datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
yzhuang/autotree_automl_bank-marketing_gosdt_l512_d3_sd2 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float64
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float64
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 5538400000
num_examples: 100000
- name: validation
num_bytes: 553840000
num_examples: 10000
download_size: 809008458
dataset_size: 6092240000
---
# Dataset Card for "autotree_automl_bank-marketing_gosdt_l512_d3_sd2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bigscience-data/roots_ca_wikinews | ---
language: ca
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ca_wikinews
# wikinews_filtered
- Dataset uid: `wikinews_filtered`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0307 % of total
- 0.0701 % of ar
- 0.3036 % of pt
- 0.0271 % of en
- 0.0405 % of fr
- 0.2119 % of indic-ta
- 0.0081 % of zh
- 0.0510 % of es
- 0.0725 % of ca
### BigScience processing steps
#### Filters applied to: ar
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ar
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: pt
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_pt
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: en
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_en
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: fr
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_indic-ta
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_300
#### Filters applied to: zh
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_zhs
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: es
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_es
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- filter_wiki_non_text_type
- dedup_document
- filter_remove_empty_docs
- split_sentences_ca
- dedup_template_soft
- replace_newline_with_space
- filter_small_docs_bytes_1024
|
AsAHuman/ForNAI | ---
license: unknown
---
|
kanak8278/focus_persona_selection | ---
dataset_info:
features:
- name: dialogID
dtype: string
- name: utterance
dtype: int64
- name: old_hit_knowledge
dtype: string
- name: old_query
dtype: string
- name: label
dtype: int64
- name: persona1
dtype: string
- name: persona2
dtype: string
- name: persona3
dtype: string
- name: persona4
dtype: string
- name: persona5
dtype: string
- name: persona6
dtype: string
- name: ground_knowledge
dtype: string
- name: query
dtype: string
- name: hit_knowledge
dtype: string
- name: persona_candidates
dtype: string
- name: persona_grounding
dtype: string
splits:
- name: test
num_bytes: 11122674
num_examples: 8644
- name: validation
num_bytes: 11162186
num_examples: 8641
- name: train
num_bytes: 72558975
num_examples: 55658
download_size: 42539563
dataset_size: 94843835
---
# Dataset Card for "focus_persona_selection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/lmind_hotpot_train5000_eval5000_v1_doc | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: all_docs
path: data/all_docs-*
- split: all_docs_eval
path: data/all_docs_eval-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
splits:
- name: train_qa
num_bytes: 864508
num_examples: 5000
- name: train_recite_qa
num_bytes: 5350190
num_examples: 5000
- name: eval_qa
num_bytes: 813536
num_examples: 5000
- name: eval_recite_qa
num_bytes: 5394796
num_examples: 5000
- name: all_docs
num_bytes: 8524332
num_examples: 18224
- name: all_docs_eval
num_bytes: 8523131
num_examples: 18224
- name: train
num_bytes: 8524332
num_examples: 18224
- name: validation
num_bytes: 8524332
num_examples: 18224
download_size: 28418740
dataset_size: 46519157
---
# Dataset Card for "lmind_hotpot_train5000_eval5000_v1_doc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joelniklaus/brazilian_court_decisions | ---
annotations_creators:
- found
language_creators:
- found
language:
- pt
license:
- 'other'
multilinguality:
- monolingual
pretty_name: predicting-brazilian-court-decisions
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for predicting-brazilian-court-decisions
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/lagefreitas/predicting-brazilian-court-decisions
- **Paper:** Lage-Freitas, A., Allende-Cid, H., Santana, O., & Oliveira-Lage, L. (2022). Predicting Brazilian Court
Decisions. PeerJ. Computer Science, 8, e904–e904. https://doi.org/10.7717/peerj-cs.904
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
The dataset is a collection of 4043 *Ementa* (summary) court decisions and their metadata from
the *Tribunal de Justiça de Alagoas* (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled
according to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset
supports the task of Legal Judgment Prediction.
### Supported Tasks and Leaderboards
Legal Judgment Prediction
### Languages
Brazilian Portuguese
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test) for each configuration.
### Data Fields
The dataset contains the following fields:
- `process_number`: A number assigned to the decision by the court
- `orgao_julgador`: Judging Body: one of '1ª Câmara Cível', '2ª Câmara Cível', '3ª Câmara Cível', 'Câmara Criminal', '
Tribunal Pleno', 'Seção Especializada Cível'
- `publish_date`: The date, when the decision has been published (14/12/2018 - 03/04/2019). At that time (in 2018-2019),
the scraping script was limited and not configurable to get data based on date range. Therefore, only the data from
the last months has been scraped.
- `judge_relator`: Judicial panel
- `ementa_text`: Summary of the court decision
- `decision_description`: **Suggested input**. Corresponds to ementa_text - judgment_text - unanimity_text. Basic
statistics (number of words): mean: 119, median: 88, min: 12, max: 1400
- `judgment_text`: The text used for determining the judgment label
- `judgment_label`: **Primary suggested label**. Labels that can be used to train a model for judgment prediction:
- `no`: The appeal was denied
- `partial`: For partially favourable decisions
- `yes`: For fully favourable decisions
- removed labels (present in the original dataset):
- `conflito-competencia`: Meta-decision. For example, a decision just to tell that Court A should rule this case
and not Court B.
- `not-cognized`: The appeal was not accepted to be judged by the court
- `prejudicada`: The case could not be judged for any impediment such as the appealer died or gave up on the
case for instance.
- `unanimity_text`: Portuguese text to describe whether the decision was unanimous or not.
- `unanimity_label`: **Secondary suggested label**. Unified labels to describe whether the decision was unanimous or
not (in some cases contains ```not_determined```); they can be used for model training as well (Lage-Freitas et al.,
2019).
### Data Splits
The data has been split randomly into 80% train (3234), 10% validation (404), 10% test (405).
There are two tasks possible for this dataset.
#### Judgment
Label Distribution
| judgment | train | validation | test |
|:----------|---------:|-----------:|--------:|
| no | 1960 | 221 | 234 |
| partial | 677 | 96 | 93 |
| yes | 597 | 87 | 78 |
| **total** | **3234** | **404** | **405** |
#### Unanimity
In this configuration, all cases that have `not_determined` as `unanimity_label` can be removed.
Label Distribution
| unanimity_label | train | validation | test |
|:-----------------|----------:|---------------:|---------:|
| not_determined | 1519 | 193 | 201 |
| unanimity | 1681 | 205 | 200 |
| not-unanimity | 34 | 6 | 4 |
| **total** | **3234** | **404** | **405** |
## Dataset Creation
### Curation Rationale
This dataset was created to further the research on developing models for predicting Brazilian court decisions that are
also able to predict whether the decision will be unanimous.
### Source Data
The data was scraped from *Tribunal de Justiça de Alagoas* (TJAL, the State Supreme Court of Alagoas (Brazil).
#### Initial Data Collection and Normalization
*“We developed a Web scraper for collecting data from Brazilian courts. The scraper first searched for the URL that
contains the list of court cases […]. Then, the scraper extracted from these HTML files the specific case URLs and
downloaded their data […]. Next, it extracted the metadata and the contents of legal cases and stored them in a CSV file
format […].”* (Lage-Freitas et al., 2022)
#### Who are the source language producers?
The source language producer are presumably attorneys, judges, and other legal professionals.
### Annotations
#### Annotation process
The dataset was not annotated.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The court decisions might contain sensitive information about individuals.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton
Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset
consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the
dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,
differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to
have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the
original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to
the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
Lage-Freitas, A., Allende-Cid, H., Santana Jr, O., & Oliveira-Lage, L. (2019). Predicting Brazilian court decisions:
- "In Brazil [...] lower court judges decisions might be appealed to Brazilian courts (*Tribiunais de Justiça*) to be
reviewed by second instance court judges. In an appellate court, judges decide together upon a case and their
decisions are compiled in Agreement reports named *Acóordãos*."
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation
Information*. Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch)
; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch)
; [Github](https://github.com/kapllan)).
### Licensing Information
No licensing information was provided for this dataset. However, please make sure that you use the dataset according to
Brazilian law.
### Citation Information
```
@misc{https://doi.org/10.48550/arxiv.1905.10348,
author = {Lage-Freitas, Andr{\'{e}} and Allende-Cid, H{\'{e}}ctor and Santana, Orivaldo and de Oliveira-Lage, L{\'{i}}via},
doi = {10.48550/ARXIV.1905.10348},
keywords = {Computation and Language (cs.CL),FOS: Computer and information sciences,Social and Information Networks (cs.SI)},
publisher = {arXiv},
title = {{Predicting Brazilian court decisions}},
url = {https://arxiv.org/abs/1905.10348},
year = {2019}
}
```
```
@article{Lage-Freitas2022,
author = {Lage-Freitas, Andr{\'{e}} and Allende-Cid, H{\'{e}}ctor and Santana, Orivaldo and Oliveira-Lage, L{\'{i}}via},
doi = {10.7717/peerj-cs.904},
issn = {2376-5992},
journal = {PeerJ. Computer science},
keywords = {Artificial intelligence,Jurimetrics,Law,Legal,Legal NLP,Legal informatics,Legal outcome forecast,Litigation prediction,Machine learning,NLP,Portuguese,Predictive algorithms,judgement prediction},
language = {eng},
month = {mar},
pages = {e904--e904},
publisher = {PeerJ Inc.},
title = {{Predicting Brazilian Court Decisions}},
url = {https://pubmed.ncbi.nlm.nih.gov/35494851 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044329/},
volume = {8},
year = {2022}
}
```
### Contributions
Thanks to [@kapllan](https://github.com/kapllan) and [@joelniklaus](https://github.com/joelniklaus) for adding this
dataset.
