Datasets:

librarian-bots

/

dataset_cards_with_metadata

like 8

nzs234/E621_detaset_ID_TAG

nzs234

--- license: mit --- 搭配boxingscorpionbagel/e621-2024使用，将图片ID对应的TAG提取出来，按照by Artists,Copyright,Character,|||,Species,General,mata这样的排序弄好了，字符数大于4的TAG的"_"也替换成了空格

YUDCHI/test-paper-chunked

YUDCHI

--- license: mit ---

rohanth/tensorforests

rohanth

--- license: mit ---

NafishZaldinanda/audio

NafishZaldinanda

--- language: - id dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 7182074.0 num_examples: 51 - name: test num_bytes: 7182074.0 num_examples: 51 download_size: 13887608 dataset_size: 14364148.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

PrasannSinghal/mathprefdataex

PrasannSinghal

--- dataset_info: features: - name: question dtype: string - name: response_j dtype: string - name: response_k dtype: string - name: score_j dtype: float64 - name: score_k dtype: float64 - name: magnitude dtype: float64 splits: - name: train num_bytes: 14826635 num_examples: 43705 download_size: 5962815 dataset_size: 14826635 configs: - config_name: default data_files: - split: train path: data/train-* ---

PrasannSinghal/uniquennprefdataex

PrasannSinghal

--- dataset_info: features: - name: question dtype: string - name: source dtype: string - name: modj dtype: string - name: modk dtype: string - name: tokj dtype: int64 - name: tok dtype: int64 - name: response_j dtype: string - name: response_k dtype: string - name: magnitude dtype: float64 - name: __index_level_0__ dtype: int64 - name: score_j dtype: float64 - name: score_k dtype: float64 splits: - name: train num_bytes: 56210842 num_examples: 49700 download_size: 33538986 dataset_size: 56210842 configs: - config_name: default data_files: - split: train path: data/train-* ---

PrasannSinghal/contrastivedistillprefdataex

PrasannSinghal

--- dataset_info: features: - name: question dtype: string - name: score_j dtype: float64 - name: score_k dtype: float64 - name: response_j dtype: string - name: response_k dtype: string - name: magnitude dtype: float64 splits: - name: train num_bytes: 15244108 num_examples: 84720 download_size: 9046088 dataset_size: 15244108 configs: - config_name: default data_files: - split: train path: data/train-* ---

PrasannSinghal/wordcollectorprefdataex

PrasannSinghal

--- dataset_info: features: - name: question dtype: string - name: source dtype: string - name: modj dtype: string - name: modk dtype: string - name: tokj dtype: int64 - name: tok dtype: int64 - name: response_j dtype: string - name: response_k dtype: string - name: magnitude dtype: float64 - name: __index_level_0__ dtype: int64 - name: score_j dtype: float64 - name: score_k dtype: float64 splits: - name: train num_bytes: 39468454 num_examples: 49884 download_size: 22970913 dataset_size: 39468454 configs: - config_name: default data_files: - split: train path: data/train-* ---

PrasannSinghal/wordcolnounprompts

PrasannSinghal

--- dataset_info: features: - name: question dtype: string splits: - name: train num_bytes: 229822577 num_examples: 340025 download_size: 23959875 dataset_size: 229822577 configs: - config_name: default data_files: - split: train path: data/train-* ---

PrasannSinghal/cdistprompts

PrasannSinghal

--- dataset_info: features: - name: outputs dtype: string - name: question dtype: string splits: - name: train num_bytes: 41585307 num_examples: 286430 download_size: 23795350 dataset_size: 41585307 configs: - config_name: default data_files: - split: train path: data/train-* ---

PrasannSinghal/mathprompts

PrasannSinghal

--- dataset_info: features: - name: question dtype: string - name: outputs dtype: string splits: - name: train num_bytes: 28754891 num_examples: 200000 download_size: 11675816 dataset_size: 28754891 configs: - config_name: default data_files: - split: train path: data/train-* ---

tunis-ai/TunSwitch

tunis-ai

--- language: - ar pretty_name: TunSwitch --- Original dataset has been acquired through the following link : https://zenodo.org/records/8370566 The dataset is not cleaned yet and any contributions are welcome 🤗 ## download instructions ```python from huggingface_hub import snapshot_download snapshot_download(repo_id="tunis-ai/TunSwitch",repo_type="dataset",local_dir=".") ``` ## Information This repo contains the data used to develop and test the Tunisian Arabic Automatic Speech Recognition model developed in the following paper : A. A. Ben Abdallah*, A. Kabboudi, A. Kanoun, and S. Zaiem*, “Leveraging data collection and unsupervised learning for code-switched tunisian arabic automatic speech recognition”, Submitted to ICASSP 2024, vol. * : These two authors have contributed equally. 2023. It contains 4 zipped folders containing audio data : - TunSwitchCS.zip : containing annotated code-switched data. - TunSwitchTO.zip : containing annotated Tunisian-Only data. - weakly_labeled_tn.zip : containing weakly-labeled (or unlabeled) audio data. Audios may contain code-switching, but the current weak labels do not. - test_wavs.zip : contains annotated testing data, divided between a code-switched part and a tunisian-only part. It also contains textual data, used for language modelling, contained in TextData.zip. Finally it also contains a language-detailed annotation of TunSwitchCS in the language_annotation.zip file . More details about the data are available in the paper. The current table are in a SpeechBrain-friendly format, the column path is irrelevant and has to be changed according to your local setting. Please use the provided train-dev-test splits if you work with this dataset. Please cite the aforementioned paper if you use or refer to this dataset. You can find models trained and tested on this dataset Here. Space demos are also available. If you use or refer to this dataset, please cite : ## citation ``` @misc{abdallah2023leveraging, title={Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition}, author={Ahmed Amine Ben Abdallah and Ata Kabboudi and Amir Kanoun and Salah Zaiem}, year={2023}, eprint={2309.11327}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```

AIAT/Pangpuriye-dataset

AIAT

--- license: cc task_categories: - table-question-answering language: - th - en tags: - music - finance - code pretty_name: Thai-SQL_Question_Syntax size_categories: - 100K<n<1M --- # 🤖 [Super AI Engineer Development Program Season 4](https://superai.aiat.or.th/) - Pangpuriye House - Merged Dataset  **Pangpuriye's House Completed Fine-tuning Dataset** This dataset is a completed fine-tuning dataset, which was used in [Pangpuriye's insturction-tuned LLM model](https://huggingface.co/AIAT/Pangpuriye-openthaigpt-1.0.0-7b-chat). The dataset is set under Creative Commons license family. ## Content The dataset consists of 145,793 rows of `input`, `instruction`, and `output`. - `input`: generated schema - `instruction`: (sql extract) and query question in Thai - `output`: code sql ## Uses The dataset is intended to be used as an instruction for fine-tuning table-based QA LLM. The instruction requires some processing before it can be utilized in the process. ## Call our dataset by `datasets` library The following code is an example of calling our dataset via the `datasets` library. ```python from datasets import load_dataset dataset = load_dataset("AIAT/Pangpuriye-dataset") ``` ## Acknowledgements The dataset is collectively stored by the members of Panguriye's house during the LLMs hackathon in Super AI Engineer Development Program Season 4. We thank the organizers of this hackathon, [OpenThaiGPT](https://openthaigpt.aieat.or.th/), [AIAT](https://aiat.or.th/), [NECTEC](https://www.nectec.or.th/en/) and [ThaiSC](https://thaisc.io/) for this challenging task and opportunity to be a part of developing Thai large language model.

