN0
'18': C
'19': CENTRE0
'20': CG
'21': D
'22': D1
'23': DG
'24': E
'25': ECHO0
'26': F
'27': G
'28': G1
'29': G2
'30': H
'31': HILL0
'32': I
'33': IG
'34': J
'35': JB
'36': JM
'37': JN
'38': JNS
'39': JNT
'40': JNZ
'41': K
'42': KONG
'43': L
'44': L1
'45': LG
'46': M
'47': MG
'48': MONTY0
'49': MOUNTAIN0
'50': N
'51': N1
'52': NG
'53': NR
'54': NS
'55': NSG
'56': NT
'57': NX
'58': NZ
'59': O
'60': P
'61': PEPPER0
'62': Q
'63': QG
'64': R
'65': RG
'66': S
'67': SOUND0
'68': T
'69': TELECOM0
'70': TG
'71': TOUCH0
'72': U
'73': UG
'74': U0
'75': V
'76': V1
'77': VD
'78': VG
'79': VK
'80': VN
'81': VU
'82': VUG
'83': W
'84': X
'85': XA
'86': XB
'87': XC
'88': XD
'89': XE
'90': XJ
'91': XJB
'92': XJN
'93': XJNT
'94': XJNZ
'95': XJV
'96': XJA
'97': XL1
'98': XM
'99': XN
'100': XNG
'101': XNR
'102': XNS
'103': XNT
'104': XNX
'105': XNZ
'106': XO
'107': XP
'108': XQ
'109': XR
'110': XS
'111': XT
'112': XV
'113': XVG
'114': XVN
'115': XX
'116': Y
'117': YG
'118': Y1
'119': Z
- name: pos_tags_ud
sequence:
class_label:
names:
'0': DET
'1': PRON
'2': VERB
'3': NOUN
'4': ADJ
'5': PUNCT
'6': INTJ
'7': ADV
'8': V
'9': PART
'10': X
'11': NUM
'12': PROPN
'13': AUX
'14': CCONJ
'15': ADP
splits:
- name: train
num_bytes: 5746381
num_examples: 10801
download_size: 961514
dataset_size: 5746381
---
# Dataset Card for The Hong Kong Cantonese Corpus (HKCanCor)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://compling.hss.ntu.edu.sg/hkcancor/
- **Repository:** https://github.com/fcbond/hkcancor
- **Paper:** [Luke and Wang, 2015](https://github.com/fcbond/hkcancor/blob/master/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** Luke Kang Kwong
### Dataset Summary
The Hong Kong Cantonese Corpus (HKCanCor) comprise transcribed conversations recorded
between March 1997 and August 1998. It contains recordings of spontaneous speech (51 texts)
and radio programmes (42 texts), which involve 2 to 4 speakers, with 1 text of monologue.
In total, the corpus contains around 230,000 Chinese words. The text is word-segmented (i.e., tokenization is at word-level, and each token can span multiple Chinese characters). Tokens are annotated with part-of-speech (POS) tags and romanised Cantonese pronunciation.
* Romanisation
* Follows conventions set by the Linguistic Society of Hong Kong (LSHK).
* POS
* The tagset used by this corpus extends the one in the Peita-Fujitsu-Renmin Ribao (PRF) corpus (Duan et al., 2000). Extensions were made to further capture Cantonese-specific phenomena.
* To facilitate everyday usage and for better comparability across languages and/or corpora, this dataset also includes the tags mapped to the [Universal Dependencies 2.0](https://universaldependencies.org/u/pos/index.html) format. This mapping references the [PyCantonese](https://github.com/jacksonllee/pycantonese) library.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Yue Chinese / Cantonese (Hong Kong).
## Dataset Structure
This corpus has 10801 utterances and approximately 230000 Chinese words.
There is no predefined split.
### Data Instances
Each instance contains a conversation id, speaker id within that conversation,
turn number, part-of-speech tag for each Chinese word in the PRF format and UD2.0 format,
and the utterance written in Chinese characters as well as its LSHK format romanisation.
For example:
```python
{
'conversation_id': 'TNR016-DR070398-HAI6V'
'pos_tags_prf': ['v', 'w'],
'pos_tags_ud': ['VERB', 'PUNCT'],
'speaker': 'B',
'transcriptions': ['hai6', 'VQ1'],
'turn_number': 112,
'tokens': ['係', '。']
}
```
### Data Fields
- conversation_id: unique dialogue-level id
- pos_tags_prf: POS tag using the PRF format at token-level
- pos_tag_ud: POS tag using the UD2.0 format at token-level
- speaker: unique speaker id within dialogue
- transcriptions: token-level romanisation in the LSHK format
- turn_number: turn number in dialogue
- tokens: Chinese word or punctuation at token-level
### Data Splits
There are no specified splits in this dataset.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This work is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/deed.ast).
### Citation Information
This corpus was developed by [Luke and Wong, 2015](http://compling.hss.ntu.edu.sg/hkcancor/data/LukeWong_Hong-Kong-Cantonese-Corpus.pdf).
```
@article{luke2015hong,
author={Luke, Kang-Kwong and Wong, May LY},
title={The Hong Kong Cantonese corpus: design and uses},
journal={Journal of Chinese Linguistics},
year={2015},
pages={309-330},
month={12}
}
```
The POS tagset to Universal Dependency tagset mapping is provided by Jackson Lee, as a part of the [PyCantonese](https://github.com/jacksonllee/pycantonese) library.
```
@misc{lee2020,
author = {Lee, Jackson},
title = {PyCantonese: Cantonese Linguistics and NLP in Python},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jacksonllee/pycantonese}},
commit = {1d58f44e1cb097faa69de6b617e1d28903b84b98}
}
```
### Contributions
Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset. |
hushell/graspnet-h5 | ---
license: cc-by-nc-4.0
---
|
dvgodoy/auto-mpg-split | ---
dataset_info:
features:
- name: mpg
dtype: float64
- name: cylinders
dtype: int64
- name: displacement
dtype: float64
- name: horsepower
dtype: float64
- name: weight
dtype: int64
- name: acceleration
dtype: float64
- name: model year
dtype: int64
- name: origin
dtype: int64
- name: car name
dtype: string
splits:
- name: train
num_bytes: 26742.361809045226
num_examples: 318
- name: test
num_bytes: 3363.819095477387
num_examples: 40
- name: valid
num_bytes: 3363.819095477387
num_examples: 40
download_size: 22370
dataset_size: 33470.0
---
# Dataset Card for "auto-mpg-split"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
clue2solve/langchain-docs-modules | ---
license: apache-2.0
language:
- en
tags:
- code
- 'Langchain docs modules '
pretty_name: 'Langchain Docs - Modules '
size_categories:
- 1K<n<10K
--- |
Phando/uspto-full | ---
dataset_info:
features:
- name: PatentNumber
dtype: string
- name: Year
dtype: int64
- name: reactions
dtype: string
- name: canonical_reactions
dtype: string
splits:
- name: train
num_bytes: 519191703
num_examples: 1808937
download_size: 144493447
dataset_size: 519191703
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "uspto-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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