|
monroex/login-screen-dataset-simple | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 14037383.0
num_examples: 61
download_size: 13702093
dataset_size: 14037383.0
---
# Dataset Card for "login-screen-dataset-simple"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_beomi__KoAlpaca-Polyglot-5.8B | ---
pretty_name: Evaluation run of beomi/KoAlpaca-Polyglot-5.8B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [beomi/KoAlpaca-Polyglot-5.8B](https://huggingface.co/beomi/KoAlpaca-Polyglot-5.8B)\
\ 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 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_beomi__KoAlpaca-Polyglot-5.8B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T22:10:39.400321](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__KoAlpaca-Polyglot-5.8B/blob/main/results_2023-09-22T22-10-39.400321.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.01541526845637584,\n\
\ \"em_stderr\": 0.0012616582904353766,\n \"f1\": 0.054131711409395974,\n\
\ \"f1_stderr\": 0.0017182561984205931,\n \"acc\": 0.24544616266538535,\n\
\ \"acc_stderr\": 0.007403949973545061\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.01541526845637584,\n \"em_stderr\": 0.0012616582904353766,\n\
\ \"f1\": 0.054131711409395974,\n \"f1_stderr\": 0.0017182561984205931\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225404\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.49013417521704816,\n \"acc_stderr\": 0.014049749833367582\n\
\ }\n}\n```"
repo_url: https://huggingface.co/beomi/KoAlpaca-Polyglot-5.8B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|arc:challenge|25_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T10_40_00.706474
path:
- '**/details_harness|drop|3_2023-09-17T10-40-00.706474.parquet'
- split: 2023_09_22T22_10_39.400321
path:
- '**/details_harness|drop|3_2023-09-22T22-10-39.400321.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T22-10-39.400321.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T10_40_00.706474
path:
- '**/details_harness|gsm8k|5_2023-09-17T10-40-00.706474.parquet'
- split: 2023_09_22T22_10_39.400321
path:
- '**/details_harness|gsm8k|5_2023-09-22T22-10-39.400321.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T22-10-39.400321.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hellaswag|10_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:52:43.613378.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T12:52:43.613378.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T12:52:43.613378.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T10_40_00.706474
path:
- '**/details_harness|winogrande|5_2023-09-17T10-40-00.706474.parquet'
- split: 2023_09_22T22_10_39.400321
path:
- '**/details_harness|winogrande|5_2023-09-22T22-10-39.400321.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T22-10-39.400321.parquet'
- config_name: results
data_files:
- split: 2023_07_18T12_52_43.613378
path:
- results_2023-07-18T12:52:43.613378.parquet
- split: 2023_09_17T10_40_00.706474
path:
- results_2023-09-17T10-40-00.706474.parquet
- split: 2023_09_22T22_10_39.400321
path:
- results_2023-09-22T22-10-39.400321.parquet
- split: latest
path:
- results_2023-09-22T22-10-39.400321.parquet
---
# Dataset Card for Evaluation run of beomi/KoAlpaca-Polyglot-5.8B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/beomi/KoAlpaca-Polyglot-5.8B
- **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 [beomi/KoAlpaca-Polyglot-5.8B](https://huggingface.co/beomi/KoAlpaca-Polyglot-5.8B) 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 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_beomi__KoAlpaca-Polyglot-5.8B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T22:10:39.400321](https://huggingface.co/datasets/open-llm-leaderboard/details_beomi__KoAlpaca-Polyglot-5.8B/blob/main/results_2023-09-22T22-10-39.400321.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.01541526845637584,
"em_stderr": 0.0012616582904353766,
"f1": 0.054131711409395974,
"f1_stderr": 0.0017182561984205931,
"acc": 0.24544616266538535,
"acc_stderr": 0.007403949973545061
},
"harness|drop|3": {
"em": 0.01541526845637584,
"em_stderr": 0.0012616582904353766,
"f1": 0.054131711409395974,
"f1_stderr": 0.0017182561984205931
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225404
},
"harness|winogrande|5": {
"acc": 0.49013417521704816,
"acc_stderr": 0.014049749833367582
}
}
```
### 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] |
lewtun/music_genres | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: song_id
dtype: int64
- name: genre_id
dtype: int64
- name: genre
dtype: string
splits:
- name: test
num_bytes: 1978321742.996
num_examples: 5076
- name: train
num_bytes: 7844298868.902
num_examples: 19909
download_size: 9793244255
dataset_size: 9822620611.898
---
# Dataset Card for "music_genres"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ShuaKang/real_world_train | ---
dataset_info:
features:
- name: goal_image
dtype: image
- name: obs_image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 382291755.75
num_examples: 3505
download_size: 382258795
dataset_size: 382291755.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "real_world_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ali-vakil/PMP_QA_dataset_not_clean | ---
license: apache-2.0
task_categories:
- question-answering
language:
- en
pretty_name: PMP test QA
size_categories:
- n<1K
---
Print ("This dataset includes 580 Q/A records, not separated, not cleaned yet.-I am working to clean it up-therefore I'm not sharing it publicly.")
|
zoiz/test | ---
license: afl-3.0
---
|
jogambee/greninja | ---
license: openrail
---
|
RandomCatLover/logs_for_demo_nlp | ---
license: apache-2.0
---
|
FODASESEE/EU | ---
license: openrail
---
|
mangesh13/water_bottle_images | ---
license: openrail
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 134256.0
num_examples: 9
download_size: 136065
dataset_size: 134256.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CVasNLPExperiments/OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_5046_OE | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0
num_bytes: 919899
num_examples: 5046
download_size: 356578
dataset_size: 919899
---
# Dataset Card for "OK_VQA_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_5046_OE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fathyshalab/reklamation24_supermaerkte-drogerien | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: label_name
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 234328
num_examples: 410
- name: test
num_bytes: 58653
num_examples: 103
download_size: 0
dataset_size: 292981
---
# Dataset Card for "reklamation24_supermaerkte-drogerien"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aggr8/flickr_hf | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3895293840.104
num_examples: 29751
download_size: 4123842521
dataset_size: 3895293840.104
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
happydale/testonly | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 26390405
num_examples: 51325
- name: val
num_bytes: 1777077
num_examples: 3500
download_size: 5652485
dataset_size: 28167482
---
# Dataset Card for "testonly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MasterThesisCBS/NorEval | ---
license: cc-by-4.0
language:
- 'no'
- nb
tags:
- instruction-finetuning
pretty_name: NB Alpaca Norwegian Bokmål
task_categories:
- text-generation
dataset_info:
features:
- name: Category
dtype: string
- name: SubCategory
dtype: string
- name: Instruction
dtype: string
- name: Input
dtype: string
- name: Output
dtype: string
splits:
- name: train
num_bytes: 101921
num_examples: 288
download_size: 56767
dataset_size: 101921
---
# NorEval
NorEval is a self-curated dataset to evaluate instruction-following LLMs, seeking to evaluate the models in nine categories: Language, Code, Mathematics, Classification, Communication & Marketing, Medical, General Knowledge, and Business Operations |
mii-llm/quesiti-universitari | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 5021246
num_examples: 2700
download_size: 2770346
dataset_size: 5021246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "quesiti-universitari"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
murodbek/uz-text-classification | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Avto
'1': Ayollar
'2': Dunyo
'3': Foto
'4': Iqtisodiyot
'5': Jamiyat
'6': Jinoyat
'7': Madaniyat
'8': O‘zbekiston
'9': Pazandachilik
'10': Qonunchilik
'11': Salomatlik
'12': Siyosat
'13': Sport
'14': Texnologiya
splits:
- name: train
num_bytes: 892446788
num_examples: 410200
- name: validation
num_bytes: 111174020
num_examples: 51275
- name: test
num_bytes: 111663893
num_examples: 51275
download_size: 593012664
dataset_size: 1115284701
task_categories:
- text-classification
- fill-mask
- text-generation
language:
- uz
tags:
- uz
- news
pretty_name: UzbekTextClassification
size_categories:
- 100K<n<1M
---
# Dataset Card for "uzbek_news"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/elmurod1202/TextClassification](https://github.com/elmurod1202/TextClassification)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [https://arxiv.org/pdf/2302.14494](https://arxiv.org/pdf/2302.14494)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 593 MB
- **Size of the generated dataset:** 522 MB
- **Total amount of disk used:** 1115 MB
### Dataset Summary
Multi-label text classification dataset for Uzbek language and some sourcode for analysis. This repository contains the code and dataset used for text classification analysis for the Uzbek language. The dataset consists text data from 9 Uzbek news websites and press portals that included news articles and press releases. These websites were selected to cover various categories such as politics, sports, entertainment, technology, and others. In total, we collected 512,750 articles with over 120 million words accross 15 distinct categories, which provides a large and diverse corpus for text classification. It is worth noting that all the text in the corpus is written in the Latin script.
Please refer to [paper](https://arxiv.org/pdf/2302.14494) and [GitHub repository](https://github.com/elmurod1202/TextClassification) for further details.
Disclaimer: The team releasing UzTextClassification did not write this model card. This is HuggingFace version of the dataset that is created for mainly easy to access usage. The original dataset files can be accessed and downloaded from https://doi.org/10.5281/zenodo.7677431
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 593 MB
- **Size of the generated dataset:** 522 MB
- **Total amount of disk used:** 1115 MB
An example of 'train' looks as follows.
```
{
"label": 14,
"text": "Samsung Galaxy S21 Ultra eng yaxshi kamerofonlar reytingida 17-o‘rinni egalladi DxOMark laboratoriyasi mutaxassislari Samsung Galaxy S21 Ultra’ning asosiy ..."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
- `label`: a classification label, with possible values including 'Avto' (0), 'Ayollar' (1), 'Dunyo' (2), 'Foto' (3), 'Iqtisodiyot' (4), 'Jamiyat' (5), 'Jinoyat' (6), 'Madaniyat' (7), 'O‘zbekiston' (8), 'Pazandachilik' (9), 'Qonunchilik' (10), 'Salomatlik' (11), 'Siyosat' (12), 'Sport' (13), 'Texnologiya' (14).
### Data Splits
| name |train |validation|test|
|-------|-----:|---------:|---:|
|default|410200|51275|51275|
### Citation Information
```
@proceedings{kuriyozov_elmurod_2023_7677431,
title = {{Text classification dataset and analysis for Uzbek
language}},
year = 2023,
publisher = {Zenodo},
month = feb,
doi = {10.5281/zenodo.7677431},
url = {https://doi.org/10.5281/zenodo.7677431}
}
```
### Contact
For any questions or issues related to the dataset or code, please contact [elmurod1202@urdu.uz, ulugbek.salaev@urdu.uz]. |
lemoneresearch/cgi | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- tax
- llm
- fiscal
- cgi
- Code Général des Impôts
source_datasets:
- original
pretty_name: Code Général des Impôts (CGI)
task_categories:
- text-generation
- table-question-answering
- summarization
- conversational
size_categories:
- 1K<n<10K
---
# Code Général des Impôts, non-instruct (11-12-2023)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for tax practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `version`: `string`, denoting the version associated with the element.
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `complexity`: `int`, reflecting the degree of abstraction requested from the LLM (Legal Language Model). A value of 1 represents an instruction grounded in authoritative text, while 2 introduces added complexity or abstraction.
- `created_at`: `date`, capturing the date and time of the document's creation.
- `updated_at`: `date`, detailing the most recent update's date and time.