AndrewZeng/train_ppo_1to5_mix_equal_syn

AndrewZeng

--- license: apache-2.0 ---

AndrewZeng/train_ppo_1to5_mix_twice_syn

AndrewZeng

--- license: apache-2.0 ---

AndrewZeng/train_ppo_1to5_mix_third_syn

AndrewZeng

--- license: apache-2.0 ---

AndrewZeng/train_ppo_1to5_mix_forth_syn

AndrewZeng

--- license: apache-2.0 ---

thejagstudio/yolov8Fabric

thejagstudio

--- license: apache-2.0 ---

REILX/extracted_tagengo_gpt4

REILX

--- license: apache-2.0 --- ### Original dataset. - https://huggingface.co/datasets/lightblue/tagengo-gpt4 ### Python Code 使用以下Python遍历tagengo-gpt4的jsonl文件中的每一行数据，提取出human和gpt对话内容，并使用instruction和output作为键来保存提取的信息到新的jsonl文件中： （Use the following Python code to iterate through each line of the jsonl file of tagengo-gpt4, extract the dialogue content between human and GPT, and save the extracted information to a new jsonl file using "instruction" and "output" as keys.） ```python import json input_file_path = 'tagengo-gpt4.jsonl' output_file_path = 'extracted_tagengo_gpt4.jsonl' with open(input_file_path, 'r', encoding='utf-8') as input_file, \ open(output_file_path, 'w', encoding='utf-8') as output_file: for line in input_file: data = json.loads(line.strip()) conversations = data.get('conversations', []) extraction = {'instruction': '', 'output': ''} for conv in conversations: if conv['from'] == 'human': extraction['instruction'] = conv['value'] elif conv['from'] == 'gpt': extraction['output'] = conv['value'] output_file.write(json.dumps(extraction, ensure_ascii=False) + '\n') print(f"The conversation content has been extracted, and the results have been saved to'{output_file_path}'。") ```

asolodin/llm-rewrite-prompt-recovery-train

asolodin

--- license: apache-2.0 --- A dataset used for training models for LLM Rewrite Recovery competition. Sources: * https://www.kaggle.com/datasets/dipamc77/3000-rewritten-texts-prompt-recovery-challenge * https://huggingface.co/datasets/vishnupriyavr/wiki-movie-plots-with-summaries * https://huggingface.co/datasets/positivethoughts/rewrite_500_prompts_3k_texts * https://huggingface.co/datasets/kartikay/review-summarizer

sw24/sw24

sw24

--- license: apache-2.0 ---

guymorlan/IsraParlTweet

guymorlan

--- license: cc-by-4.0 language: - he tags: - hebrew - parliament - knesset - twitter viewer: false --- <p style="text-align: center;"> <a href="https://aclanthology.org/2024.lrec-main.819.pdf" style="display: inline-block;"> <img src="http://img.shields.io/badge/paper-ACL--anthology-B31B1B.svg" alt="Paper"> </a> <a href="https://lrec-coling-2024.org/" style="display: inline-block;"> <img src="https://img.shields.io/badge/conference-LREC_COLING_2024-blue" alt="Conference"> </a> <a href="#" style="display: inline-block;"> <img src="https://img.shields.io/badge/license-CC_BY_4.0-orange" alt="Version"> </a> </p> # IsraParlTweet: The Israeli Parliamentary and Twitter Resource Guy Mor-Lan, Effi Levi, Tamir Sheafer, Shaul R. Shenhav. _LREC-COLING 2024_. **Paper**: https://aclanthology.org/2024.lrec-main.819/ **Dataset**: https://huggingface.co/datasets/guymorlan/IsraParlTweet/tree/main For access to Twitter data, please contact authors at *guy.mor -AT- mail.huji.ac.il* ## Abstract We introduce IsraParlTweet, a new linked corpus of Hebrew-language parliamentary discussions from the Knesset (Israeli Parliament) between the years 1992-2023 and Twitter posts made by Members of the Knesset between the years 2008-2023, containing a total of 294.5 million Hebrew tokens. In addition to raw text, the corpus contains comprehensive metadata on speakers and Knesset sessions as well as several linguistic annotations. As a result, IsraParlTweet can be used to conduct a wide variety of quantitative and qualitative analyses and provide valuable insights into political discourse in Israel. # Dataset Description The corpus is divided into four main sections: Knesset Sessions, Twitter Posts, Office Sessions, and Linguistic Analyses. ## Knesset Sessions - knesset_speeches.csv This section contains the utterances of the MKs on the Knesset floor, in the order in which they appeared in the consecutive plenary protocol files. The utterances vary in length and may contain anything from a few words to a complete speech. Interruptions and interjections are preserved as they appear in the protocols. In total, this section contains approximately 4.5M individual utterances. The data is organized in CSV format, where each row represents a single utterance and contains the following fields: - **text**: The text of the utterance. - **uuid**: A unique text identifier used for associating the text with separately provided morphological analysis. - **knesset**: Knesset term. - **session_number**: Session number in current Knesset term. - **date**: Date of session. - **person_id**: Numeric identifier for speaker. Numeric identifiers are only assigned to MKs. 3% of speakers lack an identifier in cases of non-MK politicians (e.g. president, non-MK ministers), administrative Knesset workers, or guests, or if the matching MK could not be determined. Speakers are assigned an identifier if they were MKs in the time period of the corpus, even if they are not MKs at the time the utterance is made (e.g. presidents that were previously MKs). - **canonical_name**: The canonical name (first name and surname) of the speaker. Only present for MKs for which an identifier can be determined. - **name**: The name of the speaker as extracted from the protocol. - **chair**: Indicator for whether or not the speaker was the chair of the session. - **topic**: Topic of discussion or agenda item. - **topic_extra**: Additional information on the topic (e.g. subtitle, legislation proposal number). - **qa**: Indicator for whether or not the utterance is part of a Questions and Answers session. - **query**: The written query to which the utterance is an oral response. - **only_read**: Indicator for whether or not the utterance was a Q&A response that was read and not delivered by the answerer orally. ## Twitter Posts - contact authors for access - **text**: The text of the tweet. - **uuid**: A unique text identifier used for associating the text with separately provided morphological analysis. - **tweet_id**: Twitter's unique tweet identifier. - **date**: Date of the tweet. - **knesset**: The Knesset term corresponding to the date of the tweet. - **person_id**: Numeric identifier for the tweet poster. All rows have an identifier since only posts by MKs were collected. However, note that the poster was not necessarily serving as an MK at the time of posting. - **user_id**: Twitter user ID number. - **username**: Twitter handle name. - **name**: The canonical name (first name + surname) of the poster. - **likes**: Number of likes received at collection time. - **retweets**: Number of retweets at collection time. - **replies**: Number of replies at collection time. - **quotes**: Number of quotes at collection time. ## Office Sessions - metadata.csv This section contains metadata describing the office sessions of the MKs. An office session is a period of time in which a person served as an MK under a given party or faction. The data is organized in CSV format, where each row, representing a single office session, contains the following fields: - **start_date**: Start date of office session. - **end_date**: End date of office session. - **knesset**: Relevant Knesset term. - **person_id**: A unique personal id used for matching with Knesset Session utterances and Twitter Posts. - **first_name**: MK's first name. - **surname**: MK's surname. - **gender**: MK's gender. - **faction**: Name of faction under which the MK served. - **faction_id**: Unique identifier for faction. - **party_name**: Unified party name under which the MK served. - **dob**: MK's date of birth. - **cob**: MK's country of birth. - **yod**: MK's year of death. - **yoi**: MK's year of immigration (Aliyah) to Israel. - **city**: MK's city of residence. - **languages**: MK's spoken languages – a comma separated string. ## Linguistic Analyses All JSON files utilize the texts' uuid as keys. - **knesset_sentences.json**: List of segmented sentences (processed by Stanza) for Knesset utterances. - **knesset_lemmas.json**: List of lemmas (processed by Stanza) for Knesset utterances. - **knesset_sentiment**: List of predicted sentiment (by HeBERT sentiment model) for Knesset utterances. For additional linguistic analyses, please contact the authors. ## BibTeX ``` @inproceedings{mor-lan-etal-2024-israparltweet-israeli, title = "{I}sra{P}arl{T}weet: The Israeli Parliamentary and {T}witter Resource", author = "Mor-Lan, Guy and Levi, Effi and Sheafer, Tamir and Shenhav, Shaul R.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.819", pages = "9372--9381", abstract = "We introduce IsraParlTweet, a new linked corpus of Hebrew-language parliamentary discussions from the Knesset (Israeli Parliament) between the years 1992-2023 and Twitter posts made by Members of the Knesset between the years 2008-2023, containing a total of 294.5 million Hebrew tokens. In addition to raw text, the corpus contains comprehensive metadata on speakers and Knesset sessions as well as several linguistic annotations. As a result, IsraParlTweet can be used to conduct a wide variety of quantitative and qualitative analyses and provide valuable insights into political discourse in Israel.", } ```