- `expiration`: `date`, delineating the expiration date of the legal information.
- `status`: `string`, specifying the application status of the law.
- `coming_into_force`: `date`, signifying the date when the legal information becomes enforceable.
- `language`: `string`, describing the language in which the legal information is presented.
- `length`: `int`, offering information regarding the length of the legal content.
- `source`: `string`, representing the source from which the legal information originated.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Citing this project
If you use this code in your research, please use the following BibTeX entry.
```BibTeX
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Code Général des Impôts, non-instruct (11-12-2023)},
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/cgi}},
year = {2023}
}
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
Mediocreatmybest/Example | ---
license: cc0-1.0
---
Inital example files to test an easy way to store and manage data text and images.
Created from python scripts available at https://github.com/mediocreatmybest/gaslightingeveryone/tree/main/tools
Creation script: https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/images2parq.py
Extraction script: https://github.com/mediocreatmybest/gaslightingeveryone/blob/main/tools/parq2folder.py
|
senhorsapo/kuzco | ---
license: openrail
---
|
Asimok/KGLQA-KeySentenceSelect-QuALITY | ---
configs:
- config_name: normal
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-QuALITY/train.jsonl"
- split: dev
path:
- "KGLQA-KeySentenceSelect-QuALITY/dev.jsonl"
- split: test
path:
- "KGLQA-KeySentenceSelect-QuALITY/test.jsonl"
- config_name: instruct
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-QuALITY-instruct/train.jsonl"
- split: dev
path:
- "KGLQA-KeySentenceSelect-QuALITY-instruct/dev.jsonl"
- config_name: raw
data_files:
- split: train
path:
- "KGLQA-KeySentenceSelect-QuALITY-raw/*.train"
- split: dev
path:
- "KGLQA-KeySentenceSelect-QuALITY-raw/*.dev.jsonl"
- split: test
path:
- "KGLQA-KeySentenceSelect-QuALITY-raw/*.test.jsonl"
---
|
Jacque008/fwd_all | ---
dataset_info:
features:
- name: id
dtype: int64
- name: origin
dtype: string
- name: id_fwd
dtype: int64
- name: refer
dtype: string
- name: forward
dtype: string
splits:
- name: train
num_bytes: 23602934
num_examples: 10246
- name: test
num_bytes: 12978603
num_examples: 4963
download_size: 10076440
dataset_size: 36581537
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
joey234/mmlu-clinical_knowledge-neg-prepend | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: ori_prompt
dtype: string
- name: neg_prompt
dtype: string
- name: fewshot_context_neg
dtype: string
- name: fewshot_context_ori
dtype: string
splits:
- name: dev
num_bytes: 6643
num_examples: 5
- name: test
num_bytes: 1915838
num_examples: 265
download_size: 205749
dataset_size: 1922481
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-clinical_knowledge-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Zorigami/abimages | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 639313.0
num_examples: 13
download_size: 639921
dataset_size: 639313.0
---
# Dataset Card for "abimages"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Lorenzob/db_dir_astra | ---
license: apache-2.0
---
|
mask-distilled-one-sec-cv12/chunk_74 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1274242448
num_examples: 250244
download_size: 1299212227
dataset_size: 1274242448
---
# Dataset Card for "chunk_74"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
micsell/hebrew_kan_sentence110000 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: language
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1875212865.0
num_examples: 10000
download_size: 1874451480
dataset_size: 1875212865.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
weneatt/eddie | ---
license: apache-2.0
---
|
Dampish/Dante_data | ---
license: cc-by-nc-4.0
---
|
Helsinki-NLP/opus_tedtalks | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- hr
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: OpusTedtalks
dataset_info:
config_name: en-hr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- hr
splits:
- name: train
num_bytes: 15249309
num_examples: 86348
download_size: 9932158
dataset_size: 15249309
configs:
- config_name: en-hr
data_files:
- split: train
path: en-hr/train-*
---
# Dataset Card for OpusTedtalks
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/TedTalks.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from https://wit3.fbk.eu. The corpus is compiled by Željko Agić and is taken from http://lt.ffzg.hr/zagic provided under the CC-BY-NC-SA license. This corpus is sentence aligned for both language pairs. The documents were collected and aligned using the Hunalign algorithm.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[CC-BY-NC-SA license]<http://creativecommons.org/licenses/by-sa/3.0/>
### Citation Information
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. |
lshowway/Wikipedia_5gram_less_orders | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3754120542
num_examples: 1893405
download_size: 2356370630
dataset_size: 3754120542
---
# Dataset Card for "Wikipedia_5gram_less_orders"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_85 | ---
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: 32383665
num_examples: 3801
download_size: 7608191
dataset_size: 32383665
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_85"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sunbird/salt-studio-lug | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: audio
sequence: float32
- name: audio_language
dtype: string
- name: is_studio
dtype: bool
- name: speaker_id
dtype: string
- name: sample_rate
dtype: int64
splits:
- name: train
num_bytes: 880655669
num_examples: 2395
- name: dev
num_bytes: 18852996
num_examples: 50
- name: test
num_bytes: 16076881
num_examples: 43
download_size: 454989170
dataset_size: 915585546
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
|
liuyanchen1015/MULTI_VALUE_rte_comparative_more_and | ---
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: test
num_bytes: 8386
num_examples: 16
- name: train
num_bytes: 9240
num_examples: 22
download_size: 24173
dataset_size: 17626
---
# Dataset Card for "MULTI_VALUE_rte_comparative_more_and"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JesseGuerrero/darkan-core | ---
license: mit
---
|
Ahren09/DTDGVisualization | ---
license: artistic-2.0
---
|
GreeneryScenery/SheepsLAIONSquare | ---
dataset_info:
features:
- name: url
dtype: string
- name: prompt
dtype: string
- name: image
dtype: image
- name: square_image
dtype: image
splits:
- name: train
num_bytes: 27470879234.0
num_examples: 29000
download_size: 27459163664
dataset_size: 27470879234.0
---
# Dataset Card for "SheepsLAIONSquare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/wikiclir_pl | ---
pretty_name: '`wikiclir/pl`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `wikiclir/pl`
The `wikiclir/pl` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/pl).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,234,316
- `queries` (i.e., topics); count=693,656
- `qrels`: (relevance assessments); count=2,471,360
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/wikiclir_pl', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ...}
queries = load_dataset('irds/wikiclir_pl', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/wikiclir_pl', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{sasaki-etal-2018-cross,
title = "Cross-Lingual Learning-to-Rank with Shared Representations",
author = "Sasaki, Shota and
Sun, Shuo and
Schamoni, Shigehiko and
Duh, Kevin and
Inui, Kentaro",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2073",
doi = "10.18653/v1/N18-2073",
pages = "458--463"
}
```
|
benayas/atis_chatgpt_20pct_v2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 440716
num_examples: 4455
download_size: 147370
dataset_size: 440716
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
olm/olm-wikipedia-20221220 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: OLM December 2022 Wikipedia
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- pretraining
- language modelling
- wikipedia
- web
task_categories: []
task_ids: []
---
# Dataset Card for OLM December 2022 Wikipedia
Pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from a December 2022 Wikipedia snapshot. |
Nexdata/2_People_New_Zealand_English_Average_Tone_Speech_Synthesis_Corpus | ---
license: cc-by-nc-nd-4.0
---
## Description
2 People - New Zealand English Average Tone Speech Synthesis Corpus. It is recorded by rn native New Zealanders, with authentic accent. The phoneme coverage is balanced. Professional phonetician participates in the annotation. It precisely matches with the research and development needs of the speech synthesis.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1350?source=Huggingface
## Format
48,000Hz, 24bit, uncompressed wav, mono channel;
## Recording environment
professional recording studio;
## Recording content
customer service and general;
## Speaker
new zealanders, 1 male and 1 female;
## Annotation
word and phoneme transcription, four-level prosodic boundary annotation;
## Device
microphone;
## Language
New Zealand English;
## Application scenarios
speech synthesis.
# Licensing Information
Commercial License
|
autoevaluate/autoeval-staging-eval-project-be45ecbd-7284774 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: echarlaix/bart-base-cnn-r2-18.7-d23-hybrid
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# 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: echarlaix/bart-base-cnn-r2-18.7-d23-hybrid
* Dataset: cnn_dailymail
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
Falcon2006VN/pascal-code-generation-18mb | ---
license: mit
---
|
hlillemark/flores200_eng_input_scaffolding_mix3_mt5 | ---
dataset_info:
features:
- name: id
dtype: int32
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 9290803477
num_examples: 10240000
- name: val
num_bytes: 3827042
num_examples: 5000
- name: test
num_bytes: 7670994
num_examples: 10000
download_size: 4445111273
dataset_size: 9302301513
---
# Dataset Card for "flores200_eng_input_scaffolding_mix3_mt5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
likhith231/wmt_en_ro_7000 | ---
dataset_info:
features:
- name: translation
struct:
- name: en
dtype: string
- name: ro
dtype: string
splits:
- name: train
num_bytes: 735075
num_examples: 5000
- name: validation
num_bytes: 283467
num_examples: 1000
- name: test
num_bytes: 274013
num_examples: 1000
download_size: 704483
dataset_size: 1292555
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_103 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1399896696.0
num_examples: 272778
download_size: 1408939647
dataset_size: 1399896696.0
---
# Dataset Card for "chunk_103"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arubenruben/portuguese_europarl | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': pt-PT
'1': pt-BR
splits:
- name: train
num_bytes: 276595020
num_examples: 7547
- name: test
num_bytes: 80381927
num_examples: 1887
download_size: 193710364
dataset_size: 356976947
---
# Dataset Card for "portuguese_europarl_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
skubis/laser | ---
license: gpl-3.0
---
|
ZakeeQureshi/prompt | ---
license: openrail
---
|
brackozi/Resume | ---
license: mit
---
|
Nexdata/21299_Images_of_Human_Body_and_Face_Segmentation_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
21,299 Images of Human Body and Face Segmentation Data. The data includes indoor scenes and outdoor scenes. The data covers female people and male people. The race distribution includes Asian, black race and Caucasian. The age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The dataset diversity includes multiple scenes, ages, races, postures, and appendages. In terms of annotation, we adpoted pixel-wise segmentation annotations on human face, the five sense organs, body and appendages. The data can be used for tasks such as human body segmentation.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1188?source=Huggingface
## Data size
21,299 images
## Race distribution
Asian, Caucasian, Black
## Gender distribution
male and female
## Age distribution
ranging from teenager to the elderly, the middle-aged and young people are the majorities
## Collecting environment
including indoor and outdoor scenes
## Data diversity
multiple scenes, ages, races, postures, and appendages
## Data format
the image data is in .jpg or .png format, the annotation file is in .json format
## Annotation content
segmentation annotation of human face, the five sense organs, body and appendages
## Accuracy
the mask edge location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; the annotation part (id) is
# Licensing Information
Commercial License
|
graphs-datasets/ZINC | ---
license: unknown
dataset_info:
features:
- name: node_feat
sequence:
sequence: int64
- name: edge_index
sequence:
sequence: int64
- name: edge_attr
sequence:
sequence: int64
- name: 'y'
sequence: float64
- name: num_nodes
dtype: int64
splits:
- name: train
num_bytes: 376796456
num_examples: 220011
- name: test
num_bytes: 8538528
num_examples: 5000
- name: validation
num_bytes: 41819628
num_examples: 24445
download_size: 20636253
dataset_size: 427154612
task_categories:
- graph-ml
---
# Dataset Card for ZINC
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://zinc15.docking.org/)**
- **[Repository](https://www.dropbox.com/s/feo9qle74kg48gy/molecules.zip?dl=1):**:
- **Paper:**: ZINC 15 – Ligand Discovery for Everyone (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/)
### Dataset Summary
The `ZINC` dataset is a "curated collection of commercially available chemical compounds prepared especially for virtual screening" (Wikipedia).
### Supported Tasks and Leaderboards
`ZINC` should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE.
The associated leaderboard is here: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-regression-on-zinc).
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | big |
| #graphs | 220011 |
| average #nodes | 23.15 |
| average #edges | 49.81 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset, and follows the provided data splits.
This information can be found back using
```python
from torch_geometric.datasets import ZINC
dataset = ZINC(root = '', split='train') # valid, test
```
## Additional Information
### Licensing Information
The dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset.
### Citation Information
```bibtex
@article{doi:10.1021/acs.jcim.5b00559,
author = {Sterling, Teague and Irwin, John J.},
title = {ZINC 15 – Ligand Discovery for Everyone},
journal = {Journal of Chemical Information and Modeling},
volume = {55},
number = {11},
pages = {2324-2337},
year = {2015},
doi = {10.1021/acs.jcim.5b00559},
note ={PMID: 26479676},
URL = {
https://doi.org/10.1021/acs.jcim.5b00559
},
eprint = {
https://doi.org/10.1021/acs.jcim.5b00559
}
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. |
ksanjeev284/NikoBellic | ---
license: mit
---
|
fahernandez/bonito_privacy_qa_sft_data | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2093268
num_examples: 7830
- name: test
num_bytes: 530688
num_examples: 1958
download_size: 1061562
dataset_size: 2623956
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
ahdsoft/synTran-fa_base_on_pquad | ---
license: mit
---
|
iamkaikai/IMPRESSIONISM-ART | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 24298294.0
num_examples: 434
download_size: 24120501
dataset_size: 24298294.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "IMPRESSIONISM-ART"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/furen_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of furen (Fire Emblem)
This is the dataset of furen (Fire Emblem), containing 466 images and their tags.
The core tags of this character are `green_hair, long_hair, green_eyes, hair_ornament, drill_hair, bow`, 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 | 466 | 497.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 466 | 304.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 977 | 605.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 466 | 451.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 977 | 832.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/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/furen_fireemblem',
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, closed_mouth, full_body, garreg_mach_monastery_uniform, long_sleeves, solo, black_footwear, simple_background, smile, white_background, knee_boots, pantyhose, black_dress |
| 1 | 11 |  |  |  |  |  | 1girl, closed_mouth, garreg_mach_monastery_uniform, smile, solo, long_sleeves, upper_body, simple_background, hairclip, white_background |
| 2 | 27 |  |  |  |  |  | 1girl, garreg_mach_monastery_uniform, solo, long_sleeves, open_mouth, upper_body, simple_background, white_background |
| 3 | 5 |  |  |  |  |  | 1girl, bell, cat_tail, dress, solo, alternate_costume, long_sleeves, tail_ornament, white_gloves, cat_ears, open_mouth, halloween_costume, holding, paw_gloves, paw_pose, paw_print, smile |
| 4 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, mosaic_censoring, solo_focus, looking_at_viewer, hairclip, open_mouth, pov, cum, handjob, licking_penis, tongue_out |
| 5 | 12 |  |  |  |  |  | 1girl, 1boy, hetero, open_mouth, vaginal, blush, penis, sex, breasts, solo_focus, cum_in_pussy, nipples, censored, spread_legs, completely_nude, sweat |
| 6 | 9 |  |  |  |  |  | 1girl, nipples, completely_nude, navel, solo, blush, pussy, looking_at_viewer, closed_mouth, small_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | full_body | garreg_mach_monastery_uniform | long_sleeves | solo | black_footwear | simple_background | smile | white_background | knee_boots | pantyhose | black_dress | upper_body | hairclip | open_mouth | bell | cat_tail | dress | alternate_costume | tail_ornament | white_gloves | cat_ears | halloween_costume | holding | paw_gloves | paw_pose | paw_print | 1boy | blush | hetero | mosaic_censoring | solo_focus | looking_at_viewer | pov | cum | handjob | licking_penis | tongue_out | vaginal | penis | sex | breasts | cum_in_pussy | nipples | censored | spread_legs | completely_nude | sweat | navel | pussy | small_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:------------|:--------------------------------|:---------------|:-------|:-----------------|:--------------------|:--------|:-------------------|:-------------|:------------|:--------------|:-------------|:-----------|:-------------|:-------|:-----------|:--------|:--------------------|:----------------|:---------------|:-----------|:--------------------|:----------|:-------------|:-----------|:------------|:-------|:--------|:---------|:-------------------|:-------------|:--------------------|:------|:------|:----------|:----------------|:-------------|:----------|:--------|:------|:----------|:---------------|:----------|:-----------|:--------------|:------------------|:--------|:--------|:--------|:----------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | | X | X | X | | X | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 27 |  |  |  |  |  | X | | | X | X | X | | X | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 5 | 12 |  |  |  |  |  | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | | | |
| 6 | 9 |  |  |  |  |  | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | X | | | X | | X | X | X |
|
tokeron/Piyyut | ---
license: afl-3.0
language:
- heb
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
tags:
- metaphor-detection
viewer: true
---
|
tanvirsrbd1/custom_dataset | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 707678
num_examples: 267
download_size: 112485
dataset_size: 707678
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
luisroque/instruct-python-llama2-500k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1046127202
num_examples: 501349
download_size: 530786217
dataset_size: 1046127202
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- en
pretty_name: Instruct Python 500k
size_categories:
- 100K<n<1M
---
# Fine-tuning Instruct Llama2 Stack Overflow Python Q&A
## Transformed Dataset
### Objective
The transformed dataset is designed for fine-tuning LLMs to improve Python coding assistance by focusing on high-quality content from Stack Overflow. It has around 500k instructions.
### Structure
- **Question-Answer Pairing**: Questions and answers are paired using the `ParentId` linkage.
- **Quality Focus**: Only top-rated answers for each question are retained.
- **HTML Tag Removal**: All HTML tags in the content are removed.
- **Combined Question Field**: Each question's title and body are merged.
- **Filtering**: Entries with negative scores or those not containing Python code structures are excluded.
Final columns:
- `score_question`
- `score_answer`
- `question`
- `answer`
### Llama2 Transformation
The dataset has been transformed to match the Llama2 prompt structure, which is relevant for the model's fine-tuning. The format is the following:
`<s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_message }} [/INST]`
Where:
- `system_prompt` gives context or instructions to the model.
- `user_message` is the user's query following the system prompt, expecting a particular response from the model.
This structure ensures the training aligns with Llama2's expectations, optimizing the fine-tuning quality.
## Original Dataset
The dataset contains questions and answers from Stack Overflow with the `python` tag, covering the period from August 2, 2008, to October 19, 2016.
## License
All contributions are under the [CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). Attribution is required. The original dataset was posted [here](https://www.kaggle.com/datasets/stackoverflow/pythonquestions).
Keep in touch: [LinkedIn](https://www.linkedin.com/in/luisbrasroque/) |
Iceclear/StableSR-TestSets | ---
license: other
license_name: ntu-slab-license
license_link: https://github.com/IceClear/StableSR/blob/main/LICENSE.txt
task_categories:
- image-to-image
---
# StableSR TestSets Card
These test sets are used associated with the StableSR, available [here](https://github.com/IceClear/StableSR).
## Data Details
- **Developed by:** Jianyi Wang
- **Data type:** Synthetic and real-world test sets for image super-resolution
- **License:** [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt)
- **Data Description:** The test sets are used to reproduce the metric results shown in [Paper](https://arxiv.org/abs/2305.07015).
- **Resources for more information:** [GitHub Repository](https://github.com/IceClear/StableSR).
- **Cite as:**
@InProceedings{wang2023exploiting,
author = {Wang, Jianyi and Yue, Zongsheng and Zhou, Shangchen and Chan, Kelvin CK and Loy, Chen Change},
title = {Exploiting Diffusion Prior for Real-World Image Super-Resolution},
booktitle = {arXiv preprint arXiv:2305.07015},
year = {2023},
}
# Uses
Please refer to [S-Lab License 1.0](https://github.com/IceClear/StableSR/blob/main/LICENSE.txt)
We currently provide the following test sets:
- DIV2K_Val: 3000 synthetic data pairs on the validation of [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) generated used the same degradation used for training StableSR.
- RealSR Val: Center-cropped data pairs on [RealSRv3](https://github.com/csjcai/RealSR).
- DRealSR Val: Center-cropped data pairs on [DRealSR](https://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution).
- DPED Val: Center-cropped LQ-only data on [DPED](https://github.com/aiff22/DPED).
## Evaluation Results
See [Paper](https://arxiv.org/abs/2305.07015) for details. |
conceptual_captions | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: conceptual-captions
pretty_name: Conceptual Captions
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: caption
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 623230370
num_examples: 3318333
- name: validation
num_bytes: 2846024
num_examples: 15840
download_size: 0
dataset_size: 626076394
- config_name: unlabeled
features:
- name: image_url
dtype: string
- name: caption
dtype: string
splits:
- name: train
num_bytes: 584520156
num_examples: 3318333
- name: validation
num_bytes: 2698726
num_examples: 15840
download_size: 567211172
dataset_size: 587218882
- config_name: labeled
features:
- name: image_url
dtype: string
- name: caption
dtype: string
- name: labels
sequence: string
- name: MIDs
sequence: string
- name: confidence_scores
sequence: float64
splits:
- name: train
num_bytes: 1199330856
num_examples: 2007090
download_size: 1282463277
dataset_size: 1199330856
---
# Dataset Card for Conceptual Captions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/)
- **Repository:** [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions)
- **Paper:** [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/)
- **Leaderboard:** [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard
- **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)
### Dataset Summary
Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions.