asolodin/llm-rewrite-prompt-recovery-test

asolodin

--- license: apache-2.0 ---

AIAT/Optimizer-datasetfinal

AIAT

--- license: cc-by-nc-2.0 dataset_info: features: - name: Question dtype: string - name: Expression dtype: string - name: header dtype: string splits: - name: train num_bytes: 20938658 num_examples: 6716 download_size: 728889 dataset_size: 20938658 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - table-question-answering language: - th - en tags: - generated-by-cluade - generated-by-gpt4 - generated-by-Llama-2-70b ---

AIAT/The_Scamper-train

AIAT

--- task_categories: - question-answering ---

stair-lab/proteinea_fluorescence-esm2_t33_650M_UR50D-embedding

stair-lab

--- dataset_info: features: - name: inputs_embeds sequence: float64 - name: rewards dtype: float64 splits: - name: train num_bytes: 219864392 num_examples: 21446 - name: validation num_bytes: 54971224 num_examples: 5362 download_size: 67405937 dataset_size: 274835616 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

stair-lab/proteinea_fluorescence-Meta-Llama-3-8B-embedding

stair-lab

--- dataset_info: features: - name: inputs_embeds sequence: float64 - name: rewards dtype: float64 splits: - name: train num_bytes: 702999880 num_examples: 21446 - name: validation num_bytes: 175766360 num_examples: 5362 download_size: 165588685 dataset_size: 878766240 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

spikingneurons/ASCADv2

spikingneurons

--- license: bsd ---

AIAT/EXP-thai2sql

AIAT

--- license: apache-2.0 language: - th - en pretty_name: a task_categories: - text-generation --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

nirajandhakal/Mahabharata-HHGTTG-Text

nirajandhakal

--- license: pddl task_categories: - text-generation language: - en tags: - Mahabharata - The Hitchhiker Guide To The Galaxy - A Restaurant At The Edge of The Universe - Life, The Universe And Everything - So Long, And So Thanks For All The Fish - Mostly Harmless ---

abhinit27052001/eiffel-toy

abhinit27052001

--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 3538539.0 num_examples: 3 download_size: 3474272 dataset_size: 3538539.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

vc64/QA_gpt

vc64

--- dataset_info: features: - name: context dtype: string - name: answer dtype: string - name: title dtype: string - name: question dtype: string - name: reworded_answer dtype: string - name: id dtype: string splits: - name: train num_bytes: 4523437 num_examples: 3344 download_size: 2334738 dataset_size: 4523437 configs: - config_name: default data_files: - split: train path: data/train-* ---

maverickrzw/go_dataset_smaller

maverickrzw

--- license: apache-2.0 ---

stair-lab/proteinea_fluorescence-Mistral-7B-v0.1-embedding

stair-lab

--- dataset_info: features: - name: inputs_embeds sequence: float64 - name: rewards dtype: float64 splits: - name: train num_bytes: 702999880 num_examples: 21446 - name: validation num_bytes: 175766360 num_examples: 5362 download_size: 165594417 dataset_size: 878766240 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

AIAT/Machima-chatgptdataset

AIAT

--- license: cc-by-nc-4.0 task_categories: - table-question-answering language: - th - en ---

keunjoo/llama3

keunjoo

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 5826 num_examples: 39 download_size: 2572 dataset_size: 5826 configs: - config_name: default data_files: - split: train path: data/train-* ---

AIAT/Machima-gptloppingdataset

AIAT

--- license: cc-by-nc-4.0 task_categories: - table-question-answering language: - th - en ---

skygpt/zhilin-llama3-tt

skygpt

--- license: apache-2.0 ---

onsba/eeee

onsba

--- license: other license_name: ons license_link: LICENSE ---

yiweifu/relearn_data_lastname.hf

yiweifu

--- dataset_info: features: - name: text dtype: string - name: labels dtype: string splits: - name: train num_bytes: 2430856 num_examples: 641 download_size: 1485525 dataset_size: 2430856 configs: - config_name: default data_files: - split: train path: data/train-* ---

AIAT/Pangpuriye-public_alpaca-cleaned

AIAT

--- license: cc-by-4.0 language: - th - en tags: - code - sql size_categories: - 1K<n<10K --- # 🤖 [Super AI Engineer Development Program Season 4](https://superai.aiat.or.th/) - Pangpuriye House - Alpaca-Cleaned  ## Original Dataset We adopt this [alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset from `https://huggingface.co/datasets/yahma/alpaca-cleaned` the original repository. We used this dataset during the fine-tuning of [Panguriye's LLM](https://huggingface.co/AIAT/Pangpuriye-openthaigpt-1.0.0-7b-chat). The dataset is available under the Creative Commons Non Commercial (CC BY-NC 4.0). The original dataset consists of 51,760 rows of `input`, `instruction`, and `output` in English. We think that the `alpaca-cleaned` dataset is well structured and important to the learning of the instruction-tuned model. During the fine-tuning process, our goal of adding a mixture of English and Thai data is to help the LLM in understanding both sides equivalently. ## Call Dataset The following code is an example calling from `datasets` library. ```python from datasets import load_dataset dataset = load_dataset("AIAT/Pangpuriye-public_alpaca-cleaned") ``` ## Citation Information We acknowledge the original dataset, and please redirect to the original paper as follow: Please refer to the original dataset here [https://huggingface.co/datasets/yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). ``` @misc{alpaca, author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, title = {Stanford Alpaca: An Instruction-tuned LLaMA model}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, } ```

AIAT/Optimizer-gptgeneratedpandas

AIAT

--- license: cc-by-nc-2.0 task_categories: - text2text-generation language: - en tags: - generated-by-gpt3-5 ---

JijoJS/car_damage

JijoJS

--- license: apache-2.0 ---

CavidanZ/Audiobook

CavidanZ

--- license: apache-2.0 ---

SaProtHub/Dataset-Fluorescence-TAPE

SaProtHub

--- license: mit --- # Description Fluorescence prediction is a regression task where each input protein *x* is mapped to a label *y* ∈ *R*, corresponding to the log-fluorescence intensity of *x*. # Splits **Structure type:** None The dataset is from [**Evaluating Protein Transfer Learning with TAPE**](https://arxiv.org/abs/1906.08230). We follow the original data splits, with the number of training, validation and test set shown below: - Train: 20963 - Valid: 5235 - Test: 25517 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **seq:** The structure-aware sequence - **fitness:** fitness label of the sequence **1:** **···**

daniel-dona/openslr-slr108

daniel-dona

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string splits: - name: train num_bytes: 133143930.32 num_examples: 2507 download_size: 133567112 dataset_size: 133143930.32 configs: - config_name: default data_files: - split: train path: data/train-* ---

stair-lab/proteinea_fluorescence-gemma-7b-embedding

stair-lab

--- dataset_info: features: - name: inputs_embeds sequence: float64 - name: rewards dtype: float64 splits: - name: train num_bytes: 527314248 num_examples: 21446 - name: validation num_bytes: 131840856 num_examples: 5362 download_size: 124579770 dataset_size: 659155104 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

VAIBHAV22334455/JARVIS

VAIBHAV22334455

--- license: mit ---

enchatted/OSCAR-2301-EL

enchatted

--- license: cc0-1.0 ---

STONE11112/yuyinmoxing

STONE11112

--- license: mit ---

aikonst2025/ai

aikonst2025

--- license: apache-2.0 ---

nslyubaykin/data_for_iql

nslyubaykin

--- license: apache-2.0 ---

alissonpadua/ham-spam-scam-toxic

alissonpadua

--- license: mit ---

catinthebag/TumpengQA

catinthebag

--- license: cc-by-nc-4.0 task_categories: - text-generation language: - id size_categories: - 10K<n<100K --- # Synthetic Indonesian dataset with Llama 3 70B TumpengQA contains 6.7M words of 28.2K input-output pairs of Indonesian question-answering. It is intended to fine-tune Llama 3 8B, which has limited Indonesian language capabilities, to properly respond in Indonesian. It is a research preview dataset and not curated for factual accuracy or safety. Use this dataset at your discretion. # Out of scope use - Commercial use - Fine-tuning non-Llama 3 models

maverickrzw/go_dataset_size9

maverickrzw

--- license: apache-2.0 ---

YYYYYYibo/eval-dataset-with-score-rank4_plus

YYYYYYibo

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string - name: original dtype: string - name: rank4_plus dtype: string - name: gpt_score dtype: int64 splits: - name: train_prefs num_bytes: 1930938 num_examples: 200 download_size: 1112496 dataset_size: 1930938 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* --- # Dataset Card for "eval-dataset-with-score-rank4_plus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pramitsahoo/spoiler-regressor-data

pramitsahoo

--- license: mit ---

udmurtNLP/zerpal-udmdunne

udmurtNLP

--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 23704473.83701251 num_examples: 5707 - name: valid num_bytes: 10163807.162987491 num_examples: 2447 download_size: 16604100 dataset_size: 33868281.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* ---

Mymodalert/modafinil

Mymodalert

--- license: mit ---

tina2900/MUMU-LLaMA

tina2900

--- license: apache-2.0 ---

aikonst2025/arrcr

aikonst2025

--- license: apache-2.0 ---

InvestmentResearchAI/earnings_10k_questions

InvestmentResearchAI

--- license: apache-2.0 --- Dataset Summary This dataset is composed of 50 qeustions to test for earnings_10k dataset as part of the LLM-ADE framework (https://arxiv.org/abs/2404.13028), specifically designed to test for native processing/training. We incorporate these qeustions into lm-evalaution-harness (https://github.com/EleutherAI/lm-evaluation-harness) to test our LLM-ADE enhanced model to score if the training was successful.

DartiParti/AWS_ICONS

DartiParti

--- license: mit --- This dataset is a altered and reworked form of steven-kuo-5s6aq. # AWS Icon Detection > 2023-05-03 5:16am https://universe.roboflow.com/steven-kuo-5s6aq/aws-icon-detection Provided by a Roboflow user License: CC BY 4.0