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("conceptual_captions")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available [here](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard). Official submission output captions are scored against the reference captions from the hidden test set using [this](https://github.com/tylin/coco-caption) implementation of the CIDEr (primary), ROUGE-L and SPICE metrics.
### Languages
All captions are in English.
## Dataset Structure
### Data Instances
#### `unlabeled`
Each instance in this configuration represents a single image with a caption:
```
{
'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
'caption': 'a very typical bus station'
}
```
#### `labeled`
Each instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores:
```
{
'image_url': 'https://thumb1.shutterstock.com/display_pic_with_logo/261388/223876810/stock-vector-christmas-tree-on-a-black-background-vector-223876810.jpg',
'caption': 'christmas tree on a black background .',
'labels': ['christmas tree', 'christmas decoration', 'font', 'text', 'graphic design', 'illustration','interior design', 'tree', 'christmas eve', 'ornament', 'fir', 'plant', 'pine', 'pine family', 'graphics'],
'MIDs': ['/m/025nd', '/m/05fc9mj', '/m/03gq5hm', '/m/07s6nbt', '/m/03c31', '/m/01kr8f', '/m/0h8nzzj', '/m/07j7r', '/m/014r1s', '/m/05ykl4', '/m/016x4z', '/m/05s2s', '/m/09t57', '/m/01tfm0', '/m/021sdg'],
'confidence_scores': [0.9818305373191833, 0.952756941318512, 0.9227379560470581, 0.8524878621101379, 0.7597672343254089, 0.7493422031402588, 0.7332468628883362, 0.6869218349456787, 0.6552258133888245, 0.6357356309890747, 0.5992692708969116, 0.585474967956543, 0.5222904086112976, 0.5113164782524109, 0.5036579966545105]
}
```
### Data Fields
#### `unlabeled`
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
#### `labeled`
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
- `labels`: A sequence of machine-generated labels obtained using the [Google Cloud Vision API](https://cloud.google.com/vision).
- `MIDs`: A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry.
- `confidence_scores`: A sequence of confidence scores denoting how likely the corresponing labels are present on the image.
### Data Splits
#### `unlabeled`
The basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs.
#### `labeled`
The labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the `unlabeled` config.
## Dataset Creation
### Curation Rationale
From the paper:
> In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.
### Source Data
#### Initial Data Collection and Normalization
From the homepage:
>For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable.
>
>To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters.
>
>We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through.
>
>In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage.
>
>The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR.
>
>We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent.
>
>Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”.
#### Who are the source language producers?
Not specified.
### Annotations
#### Annotation process
Annotations are extracted jointly with the images using the automatic pipeline.
#### Who are the annotators?
Not specified.
### 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
Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of
Google LLC ("Google") as the data source would be appreciated. The dataset is
provided "AS IS" without any warranty, express or implied. Google disclaims all
liability for any damages, direct or indirect, resulting from the use of the
dataset.
### Citation Information
```bibtex
@inproceedings{sharma2018conceptual,
title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
booktitle = {Proceedings of ACL},
year = {2018},
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) and [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
autoevaluate/autoeval-staging-eval-project-29af5371-7254761 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: elastic/distilbert-base-cased-finetuned-conll03-english
dataset_name: conll2003
dataset_config: conll2003
dataset_split: validation
col_mapping:
tokens: tokens
tags: ner_tags
metrics: []
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: elastic/distilbert-base-cased-finetuned-conll03-english
* Dataset: conll2003
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@douwekiela](https://huggingface.co/douwekiela) for evaluating this model. |
hippocrates/CochranePLS_zero_test | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
splits:
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- name: valid
num_bytes: 14262021
num_examples: 3568
- name: test
num_bytes: 1917804
num_examples: 480
download_size: 14250050
dataset_size: 30441846
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
hotal/motivation_alpaca | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: system
dtype: string
splits:
- name: train
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download_size: 63040
dataset_size: 227789
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
suneeln-duke/duke_qac | ---
dataset_info:
features:
- name: Question
dtype: string
- name: Context
dtype: string
- name: Answer
dtype: string
splits:
- name: train
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- name: val
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num_examples: 67
download_size: 1202393
dataset_size: 3259377
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
---
|
autoevaluate/autoeval-eval-jeffdshen__redefine_math2_8shot-jeffdshen__redefine_mat-af4c71-1853163410 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/redefine_math2_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: jeffdshen/redefine_math2_8shot
dataset_config: jeffdshen--redefine_math2_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-2.7b_eval
* Dataset: jeffdshen/redefine_math2_8shot
* Config: jeffdshen--redefine_math2_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. |
EthioNLP/EthioSenti | ---
license: mit
---
|
AYUNTAMIENTOVERA/autotrain-data-8a00-9wrj-ig4m | ---
dataset_info:
features:
- name: id
dtype: int64
- name: grupo
dtype: string
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dtype: string
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dtype: int64
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- name: burbuja1
dtype: string
- name: enlace1
dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: float64
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dtype: string
splits:
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num_examples: 264
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num_bytes: 164025
num_examples: 264
download_size: 174744
dataset_size: 328050
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-8a00-9wrj-ig4m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Binaryy/reddit-images-with-captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: 'Unnamed: 0'
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 792439583.0
num_examples: 783
download_size: 791608849
dataset_size: 792439583.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "reddit-images-with-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tsungtao/tmp | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1520138.0
num_examples: 1
download_size: 1521322
dataset_size: 1520138.0
---
# Dataset Card for "tmp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NickKolok/regs-epicphotogasm-conv | ---
license: agpl-3.0
---
|
CShorten/CORD19-Chunk-2 | ---
license: afl-3.0
---
|
vikp/hermes_labeled | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
- name: rendered
dtype: string
- name: quality_prob
dtype: float64
- name: learning_prob
dtype: float64
splits:
- name: train
num_bytes: 624932230
num_examples: 242831
download_size: 285527683
dataset_size: 624932230
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hermes_labeled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chtan0212/test1 | ---
license: apache-2.0
task_categories:
- token-classification
language:
- en
pretty_name: test 1 pretty name
size_categories:
- 10K<n<100K
--- |
mstz/chess_rock_vs_pawn | ---
language:
- en
tags:
- chess
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Chess Rock VS Pawn
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- chess
license: cc
---
# Chess Rock VS Pawn
The [Chess Rock VS Pawn dataset](https://archive-beta.ics.uci.edu/dataset/22/chess+king+rook+vs+king+pawn) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|--------------------------|
| chess | Binary classification | Can the white piece win? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/chess_rock_vs_pawn")["train"]
``` |
ArchangelBelial/ESG_analysis | ---
license: apache-2.0
---
|
bharathmuppa/BAK | ---
license: gpl-3.0
---
|
ovior/twitter_dataset_1713118585 | ---
dataset_info:
features:
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dtype: string
- name: tweet_content
dtype: string
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dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 2508509
num_examples: 7878
download_size: 1395089
dataset_size: 2508509
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
RaviNaik/CulturaX-Kn | ---
language:
- kn
license: mit
size_categories:
- 1M<n<10M
task_categories:
- text-generation
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: string
- name: url
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 10347179458
num_examples: 1352142
download_size: 3976072715
dataset_size: 10347179458
---
This is a filtered version of the [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset only containing samples of Kannada language.
The dataset contains total of 1352142 samples.