NobodyExistsOnTheInternet/wildchat1Mjsonl

NobodyExistsOnTheInternet

--- license: mit ---

VonOx/carelink-diabetes-closedloop

VonOx

--- license: apache-2.0 ---

DIBT/aya_english_dpo_raw

DIBT

--- size_categories: n<1K dataset_info: features: - name: instruction dtype: string - name: targets dtype: string - name: language dtype: string - name: language_code dtype: string - name: annotation_type dtype: string - name: user_id dtype: string - name: generation_model dtype: string - name: generation dtype: string - name: predicted_generation_language dtype: string - name: predicted_generation_language_score dtype: float64 - name: generations sequence: string - name: generation_models sequence: string - name: model_name dtype: string - name: ratings sequence: int64 - name: rationales sequence: string splits: - name: train num_bytes: 567777 num_examples: 100 download_size: 320089 dataset_size: 567777 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for aya_english_dpo_raw This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/DIBT/aya_english_dpo_raw/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/DIBT/aya_english_dpo_raw/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "annotation_type": "original-annotations", "generation": "The largest organ in the human body is the skin. It covers the entire surface of the body and has a total area of about 22 square feet (2 square meters) in adults. The skin performs many important functions, such as:\n\n* Protecting the body from external damage, such as injury, infection, and UV radiation\n* Regulating body temperature\n* Aiding in the production of vitamin D\n* Assisting in the sensation of touch and feel\n* Helping to maintain fluid balance and hydration\n\nThe skin is composed of several layers, with the outermost layer being the epidermis, followed by the dermis, and then the hypodermis. It is estimated that the skin accounts for about 15% of an adult\u0027s total body weight.\n\nIt\u0027s worth noting that while the skin is the largest organ by surface area, the liver is often considered the largest organ by mass, weighing around 3 pounds (1.4 kilograms) in adults.", "generation_model": "meta-llama/Meta-Llama-3-70B-Instruct", "generation_models": [ "aya", "meta-llama/Meta-Llama-3-70B-Instruct" ], "generations": [ "The largest organ in the human body is the skin. It is the body\u0027s largest organ by surface area and weight. The skin serves many essential functions, including protecting the body from physical damage, pathogens, and dehydration, as well as helping to regulate body temperature and producing vitamin D in response to sunlight. It\u0027s a complex organ with multiple layers, cells, and structures, each serving a unique purpose in maintaining the body\u0027s overall health and well-being.", "The largest organ in the human body is the skin. It covers the entire surface of the body and has a total area of about 22 square feet (2 square meters) in adults. The skin performs many important functions, such as:\n\n* Protecting the body from external damage, such as injury, infection, and UV radiation\n* Regulating body temperature\n* Aiding in the production of vitamin D\n* Assisting in the sensation of touch and feel\n* Helping to maintain fluid balance and hydration\n\nThe skin is composed of several layers, with the outermost layer being the epidermis, followed by the dermis, and then the hypodermis. It is estimated that the skin accounts for about 15% of an adult\u0027s total body weight.\n\nIt\u0027s worth noting that while the skin is the largest organ by surface area, the liver is often considered the largest organ by mass, weighing around 3 pounds (1.4 kilograms) in adults." ], "instruction": "What is the largest organ in the human body?", "language": "English", "language_code": "eng", "model_name": "meta-llama/Meta-Llama-3-70B-Instruct", "predicted_generation_language": "eng_Latn", "predicted_generation_language_score": 0.9590950012207031, "ratings": [ 5, 5 ], "rationales": [ "The text is accurate, informative, and comprehensive in describing the skin as the largest organ in the human body. It provides specific details about its functions, structure, and importance, aligning perfectly with the instruction.", "This text is equally excellent, providing a clear and concise answer to the question. It lists the skin\u0027s functions, describes its composition, and offers additional interesting facts, such as the comparison with the liver\u0027s mass. The text is well-structured, accurate, and confident in its information, making it an excellent response." ], "targets": "The largest organ in the human body is the skin. It is the body\u0027s largest organ by surface area and weight. The skin serves many essential functions, including protecting the body from physical damage, pathogens, and dehydration, as well as helping to regulate body temperature and producing vitamin D in response to sunlight. It\u0027s a complex organ with multiple layers, cells, and structures, each serving a unique purpose in maintaining the body\u0027s overall health and well-being.", "user_id": "29f22cf193a81e1a5c47d76af453a91b3cd19aa348995c7add1df15fe24e8801" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_english_dpo_raw", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("DIBT/aya_english_dpo_raw") ``` </details>

scaleszw/my_ai_information

scaleszw

--- license: llama3 ---

hannybu/wild_angel_episodes_description_ru

hannybu

--- license: apache-2.0 ---

aironman/classifier-github-issues

aironman

--- license: apache-2.0 ---

aintech/vdf_paul_graham_essay

aintech

--- tags: - vdf - vector-io - vector-dataset - vector-embeddings --- This is a dataset created using [vector-io](https://github.com/ai-northstar-tech/vector-io)

jq/salt-asr-data-transcriptions

jq

--- dataset_info: - config_name: multispeaker-ach features: - name: id dtype: int64 - name: text dtype: string - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 - name: transcription dtype: string - name: edit_distance dtype: int64 splits: - name: train num_bytes: 704574 num_examples: 4811 - name: dev num_bytes: 14750 num_examples: 101 - name: test num_bytes: 14497 num_examples: 96 download_size: 401928 dataset_size: 733821 - config_name: multispeaker-eng features: - name: id dtype: int64 - name: text dtype: string - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 - name: transcription dtype: string - name: edit_distance dtype: int64 splits: - name: dev num_bytes: 15282 num_examples: 100 - name: test num_bytes: 15194 num_examples: 96 - name: train num_bytes: 734854 num_examples: 4797 download_size: 402022 dataset_size: 765330 - config_name: multispeaker-lgg features: - name: id dtype: int64 - name: text dtype: string - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 - name: transcription dtype: string - name: edit_distance dtype: int64 splits: - name: train num_bytes: 704330 num_examples: 4811 - name: dev num_bytes: 14684 num_examples: 101 - name: test num_bytes: 14411 num_examples: 96 download_size: 406173 dataset_size: 733425 - config_name: multispeaker-lug features: - name: id dtype: int64 - name: text dtype: string - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 - name: transcription dtype: string - name: edit_distance dtype: int64 splits: - name: train num_bytes: 801734 num_examples: 5016 - name: dev num_bytes: 16421 num_examples: 103 - name: test num_bytes: 16270 num_examples: 97 download_size: 819770 dataset_size: 834425 - config_name: multispeaker-nyn features: - name: id dtype: int64 - name: text dtype: string - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 - name: transcription dtype: string - name: edit_distance dtype: int64 splits: - name: train num_bytes: 700078 num_examples: 4811 - name: dev num_bytes: 14574 num_examples: 101 - name: test num_bytes: 14351 num_examples: 96 download_size: 417568 dataset_size: 729003 - config_name: multispeaker-teo features: - name: id dtype: int64 - name: text dtype: string - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 - name: transcription dtype: string - name: edit_distance dtype: int64 splits: - name: train num_bytes: 690253 num_examples: 4811 - name: dev num_bytes: 14389 num_examples: 101 - name: test num_bytes: 14113 num_examples: 96 download_size: 401353 dataset_size: 718755 configs: - config_name: multispeaker-ach data_files: - split: train path: multispeaker-ach/train-* - split: dev path: multispeaker-ach/dev-* - split: test path: multispeaker-ach/test-* - config_name: multispeaker-eng data_files: - split: dev path: multispeaker-eng/dev-* - split: test path: multispeaker-eng/test-* - split: train path: multispeaker-eng/train-* - config_name: multispeaker-lgg data_files: - split: train path: multispeaker-lgg/train-* - split: dev path: multispeaker-lgg/dev-* - split: test path: multispeaker-lgg/test-* - config_name: multispeaker-lug data_files: - split: train path: multispeaker-lug/train-* - split: dev path: multispeaker-lug/dev-* - split: test path: multispeaker-lug/test-* - config_name: multispeaker-nyn data_files: - split: train path: multispeaker-nyn/train-* - split: dev path: multispeaker-nyn/dev-* - split: test path: multispeaker-nyn/test-* - config_name: multispeaker-teo data_files: - split: train path: multispeaker-teo/train-* - split: dev path: multispeaker-teo/dev-* - split: test path: multispeaker-teo/test-* ---

NobodyExistsOnTheInternet/wildchat650kjsonl

NobodyExistsOnTheInternet

--- license: mit ---

kjetMol/ArtificiallyNoisySpeechTranscriptions

kjetMol

--- language: - 'no' --- This dataset contains transcriptions of speech files derived from the Norwegian language corpus provided by Språkbanken, specifically the nb_samtale subset. These transcriptions have been subjected to controlled noise addition to simulate various acoustic environments. URI: https://huggingface.co/datasets/Sprakbanken/nb_samtale/viewer/annotations/train?f[duration][min]=24.6432&f[duration][imax]=27.368 Original Audio properties: Duration : 24 secounds - 27 secounds Format: WAV Number of files: 9 Transcriptions: Number of Models tested: 3 Number of noise tested: 4 Number of noise levels: 16 TOTAL Number of files: 1682 Metrics Word Error Rate (WER): WER calculations are based on the transcriptions with 0% added noise as the ground truth. This metric helps in assessing the performance of speech recognition systems under varying noise conditions.

ServiceNow/repliqa_inference_tests

ServiceNow

--- dataset_info: features: - name: topic dtype: string - name: context dtype: string - name: question dtype: string - name: answer dtype: string - name: predicted_answer dtype: string - name: retrieved_topic dtype: string splits: - name: mistral_large num_bytes: 25984 num_examples: 33 - name: llama_3_70b_instruct num_bytes: 25144 num_examples: 33 - name: gpt_4_turbo num_bytes: 24963 num_examples: 33 download_size: 70547 dataset_size: 76091 configs: - config_name: default data_files: - split: mistral_large path: data/mistral_large-* - split: llama_3_70b_instruct path: data/llama_3_70b_instruct-* - split: gpt_4_turbo path: data/gpt_4_turbo-* ---

SaProtHub/Dataset-Thermostability-FLIP

SaProtHub

--- license: mit --- # Description Thermostability prediction is a regression task where each input protein *x* is mapped to a label *y* ∈ *R*, corresponding to the thermostability of *x*. # Splits **Structure type:** AF2 The dataset is from [**FLIP: Benchmark tasks in fitness landscape inferencefor proteins**](https://www.biorxiv.org/content/10.1101/2021.11.09.467890v2). We employ all proteins from the“Human-cell”splits (proteins that lack AF2 structures are removed), and split them based on 70% structure similarity (see [ProteinShake](https://github.com/BorgwardtLab/proteinshake/tree/main)), with the number of training, validation and test set shown below: - Train: 5310 - Valid: 706 - Test: 706 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **name:** The UniProt ID of the protein - **seq:** The structure-aware sequence - **plddt**: pLDDT values at all positions - **fitness:** fitness label of the sequence **1:** **···**

sinarproject/legisdata

sinarproject

--- license: cc-by-4.0 ---

Knowledge-Innovation-Centre/ESCO-Embeddings

Knowledge-Innovation-Centre

--- license: mit --- ESCO Skills Database Converted to Embeddings with 3072 Dimensions using OpenAI text-embedding-3-large

baraah/data_3_5_20k_withoutcolumnname

baraah

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 565493603.744 num_examples: 12604 download_size: 600841991 dataset_size: 565493603.744 configs: - config_name: default data_files: - split: train path: data/train-* ---

cocktailpeanut/town

cocktailpeanut

--- license: apache-2.0 ---

SaProtHub/Dataset-Metal_Ion_Binding

SaProtHub

--- license: mit --- # Description Metal Ion Binding prediction is a binary classification task where each input protein *x* is mapped to a label *y* ∈ {0, 1}, corresponding to whether there are metal ion–binding sites in the protein. The digital label means: 0: No 1: Yes # Splits **Structure type:** PDB The dataset is from [**Exploring evolution-aware & -free protein language models as protein function predictors**](https://arxiv.org/abs/2206.06583). We employ all proteins from the original dataset, and split them based on 70% structure similarity (see [ProteinShake](https://github.com/BorgwardtLab/proteinshake/tree/main)), with the number of training, validation and test set shown below: - Train: 5797 - Valid: 719 - Test: 719 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **name:** The PDB ID of the protein - **chain:** The chain ID of the protein - **seq:** The structure-aware sequence - **label:** Digital label of the sequence **1:** **···**

baraah/data_3_5_20k_withoutcolumnname3

baraah

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 565493603.744 num_examples: 12604 download_size: 600841991 dataset_size: 565493603.744 configs: - config_name: default data_files: - split: train path: data/train-* ---

Al-Hathboor-Bikal-ai-2023/Ahb_dataset_V.02

Al-Hathboor-Bikal-ai-2023

--- license: apache-2.0 ---

csssd/qs-llama3-tt

csssd

--- license: apache-2.0 ---

iagolaue/henriqju

iagolaue

--- license: openrail ---

Krish13/vinod_mmlutransformed_QnA_traintest

Krish13

--- dataset_info: features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: Instruction dtype: string - name: Response dtype: string splits: - name: test num_bytes: 14509714 num_examples: 14042 - name: validation num_bytes: 1588862 num_examples: 1531 - name: dev num_bytes: 262040 num_examples: 285 - name: auxiliary_train num_bytes: 326744953 num_examples: 99842 download_size: 104411440 dataset_size: 343105569 configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* - split: dev path: data/dev-* - split: auxiliary_train path: data/auxiliary_train-* ---

aniketsen/hplt_bn

aniketsen

--- dataset_info: features: - name: text dtype: string - name: metadata dtype: string splits: - name: train num_bytes: 44266046418 num_examples: 2875658 download_size: 13512248530 dataset_size: 44266046418 configs: - config_name: default data_files: - split: train path: data/train-* ---

SaProtHub/Dataset-Stability-TAPE

SaProtHub

--- license: mit --- # Description Stability Landscape Prediction is a regression task where each input protein *x* is mapped to a label *y* ∈ *R* measuring the most extreme circumstances in which protein *x* maintains its fold above a concentration threshold (a proxy for intrinsic stability). # Splits **Structure type:** None The dataset is from [**Evaluating Protein Transfer Learning with TAPE**](https://arxiv.org/abs/1906.08230). We follow the original data splits, with the number of training, validation and test set shown below: - Train: 53614 - Valid: 2512 - Test: 12851 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **seq:** The structure-aware sequence - **fitness:** fitness label of the sequence **1:** **···**

Alijeff1214/DILA_FRENCH_DATASET

Alijeff1214

--- license: mit ---

SaProtHub/Dataset-Beta_Lactamase-PEER

SaProtHub

--- license: mit --- # Description β-Lactamase Prediction studies the activity among first-order mutants of the TEM-1 beta-lactamase protein. The target *y* ∈ *R* is the experimentally tested fitness score which records the scaled mutation effect for each mutant. # Splits **Structure type:** None The dataset is from [**PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding**](https://arxiv.org/abs/2206.02096). We follow the original data splits, with the number of training, validation and test set shown below: - Train: 4158 - Valid: 520 - Test: 520 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **seq:** The structure-aware sequence - **fitness:** fitness label of the sequence **1:** **···**

asdcw/flower

asdcw

--- license: artistic-2.0 tags: - art pretty_name: the flower I like size_categories: - n<1K ---