### Dataset Structure:
```python
{
"text": ...,
"timestamp": ...,
"url": ...,
"source": "mc4" | "OSCAR-xxxx",
}
```
### Data Sample:
```python
{'text': "ಭಟ್ಕಳ : ತಂದೆ ತಾಯಿ ಸ್ಮರಣಾರ್ಥ ; ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಣೆ | Vartha Bharati- ವಾರ್ತಾ ಭಾರತಿ\nಮುದರಂಗಡಿ ಬಿಜೆಪಿ ಗ್ರಾಪಂ ಸದಸ್ಯರ ವಿರುದ್ಧ ಪ್ರತಿಭಟನೆ\nಹೋಮ್ ಕ್ವಾರಂಟೈನ್ ನಿಯಮ ಉಲ್ಲಂಘನೆ: ಪ್ರಕರಣ ದಾಖಲು\nಭಟ್ಕಳ : ತಂದೆ ತಾಯಿ ಸ್ಮರಣಾರ್ಥ ; ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಣೆ\nವಾರ್ತಾ ಭಾರತಿ Jun 19, 2019, 10:52 PM IST\nಭಟ್ಕಳ : ತಾಲೂಕಿನ ಹುರುಳಿಸಾಲಿನ ನಿವಾಸಿಗಳಾದ ವೃತ್ತಿಯಲ್ಲಿ ಶಿಕ್ಷಕರಾದ ವೆಂಕಟೇಶ ನಾರಾಯಣ ನಾಯ್ಕ ಪಟೇಲರಮನೆ ಇವರ ತಂದೆ ತಾಯಿಗಳ ಅಕಾಲಿಕ ಮರಣದಿಂದ ಅವರ ಮರಣ ದಿನದ ಸವಿನೆನಪಿಗಾಗಿ ಕಳೆದ 9 ವರ್ಷದಿಂದ ಇಲ್ಲಿನ ಶಾಲಾ ಮಕ್ಕಳಿಗೆ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸುತ್ತಾ ಬಂದಿದ್ದು, ಮಂಗಳವಾರದಂದು ಇಲ್ಲಿನ ಸರಕಾರಿ ಹಿರಿಯ ಪ್ರಾಥಮಿಕ ಶಾಲೆ ಮುಟ್ಟಳ್ಳಿಗೆ ತೆರಳಿ ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ನೋಟ್ ವಿತರಿಸಿದರು.\nನೋಟ್ ಬುಕ್ ವಿತರಣೆ ಮಾಡಿ ಮಾತನಾಡಿದ ಶಿಕ್ಷಕ ವೆಂಕಟೇಶ ನಾಯ್ಕ 'ವಿದ್ಯಾರ್ಥಿಗಳ ಭವಿಷ್ಯದ ದಿಸೆಯಿಂದ ಹಾಗೂ ತಂದೆ-ತಾಯಿಗಳ ಸವಿನೆನಪಿಗಾಗಿ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸಲಾಗುತ್ತಿದೆ. ದುಡಿಮೆಯ ಒಂದು ಭಾಗವನ್ನು ಸಮಾಜಮುಖಿ ಕೆಲಕ್ಕೆ ಪ್ರತಿ ವರ್ಷ, ನನ್ನ ಮಡದಿ ಜಯಲಕ್ಷ್ಮೀ ನಾಯ್ಕ ಅವರ ಸಹಕಾರದಿಂದ ಕುಟುಂಬದವರ ಸಹಕಾರದಿಂದ ಈ ಕಾರ್ಯ ಮಾಡುತ್ತಿದ್ದೇನೆ. ಸಮಾಜದಲ್ಲಿ ಎಷ್ಟೇ ಎತ್ತರಕ್ಕೆ ಬೆಳೆದರು ತಂದೆತಾಯಿಗಳ ಹಾಗೂ ಗುರುಗಳ ಋಣ ತೀರಿಸಲು ಸಾಧ್ಯವಿಲ್ಲ. ನಾನು ಮಾಡಿದ ಕಾರ್ಯವನ್ನು ಮುಂದಿನ ದಿನದಲ್ಲಿ ದುಡಿಯುವ ವೇಳೆ ನಿಮ್ಮದಿಂದಾಗುವಷ್ಟು ಸಹಾಯ ಸೇವೆ ಮಾಡಿ ಎಂದು ಕರೆ ನೀಡಿದರು.\nನಂತರ ದಂತ ವೈದ್ಯರಾದ ಡಾ. ರವಿ ಮಾತನಾಡಿ ನಮ್ಮ ಸಮಾಜದಲ್ಲಿ ಇಂತಹ ವ್ಯಕ್ತಿಗಳಿರುವದರಿಂದ ನಮ್ಮ ಸಮಾಜವು ಏಳಿಗೆಯತ್ತ ಮುಖ ಮಾಡುತ್ತದೆ. ಮಕ್ಕಳಾದ ನಾವು ಎಲ್ಲೇ ಇರಿಬಹುದು ಹೇಗೆ ಇರಿಬಹುದ ಆದರೆ ತಂದೆ ತಾಯಿಗಳು ನಮಗೆ ಮಾಡಿರುವ ತ್ಯಾಗಕ್ಕೆ ನಾವು ಋಣ ತೀರಿಸಲು ಸಾಧ್ಯವಾಗದಿದ್ದರು ಇಂತಹ ಕೆಲಸ ಮಾಡಿ ಅವರ ತ್ಯಾಗಕ್ಕೆ ಪ್ರತಿಫಲ ಕೊಟ್ಟಂತೆ ಆಗುತ್ತದೆ ಅಂದು ಕಿವಿ ಮಾತನ್ನು ಮಕ್ಕಳಿಗೆ ಹೇಳಿದರು.\nಈ ಸಂಧರ್ಭದಲ್ಲಿ ಮುಟ್ಟಳ್ಳಿ ಶಾಲಾ ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸಿದರು.\nಈಗಿನ ಇಲೆಕ್ಟ್ರಾನಿಕ ಜೀವನ ಶೈಲಿಯಲ್ಲಿ ಸಾಕಿದ ತಂದೆ ತಾಯಿಗಳನ್ನು ಅನಾಥಾಶ್ರಾಮಕ್ಕೊ ಅಥವಾ ದಾರಿಯ ಮೇಲೋ ಮನೆಯಿಂದ ಹೊರಗೆ ಹಾಕುವ ಮಕ್ಕಳ ನಡುವೆ ಅವರ ಅಕಾಲಿಕ ಮರಣದಿಂದ ನೊಂದು ಅವರ ಸವಿನೆನಪನ್ನು ಉತ್ತಮ ಕಾರ್ಯ ಮಾಡುವುದರೊಂದಿಗೆ ಸಾರ್ಥಕತೆಯನ್ನು ಮೆರೆದಿದ್ದಾರೆ.\nಈ ಸಂಧರ್ಭದಲ್ಲಿ ಶಾಲೆಯ ಎಸ್.ಡಿ. ಎಂ ಅಧ್ಯಕ್ಷರಾದ ವೆಂಕಟೇಶ ನಾಯ್ಕ, ರಾಜ್ಯ ಸರಕಾರಿ ನೌಕರರ ಸಂಘ ಸದಸ್ಯ ಬಿ.ಕೆ.ನಾಯ್ಕ, ಶಿಕ್ಷಕ ಸಿ.ಡಿ.ಪಡುವಣಿ, ಗಜಾನನ ನಾಯ್ಕ ಮುಖ್ಯ ಶಿಕ್ಷಕರು ವೆಂಕಟೇಶ್ ದೇವಡಿಗ್ ಶಿಕ್ಷಕರು ಉಪಸ್ಥಿತರಿದ್ದರು.",
'timestamp': '2020/07/07 13:00:41',
'url': 'http://www.varthabharati.in/article/karavali/196595',
'source': 'mC4'}
```
### Use with Datasets
```python
from datasets import load_dataset
ds = load_dataset("RaviNaik/CulturaX-Kn")
```
|
ekazuki/text_to_french_parliament_group_beta | ---
dataset_info:
features:
- name: text
dtype: string
- name: group
dtype: string
splits:
- name: train
num_bytes: 734.6666666666666
num_examples: 38
- name: test
num_bytes: 193.33333333333334
num_examples: 10
download_size: 10606
dataset_size: 928.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
liuyanchen1015/MULTI_VALUE_cola_remove_det_definite | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 27792
num_examples: 405
- name: test
num_bytes: 28810
num_examples: 427
- name: train
num_bytes: 248882
num_examples: 3721
download_size: 147481
dataset_size: 305484
---
# Dataset Card for "MULTI_VALUE_cola_remove_det_definite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
divyasharma0795/AppleVisionPro_Tweets | ---
license: mit
task_categories:
- text-classification
- translation
language:
- en
tags:
- Sentiment Analysis
- Tweets
- Product Performance Analysis
pretty_name: Apple Vision Pro Tweets
size_categories:
- 10K<n<100K
---
# Apple Vision Pro Tweets Dataset
## Overview
The Apple Vision Pro Tweets Dataset is a collection of tweets related to Apple Vision Pro from January 01 2024 to March 16 2024, scraped from [X](https://twitter.com/home) using the Twitter [API](https://developer.twitter.com/en/products/twitter-api). The dataset includes various attributes associated with each tweet, such as the tweet text, author information, engagement metrics, and metadata.
## Content
- *id*: Unique identifier for each tweet.
- *tweetText*: The text content of the tweet.
- *tweetURL*: URL link to the tweet.
- *type*: Type of tweet (e.g., original tweet, retweet).
- *tweetAuthor*: Name of the tweet author.
- *handle*: Twitter handle of the tweet author.
- *replyCount*: Number of replies to the tweet.
- *quoteCount*: Number of quotes (retweets with comments) of the tweet.
- *retweetCount*: Number of retweets of the tweet.
- *likeCount*: Number of likes (favorites) of the tweet.
- *views*: Number of views of the tweet (if available).
- *bookmarkCount*: Number of bookmarks (if available) of the tweet.
- *createdAt*: Timestamp indicating when the tweet was created.
## Dataset Format
The dataset is provided in `parquet` format. Each row represents a single tweet, and columns contain various attributes associated with the tweet.
## Dataset Size
The dataset contains a total of 26,704 tweets related to Apple Vision Pro, with 13 features
## Data Collection
The tweets were collected using the Twitter API by searching for
- the hashtag *#AppleVisionPro*
- Search term *Apple Vision Pro*
The data collection process involved retrieving tweets that match the search criteria and extracting relevant information such as the tweet text, handle, engagement metrics, and metadata.
## Data Usage
The data can be imported directly from HuggingFace using the following code:
```py
from datasets import load_dataset
dataset = load_dataset("divyasharma0795/AppleVisionPro_Tweets")
```
## Potential Use Cases
- *Sentiment analysis*: Analyze the sentiment expressed in tweets related to Apple Vision Pro.
- *User engagement analysis*: Study user engagement metrics (replies, retweets, likes) to understand audience interaction with Apple Vision Pro content.
- *Trend analysis*: Identify trends and patterns in discussions surrounding Apple Vision Pro on Twitter.
- *New Product Market Sentiment*: Study the sentiments related to a popular tech product before and after launch.
## Citation
If you use this dataset in your research or project, please cite it as follows:
```css
AppleVisionPro_Tweets, Apple Vision Pro Tweets Dataset, 2024. Retrieved from huggingface.co/datasets/divyasharma0795/AppleVisionPro_Tweets
```
## License
The dataset is provided under the [MIT License]. Please refer to the LICENSE file for more details.