Mantis-VL/MIQA_eval

Mantis-VL

--- dataset_info: - config_name: birds-to-words features: - name: id dtype: string - name: question_type dtype: string - name: question dtype: string - name: images sequence: image - name: options sequence: string - name: answer dtype: string - name: data_source dtype: string - name: category dtype: string splits: - name: test num_bytes: 170267865.0 num_examples: 337 download_size: 95411526 dataset_size: 170267865.0 - config_name: mantis_eval features: - name: id dtype: string - name: question_type dtype: string - name: question dtype: string - name: images sequence: image - name: options sequence: string - name: answer dtype: string - name: data_source dtype: string - name: category dtype: string splits: - name: test num_bytes: 479770102.0 num_examples: 217 download_size: 473031413 dataset_size: 479770102.0 - config_name: mementos features: - name: id dtype: string - name: question_type dtype: string - name: question dtype: string - name: images sequence: image - name: options sequence: string - name: answer dtype: string - name: data_source dtype: string - name: category dtype: string splits: - name: image_cmc num_bytes: 33771707.0 num_examples: 50 - name: image_dl num_bytes: 1468869742.0 num_examples: 448 - name: image_robo num_bytes: 539338081.0 num_examples: 199 - name: single_image_cmc num_bytes: 48207029.0 num_examples: 50 - name: single_image_dl num_bytes: 1455413585.0 num_examples: 448 - name: single_image_robo num_bytes: 550100816.0 num_examples: 199 download_size: 4094931165 dataset_size: 4095700960.0 - config_name: nlvr2 features: - name: id dtype: string - name: question_type dtype: string - name: question dtype: string - name: images sequence: image - name: options sequence: string - name: answer dtype: string - name: data_source dtype: string - name: category dtype: string splits: - name: test num_bytes: 12511043043.535 num_examples: 6967 download_size: 7454063707 dataset_size: 12511043043.535 configs: - config_name: birds-to-words data_files: - split: test path: birds-to-words/test-* - config_name: mantis_eval data_files: - split: test path: mantis_eval/test-* - config_name: mementos data_files: - split: image_cmc path: mementos/image_cmc-* - split: image_dl path: mementos/image_dl-* - split: image_robo path: mementos/image_robo-* - split: single_image_cmc path: mementos/single_image_cmc-* - split: single_image_dl path: mementos/single_image_dl-* - split: single_image_robo path: mementos/single_image_robo-* - config_name: nlvr2 data_files: - split: test path: nlvr2/test-* ---

Kamyar-zeinalipour/T4TAC

Kamyar-zeinalipour

--- dataset_info: features: - name: text dtype: string - name: category dtype: string - name: keyword dtype: string - name: clue dtype: string splits: - name: train num_bytes: 29274283 num_examples: 27403 download_size: 7066839 dataset_size: 29274283 configs: - config_name: default data_files: - split: train path: data/train-* ---

SaProtHub/Dataset-AAV-FLIP

SaProtHub

--- license: mit --- # Description AAV Prediction is a regression task where each input protein *x* is mapped to a label *y* ∈ *R* measuring the fitness score. # Splits **Structure type:** None The dataset is from [**FLIP: Benchmark tasks in fitness landscape inferencefor proteins**](https://www.biorxiv.org/content/10.1101/2021.11.09.467890v2). We follow the original data splits from the "2-vs-rest" branch, with the number of training, validation and test set shown below: - Train: 22246 - Valid: 2462 - Test: 50432 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **seq:** The structure-aware sequence - **fitness:** fitness label of the sequence **1:** **···**

krasserm/gba-trajectories

krasserm

--- dataset_info: features: - name: prompt dtype: string - name: target dtype: string - name: text dtype: string splits: - name: train num_bytes: 21224104 num_examples: 8579 - name: test num_bytes: 11940 num_examples: 5 download_size: 8000136 dataset_size: 21236044 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- A synthetic dataset from an [agent simulation](https://github.com/krasserm/grammar-based-agents/tree/master/simulation) for [planner LLM fine-tuning](https://github.com/krasserm/grammar-based-agents/tree/master/train). More details are described in [Planner fine-tuning on synthetic agent trajectories](https://krasserm.github.io/2024/05/31/planner-fine-tuning/) and the [grammar-based-agents](https://github.com/krasserm/grammar-based-agents) project. This dataset was used to fine-tune [krasserm/gba-planner-7B-v0.1](https://huggingface.co/krasserm/gba-planner-7B-v0.1) and [krasserm/gba-planner-7B-v0.1-GGUF](https://huggingface.co/krasserm/gba-planner-7B-v0.1-GGUF).

Kamyar-zeinalipour/TAC

Kamyar-zeinalipour

--- dataset_info: features: - name: answer dtype: string - name: clue dtype: string - name: Source dtype: string - name: Date dtype: string splits: - name: train num_bytes: 14586686 num_examples: 187395 download_size: 6769639 dataset_size: 14586686 configs: - config_name: default data_files: - split: train path: data/train-* ---

Krish13/vinod_mmlutransformed_QnA_train_all

Krish13

--- dataset_info: features: - name: question dtype: string - name: subject dtype: string - name: choices sequence: string - name: answer dtype: int64 - name: instruction dtype: string - name: response dtype: string splits: - name: train num_bytes: 343105569 num_examples: 115700 download_size: 104385689 dataset_size: 343105569 configs: - config_name: default data_files: - split: train path: data/train-* ---