## Contact
For any inquiries or feedback regarding the dataset, please contact divya.sharma@duke.edu. |
haoranxu/ALMA-R-Preference | ---
dataset_info:
- config_name: cs-en
features:
- name: translation
struct:
- name: Delta
dtype: float64
- name: alma_cs
dtype: string
- name: alma_cs_kiwi
dtype: float64
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dtype: float64
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dtype: float64
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dtype: string
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dtype: float64
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dtype: float64
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dtype: float64
- name: cs
dtype: string
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dtype: string
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dtype: float64
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dtype: float64
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dtype: float64
- name: gpt4_en_kiwi_xcomet
dtype: float64
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dtype: float64
- name: language_pair
dtype: string
- name: ref_cs_kiwi
dtype: float64
- name: ref_cs_kiwi_xcomet
dtype: float64
- name: ref_cs_xcomet
dtype: float64
- name: ref_en_kiwi
dtype: float64
- name: ref_en_kiwi_xcomet
dtype: float64
- name: ref_en_xcomet
dtype: float64
- name: required_directions
dtype: string
splits:
- name: train
num_bytes: 1973638
num_examples: 2009
download_size: 1407107
dataset_size: 1973638
- config_name: de-en
features:
- name: translation
struct:
- name: Delta
dtype: float64
- name: alma_de
dtype: string
- name: alma_de_kiwi
dtype: float64
- name: alma_de_kiwi_xcomet
dtype: float64
- name: alma_de_xcomet
dtype: float64
- name: alma_en
dtype: string
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dtype: float64
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dtype: float64
- name: language_pair
dtype: string
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dtype: float64
- name: ref_de_kiwi_xcomet
dtype: float64
- name: ref_de_xcomet
dtype: float64
- name: ref_en_kiwi
dtype: float64
- name: ref_en_kiwi_xcomet
dtype: float64
- name: ref_en_xcomet
dtype: float64
- name: required_directions
dtype: string
splits:
- name: train
num_bytes: 2743275
num_examples: 3065
download_size: 1782879
dataset_size: 2743275
- config_name: is-en
features:
- name: translation
struct:
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dtype: float64
- name: alma_en
dtype: string
- name: alma_en_kiwi
dtype: float64
- name: alma_en_kiwi_xcomet
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: string
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dtype: string
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
- name: required_directions
dtype: string
splits:
- name: train
num_bytes: 1990606
num_examples: 2009
download_size: 1385693
dataset_size: 1990606
- config_name: ru-en
features:
- name: translation
struct:
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dtype: float64
- name: alma_en
dtype: string
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: string
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
- name: ref_ru_xcomet
dtype: float64
- name: required_directions
dtype: string
- name: ru
dtype: string
splits:
- name: train
num_bytes: 2666563
num_examples: 2009
download_size: 1627361
dataset_size: 2666563
- config_name: zh-en
features:
- name: translation
struct:
- name: Delta
dtype: float64
- name: alma_en
dtype: string
- name: alma_en_kiwi
dtype: float64
- name: alma_en_kiwi_xcomet
dtype: float64
- name: alma_en_xcomet
dtype: float64
- name: alma_zh
dtype: string
- name: alma_zh_kiwi
dtype: float64
- name: alma_zh_kiwi_xcomet
dtype: float64
- name: alma_zh_xcomet
dtype: float64
- name: en
dtype: string
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dtype: string
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dtype: float64
- name: gpt4_en_kiwi_xcomet
dtype: float64
- name: gpt4_en_xcomet
dtype: float64
- name: gpt4_zh
dtype: string
- name: gpt4_zh_kiwi
dtype: float64
- name: gpt4_zh_kiwi_xcomet
dtype: float64
- name: gpt4_zh_xcomet
dtype: float64
- name: language_pair
dtype: string
- name: ref_en_kiwi
dtype: float64
- name: ref_en_kiwi_xcomet
dtype: float64
- name: ref_en_xcomet
dtype: float64
- name: ref_zh_kiwi
dtype: float64
- name: ref_zh_kiwi_xcomet
dtype: float64
- name: ref_zh_xcomet
dtype: float64
- name: required_directions
dtype: string
- name: zh
dtype: string
splits:
- name: train
num_bytes: 2462110
num_examples: 3065
download_size: 1697255
dataset_size: 2462110
configs:
- config_name: cs-en
data_files:
- split: train
path: cs-en/train-*
- config_name: de-en
data_files:
- split: train
path: de-en/train-*
- config_name: is-en
data_files:
- split: train
path: is-en/train-*
- config_name: ru-en
data_files:
- split: train
path: ru-en/train-*
- config_name: zh-en
data_files:
- split: train
path: zh-en/train-*
license: mit
task_categories:
- translation
language:
- ru
- cs
- zh
- is
- de
---
# Dataset Card for "ALMA-R-Preference"
This is triplet preference data used by [ALMA-R](https://arxiv.org/abs/2401.08417) model.
The triplet preference data, supporting 10 translation directions, is built upon the FLORES-200 development and test data. For each direction, we provide a source sentence along with three translations: one from GPT-4, another from ALMA-13B-LoRA, and a reference translation. For instance, in the English-German pair, our data structure is as follows:
### Sentences:
- de: Original German sentence
- en: Original English sentence
- alma_de: German sentence translated from English by ALMA
- gpt4_de: German sentence translated from English by GPT-4
- alma_en: English sentence translated from German by ALMA
- gpt4_en: English sentence translated from German by GPT-4
### Scores
- alma_en_${Score}: ${Score} of English sentence translated by ALMA
- gpt4_en_${Score}: ${Score} of English sentence translated by GPT4
- ref_en_${Score}: ${Score} of reference English sentence
- alma_de_${Score}: ${Score} of German sentence translated by ALMA
- gpt4_de_${Sscore}: ${Score} of German sentence translated by GPT4
- ref_en_${Score}: ${Score} of reference German sentence
${Score} can be numbers from kiwi ([wmt23-cometkiwi-da-xxl](https://huggingface.co/Unbabel/wmt23-cometkiwi-da-xxl)), xcomet ([XCOMET-XXL](https://huggingface.co/Unbabel/XCOMET-XXL)),
or kiwi_xcomet (average score of kiwi and xcomet).
### Others
- Delta: A value of 0 indicates non-human annotated data or tied evaluations. A postive number suggests that alma_de is better than gpt4_de, vice versa
- required_directions: An empty field implies that this data point can be used for both translation directions. If the string 'en-de' is specified, it indicates that this data point is exclusively for English to German translation
```
@misc{xu2024contrastive,
title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
year={2024},
eprint={2401.08417},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Pavarissy/artery-ultrasound-siit | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 230791779.0
num_examples: 100
download_size: 17454777
dataset_size: 230791779.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "artery-ultrasound-siit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Hallalay/TAiPET | ---
annotations_creators:
- machine-generated
language: []
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- other-my-multilinguality
pretty_name: TAiPET
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- Wallpaper
- StableDiffusion
- img2img
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
Codec-SUPERB/ljspeech_extract_unit | ---
configs:
- config_name: default
data_files:
- split: academicodec_hifi_16k_320d
path: data/academicodec_hifi_16k_320d-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
- split: academicodec_hifi_24k_320d
path: data/academicodec_hifi_24k_320d-*
- split: audiodec_24k_320d
path: data/audiodec_24k_320d-*
- split: dac_16k
path: data/dac_16k-*
- split: dac_24k
path: data/dac_24k-*
- split: dac_44k
path: data/dac_44k-*
- split: encodec_24k
path: data/encodec_24k-*
- split: funcodec_en_libritts_16k_gr1nq32ds320
path: data/funcodec_en_libritts_16k_gr1nq32ds320-*
- split: funcodec_en_libritts_16k_gr8nq32ds320
path: data/funcodec_en_libritts_16k_gr8nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds320
path: data/funcodec_en_libritts_16k_nq32ds320-*
- split: funcodec_en_libritts_16k_nq32ds640
path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
dataset_info:
features:
- name: id
dtype: string
- name: unit
sequence:
sequence: int64
splits:
- name: academicodec_hifi_16k_320d
num_bytes: 138023032
num_examples: 13100
- name: academicodec_hifi_16k_320d_large_uni
num_bytes: 138023032
num_examples: 13100
- name: academicodec_hifi_24k_320d
num_bytes: 206916312
num_examples: 13100
- name: audiodec_24k_320d
num_bytes: 441995480
num_examples: 13100
- name: dac_16k
num_bytes: 863575704
num_examples: 13100
- name: dac_24k
num_bytes: 2440045592
num_examples: 13100
- name: dac_44k
num_bytes: 725202504
num_examples: 13100
- name: encodec_24k
num_bytes: 103785656
num_examples: 13100
- name: funcodec_en_libritts_16k_gr1nq32ds320
num_bytes: 1105887256
num_examples: 13100
- name: funcodec_en_libritts_16k_gr8nq32ds320
num_bytes: 1105887256
num_examples: 13100
- name: funcodec_en_libritts_16k_nq32ds320
num_bytes: 1105874456
num_examples: 13100
- name: funcodec_en_libritts_16k_nq32ds640
num_bytes: 554727192
num_examples: 13100
- name: funcodec_zh_en_16k_nq32ds320
num_bytes: 1105874456
num_examples: 13100
- name: funcodec_zh_en_16k_nq32ds640
num_bytes: 1105874456
num_examples: 13100
- name: speech_tokenizer_16k
num_bytes: 276645464
num_examples: 13100
download_size: 1792164902
dataset_size: 11418337848
---
# Dataset Card for "ljspeech_extract_unit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-samsum-samsum-b534aa-1519254997 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: pszemraj/pegasus-x-large-book-summary
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
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: pszemraj/pegasus-x-large-book-summary
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-v2 | ---
pretty_name: Evaluation run of Phind/Phind-CodeLlama-34B-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T16:59:17.432507](https://huggingface.co/datasets/open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-v2/blob/main/results_2023-10-23T16-59-17.432507.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.32571308724832215,\n\
\ \"em_stderr\": 0.0047993190397442416,\n \"f1\": 0.3870176174496661,\n\
\ \"f1_stderr\": 0.004690520641787959,\n \"acc\": 0.47511298949899267,\n\
\ \"acc_stderr\": 0.01213509959347268\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.32571308724832215,\n \"em_stderr\": 0.0047993190397442416,\n\
\ \"f1\": 0.3870176174496661,\n \"f1_stderr\": 0.004690520641787959\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.23199393479909022,\n \
\ \"acc_stderr\": 0.01162687317509241\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7182320441988951,\n \"acc_stderr\": 0.012643326011852953\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
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_08_29T17_45_53.549865
path:
- '**/details_harness|arc:challenge|25_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T16_59_17.432507
path:
- '**/details_harness|drop|3_2023-10-23T16-59-17.432507.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T16-59-17.432507.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T16_59_17.432507
path:
- '**/details_harness|gsm8k|5_2023-10-23T16-59-17.432507.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T16-59-17.432507.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hellaswag|10_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T17:45:53.549865.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T17:45:53.549865.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T17:45:53.549865.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T16_59_17.432507
path:
- '**/details_harness|winogrande|5_2023-10-23T16-59-17.432507.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T16-59-17.432507.parquet'
- config_name: results
data_files:
- split: 2023_08_29T17_45_53.549865
path:
- results_2023-08-29T17:45:53.549865.parquet
- split: 2023_10_23T16_59_17.432507
path:
- results_2023-10-23T16-59-17.432507.parquet
- split: latest
path:
- results_2023-10-23T16-59-17.432507.parquet
---
# Dataset Card for Evaluation run of Phind/Phind-CodeLlama-34B-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Phind/Phind-CodeLlama-34B-v2
- **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 [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T16:59:17.432507](https://huggingface.co/datasets/open-llm-leaderboard/details_Phind__Phind-CodeLlama-34B-v2/blob/main/results_2023-10-23T16-59-17.432507.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.32571308724832215,
"em_stderr": 0.0047993190397442416,
"f1": 0.3870176174496661,
"f1_stderr": 0.004690520641787959,
"acc": 0.47511298949899267,
"acc_stderr": 0.01213509959347268
},
"harness|drop|3": {
"em": 0.32571308724832215,
"em_stderr": 0.0047993190397442416,
"f1": 0.3870176174496661,
"f1_stderr": 0.004690520641787959
},
"harness|gsm8k|5": {
"acc": 0.23199393479909022,
"acc_stderr": 0.01162687317509241
},
"harness|winogrande|5": {
"acc": 0.7182320441988951,
"acc_stderr": 0.012643326011852953
}
}
```
### 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] |
AdapterOcean/augmentatio-standardized_cluster_3_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 12918145
num_examples: 12676
download_size: 5481927
dataset_size: 12918145
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "augmentatio-standardized_cluster_3_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/hatebr | ---
annotations_creators:
- expert-generated
language:
- pt
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: HateBR - Offensive Language and Hate Speech Dataset in Brazilian Portuguese
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- instagram
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for HateBR - Offensive Language and Hate Speech Dataset in Brazilian Portuguese
## Dataset Description
- **Homepage:** http://143.107.183.175:14581/
- **Repository:** https://github.com/franciellevargas/HateBR
- **Paper:** https://aclanthology.org/2022.lrec-1.777/
- **Leaderboard:**
- **Point of Contact:** https://franciellevargas.github.io/
### Dataset Summary
HateBR is the first large-scale expert annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection on the web and social media. The HateBR corpus was collected from Brazilian Instagram comments of politicians and manually annotated by specialists. It is composed of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level (highly, moderately, and slightly offensive messages), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). Each comment was annotated by three different annotators and achieved high inter-annotator agreement. Furthermore, baseline experiments were implemented reaching 85% of F1-score outperforming the current literature models for the Portuguese language. Accordingly, we hope that the proposed expertly annotated corpus may foster research on hate speech and offensive language detection in the Natural Language Processing area.
**Relevant Links:**
* [**Demo: Brasil Sem Ódio**](http://143.107.183.175:14581/)
* [**MOL - Multilingual Offensive Lexicon Annotated with Contextual Information**](https://github.com/franciellevargas/MOL)
### Supported Tasks and Leaderboards
Hate Speech Detection
### Languages
Portuguese
## Dataset Structure
### Data Instances
```
{'instagram_comments': 'Hipocrita!!',
'offensive_language': True,
'offensiveness_levels': 2,
'antisemitism': False,
'apology_for_the_dictatorship': False,
'fatphobia': False,
'homophobia': False,
'partyism': False,
'racism': False,
'religious_intolerance': False,
'sexism': False,
'xenophobia': False,
'offensive_&_non-hate_speech': True,
'non-offensive': False,
'specialist_1_hate_speech': False,
'specialist_2_hate_speech': False,
'specialist_3_hate_speech': False
}
```
### Data Fields
* **instagram_comments**: Instagram comments.
* **offensive_language**: A classification of comments as either offensive (True) or non-offensive (False).
* **offensiveness_levels**: A classification of comments based on their level of offensiveness, including highly offensive (3), moderately offensive (2), slightly offensive (1) and non-offensive (0).
* **antisemitism**: A classification of whether or not the comment contains antisemitic language.
* **apology_for_the_dictatorship**: A classification of whether or not the comment praises the military dictatorship period in Brazil.
* **fatphobia**: A classification of whether or not the comment contains language that promotes fatphobia.
* **homophobia**: A classification of whether or not the comment contains language that promotes homophobia.
* **partyism**: A classification of whether or not the comment contains language that promotes partyism.
* **racism**: A classification of whether or not the comment contains racist language.
* **religious_intolerance**: A classification of whether or not the comment contains language that promotes religious intolerance.
* **sexism**: A classification of whether or not the comment contains sexist language.
* **xenophobia**: A classification of whether or not the comment contains language that promotes xenophobia.
* **offensive_&_no-hate_speech**: A classification of whether or not the comment is offensive but does not contain hate speech.
* **specialist_1_hate_speech**: A classification of whether or not the comment was annotated by the first specialist as hate speech.
* **specialist_2_hate_speech**: A classification of whether or not the comment was annotated by the second specialist as hate speech.
* **specialist_3_hate_speech**: A classification of whether or not the comment was annotated by the third specialist as hate speech.
### Data Splits
The original authors of the dataset did not propose a standard data split. To address this, we use the [multi-label data stratification technique](http://scikit.ml/stratification.html) implemented at the scikit-multilearn library to propose a train-validation-test split. This method considers all classes for hate speech in the data and attempts to balance the representation of each class in the split.
| name |train|validation|test|
|---------|----:|----:|----:|
|hatebr|4480|1120|1400|
## Considerations for Using the Data
### Discussion of Biases
Please refer to [the HateBR paper](https://aclanthology.org/2022.lrec-1.777/) for a discussion of biases.
### Licensing Information
The HateBR dataset, including all its components, is provided strictly for academic and research purposes. The use of the dataset for any commercial or non-academic purpose is expressly prohibited without the prior written consent of [SINCH](https://www.sinch.com/).
### Citation Information
```
@inproceedings{vargas2022hatebr,
title={HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection},
author={Vargas, Francielle and Carvalho, Isabelle and de G{\'o}es, Fabiana Rodrigues and Pardo, Thiago and Benevenuto, Fabr{\'\i}cio},
booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
pages={7174--7183},
year={2022}
}
```
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset. |
garythung/trashnet | ---
license: mit
---
|
reddit-tools-HF/reddit-bestofredditorupdates-processed | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: score
dtype: int64
- name: date_utc
dtype: timestamp[ns]
- name: title
dtype: string
- name: flair
dtype: string
- name: poster
dtype: string
- name: permalink
dtype: string
- name: embedding
sequence: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 145717576
num_examples: 11310
download_size: 108062085
dataset_size: 145717576
---
# Dataset Card for "reddit-bestofredditorupdates-processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
--- Generated Part of README Below ---
## Dataset Overview
This dataset is based on [derek-thomas/dataset-creator-reddit-bestofredditorupdates](https://huggingface.co/datasets/derek-thomas/dataset-creator-reddit-bestofredditorupdates)
and will add [nomic-ai/nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) embeddings based on the
`content` field.
The goal is to be able to have an automatic and free semantic/neural tool for any subreddit.
The last run was on 2024-04-15 13:00:00 UTC+0000 and updated 0 new rows.
## Creation Details
This is done by triggering [derek-thomas/processing-bestofredditorupdates](https://huggingface.co/spaces/derek-thomas/processing-bestofredditorupdates)
based on a repository update [webhook](https://huggingface.co/docs/hub/en/webhooks) to calculate the embeddings and update the [nomic atlas](https://docs.nomic.ai)
visualization. This is done by this [processing space](https://huggingface.co/spaces/derek-thomas/processing-bestofredditorupdates).
## Update Frequency
The dataset is updated based on a [webhook](https://huggingface.co/docs/hub/en/webhooks) trigger, so each time [derek-thomas/dataset-creator-reddit-bestofredditorupdates](https://huggingface.co/datasets/derek-thomas/dataset-creator-reddit-bestofredditorupdates)
is updated, this dataset will be updated.
## Opt-out
To opt-out of this dataset please make a request in the community tab
|
CyberHarem/kanna_pokemon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kanna/カンナ (Pokémon)
This is the dataset of kanna/カンナ (Pokémon), containing 323 images and their tags.
The core tags of this character are `glasses, breasts, red_hair, long_hair, red_eyes, ponytail, large_breasts, bangs, sidelocks`, 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 | 323 | 289.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanna_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 323 | 179.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanna_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 692 | 345.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanna_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 323 | 261.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanna_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 692 | 462.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kanna_pokemon/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/kanna_pokemon',
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 | 12 |  |  |  |  |  | 1girl, smile, high_heels, jacket, pencil_skirt, holding_poke_ball, formal, looking_at_viewer, pantyhose, poke_ball_(basic), solo, ahoge, cleavage_cutout, pokemon_(creature), white_background, black_footwear, full_body, suit |
| 1 | 24 |  |  |  |  |  | 1girl, ahoge, cleavage_cutout, long_sleeves, smile, solo, looking_at_viewer, pantyhose, black_jacket, sitting, black_skirt, closed_mouth |
| 2 | 19 |  |  |  |  |  | 1girl, purple_skirt, looking_at_viewer, black_shirt, sleeveless_shirt, solo, smile, bracelet, orange_eyes, orange_hair, side_slit, bare_arms, closed_mouth, hand_up, holding_poke_ball, poke_ball_(basic), eyelashes, simple_background |
| 3 | 36 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, thick_thighs, cleavage, beach, huge_breasts, outdoors, sky, cloud, day, ocean, miniskirt, sleeveless_shirt, armpits, sand, shore, sweat, black_shirt, curvy, muscular_female, arms_up, blush, arms_behind_head, purple_skirt |
| 4 | 29 |  |  |  |  |  | nude, 1girl, nipples, solo, navel, smile, looking_at_viewer, pussy, blush |
| 5 | 6 |  |  |  |  |  | 1girl, hetero, nipples, penis, vaginal, 1boy, cum_in_pussy, blush, completely_nude, solo_focus, spread_legs, sweat, uncensored, sex_from_behind, straddling |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | high_heels | jacket | pencil_skirt | holding_poke_ball | formal | looking_at_viewer | pantyhose | poke_ball_(basic) | solo | ahoge | cleavage_cutout | pokemon_(creature) | white_background | black_footwear | full_body | suit | long_sleeves | black_jacket | sitting | black_skirt | closed_mouth | purple_skirt | black_shirt | sleeveless_shirt | bracelet | orange_eyes | orange_hair | side_slit | bare_arms | hand_up | eyelashes | simple_background | thick_thighs | cleavage | beach | huge_breasts | outdoors | sky | cloud | day | ocean | miniskirt | armpits | sand | shore | sweat | curvy | muscular_female | arms_up | blush | arms_behind_head | nude | nipples | navel | pussy | hetero | penis | vaginal | 1boy | cum_in_pussy | completely_nude | solo_focus | spread_legs | uncensored | sex_from_behind | straddling |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------|:---------|:---------------|:--------------------|:---------|:--------------------|:------------|:--------------------|:-------|:--------|:------------------|:---------------------|:-------------------|:-----------------|:------------|:-------|:---------------|:---------------|:----------|:--------------|:---------------|:---------------|:--------------|:-------------------|:-----------|:--------------|:--------------|:------------|:------------|:----------|:------------|:--------------------|:---------------|:-----------|:--------|:---------------|:-----------|:------|:--------|:------|:--------|:------------|:----------|:-------|:--------|:--------|:--------|:------------------|:----------|:--------|:-------------------|:-------|:----------|:--------|:--------|:---------|:--------|:----------|:-------|:---------------|:------------------|:-------------|:--------------|:-------------|:------------------|:-------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 24 |  |  |  |  |  | X | X | | | | | | X | X | | X | X | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 19 |  |  |  |  |  | X | X | | | | X | | X | | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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