asoria/datasets_features_outputs

asoria

--- size_categories: n<1K dataset_info: features: - name: dataset dtype: string - name: columns dtype: string - name: instruction dtype: string - name: generation_model dtype: string - name: generation dtype: string splits: - name: train num_bytes: 1080701 num_examples: 73 download_size: 493549 dataset_size: 1080701 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for datasets_features_outputs This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/asoria/datasets_features_outputs/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/asoria/datasets_features_outputs/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "columns": "{\"text\": {\"dtype\": \"string\", \"_type\": \"Value\"}}", "dataset": "huggingartists/bushido-zho", "generation": "\n\nQuestion: Which words appear most frequently in the text column of the dataset?\n{\"question\": \"Which words appear most frequently in the text column of the dataset?\", \"sql_query\": \"SELECT word, COUNT(*) as frequency FROM (SELECT TRIM(REGEXP_SPLIT_TO_TABLE(text, \u0027\\s+\u0027)) as word FROM data) words GROUP BY word ORDER BY frequency DESC LIMIT 10\"}", "generation_model": "mistralai/Mistral-7B-Instruct-v0.2", "instruction": "You are a data analyst tasked with exploring a dataset named huggingartists/bushido-zho. Below is the dataset schema in SQL format along with a sample of 5 rows:\nCREATE TABLE \"data\"(\"text\" VARCHAR);\nSample rows:\n{\u0027text\u0027: \u0027\u0027}\n{\u0027text\u0027: \u0027...\u0442\u044f\u043d\u0435\u0442 \u0441\u0432\u043e\u0438 \u0440\u0443\u043a\u0438\\n\u0425\u043e\u0447\u0435\u0442 \u044d\u0442\u043e \u0432\u043d\u0430\u0442\u0443\u0440\u0435 \\n\u042d\u0442\u0430 \u0441\u0443\u043a\u0430 \u043d\u0435\u043f\u043e\u0441\u043b\u0443\u0448\u043d\u0430\u044f\\n\u0414\u0430, \u043e\u043d\u0430 \u0437\u043d\u0430\u0435\u0442, \u0447\u0442\u043e \u044f \u0431\u0443\u0434\u0443 \u043b\u0443\u0447\u0448\u0435\\u2005\\n\u0423 \u043c\u043e\u0438\u0445 \u0441\u0443\u0447\u0435\u043a\\u2005\u043f\u0438\u0437\u0434\u0430\u0442\u044b\u0435 \u0441\u0443\u043c\u043a\u0438\\n\u0412\u043d\u0443\u0442\u0440\u0438 \u044d\u0442\u0438\u0445 \u0441\u0443\u043c\u043e\u043a \u043e\u0433\u0440\u043e\u043c\u043d\u044b\u0435 \u0441\u0443\u043c\u043c\u044b\\u2005\\n\u041d\u0443-\u043a\u0430, \u043d\u0443-\u043a\u0430, \u043d\u0443-\u043a\u0430, \u043c\u044b \u0441\u0442\u0440\u043e\u0438\u043c\\n\u0414\u0430, \u043c\u044b \u0431\u0443\u0434\u0442\u043e \u0448\u0442\u0443\u043a\u0430\u0442\u0443\u0440\u043a\u0430\\n\u0412\u0441\u0435 \u0433\u0438\u043b\u044c\u0437\u044b \u043f\u0430\u0434\u0430\u044e\u0442 \u043d\u0430 \u043f\u043e\u043b\\n\u042f \u043a\u043b\u0430\u0434\u0443 \u0434\u0435\u043d\u044c\u0433\u0438 \u043d\u0430 \u0441\u0442\u043e\u043b \u0441\u0432\u043e\u0435\u0439 \u043c\u0430\u043c\u0435 \\n\u0417\u0434\u0435\u0441\u044c SEEMEE \u043f\u0430\u043f\u0430\\n\u0412\u0438\u0434\u0438\u0448\u044c \u043c\u0435\u043d\u044f, \u044f \u043f\u0443\u043b\u044f\u044e \u043a\u0430\u043a choppa \\n...\u0027}\n{\u0027text\u0027: \u0027Glizzy, what you cookin up?\\n\u041f\u043e-\u043f\u043e\\nBUSHIDO ZHO down away\\n\u042d\u0439, gang\\n\u0425\u0435\u0439\\n\u0425\u0430 \\n\u0414\u0440\u0438\u043f \\n\u0414\u0440\u0438\u043f \\n\u0414\u0432\u0430-\u043d\u043e\u043b\u044c, \u043e\u0434\u0438\u043d\\n\u0415, \u0435, \u043e\u043a\u0435\u0439 wait, \u0435\u0449\u0435\\n\u041c\u043e\u0438 \u0424\u043e\u0440\u0441\u044b \u043d\u0430 \u043b\u0438\u0446\u043e \u0442\u0432\u043e\u0435\u0439 slatt \\n\u0421\u0443\u043a\u0430, \u043c\u043e\u0439 \u0434\u0440\u0438\u043f \u2014 \u043c\u044f\u0441\u0446\u043e , \u044f \u0442\u0440\u044d\u043f \u043f\u043e\u044d\u0442 \\nZHO \u043a\u0443\u043f\u0438\u043b \u0431\u044b Porsche , \u0434\u0430, \u044f \u043a\u0443\u0440\u044e \u0431\u043e\u0440\u0449 \\n\u0414\u0430\u043b \u0431\u043b\u044f\u0434\u0435 \u043b\u0435\u0449\u0430 , \u0442\u044b \u0432\u0435\u0434\u044c \u0437\u043d\u0430\u0435\u0448\u044c \\n\u0414\u0430, \u044f \u043d\u0430\u0445\u0430\u043b, \u044f \u0441\u043a\u0430\u0437\u0430\u043b: bust down \\n\u041c\u043e\u043d\u043e\u0442\u043e\u043d\u043d\u044b\u0439 voice \u2014 \u044d\u0442\u043e maintown \\n\u042f \u043a\u0443\u0440\u044e moonrock , \u043f\u0430\u0440\u0435\u043d\u044c, \u044d\u0442\u043e dope walk \\n\u041d\u0435 \u0431\u043e\u044f\u043b\u0441\u044f \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u044f \u0442\u0435\u043a\u0441\u0442 \u043f\u0440\u043e hoes, \u043f\u0440\u043e\u0441\u0442\u0438\\n\u0414\u044f\u0434\u044f, \u0442\u044b \u0434\u0435\u0431\u0438\u043b, \u044f \u0442\u043e\u043f\u043b\u044e \u0437\u0430 \u0440\u0443\u0441\u0441\u043a\u0438\u0439 \u0434\u0440\u0438\u043b\u043b\\n\u0417\u043e\u043b\u043e\u043c\u0430\u043a\u0441, \u044f killah, \u0442\u0440\u0438 \u0431\u0430\u0440\u0430, \u043e\u0442\u0443\u043f\u0435\u043b, \u043d\u0435 \u0444\u0430\u0440\u043c\u0430\u0446\u0435\u0432\u0442\\nKillah on my way , killah on my way \\n\u0412\u043e \u043c\u043d\u0435 \u043e\u0447\u0435\u043d\u044c \u043c\u0430\u043b\u043e \u043f\u0430\u043c\u044f\u0442\u0438 \\n\u0422\u0432\u043e\u044f \u0441\u0443\u043a\u0430 \u0433\u043e\u0432\u043e\u0440\u0438\u0442 \u043c\u043d\u0435: \u00ab\u0412\u044b \u0432\u0441\u0435\u0433\u043e \u043f\u0440\u0438\u044f\u0442\u0435\u043b\u0438\u00bb \\n\u041e\u0441\u0442\u0430\u043d\u0443\u0441\u044c \u0432 \u0435\u0451 \u043f\u0430\u043c\u044f\u0442\u0438 \\n\u041d\u0430\u0443\u0447\u0443 \u0442\u0440\u044d\u043f \u0433\u0440\u0430\u043c\u043e\u0442\u0435 \\n\u041c\u043e\u0438 \u0424\u043e\u0440\u0441\u044b \u043d\u0430 \u043b\u0438\u0446\u043e \u0442\u0432\u043e\u0435\u0439 slatt \\n\u041c\u043e\u0451 \u0438\u043c\u044f \u2014 ZHO, ZHO, ZHO, god damn \\nZHO \u043a\u0443\u043f\u0438\u043b \u0431\u044b Porsche , \u0434\u0430, \u044f \u043a\u0443\u0440\u044e \u0431\u043e\u0440\u0449 \\n\u0414\u0430\u043b \u0431\u043b\u044f\u0434\u0435 \u043b\u0435\u0449\u0430 , \u0442\u044b \u0432\u0435\u0434\u044c \u0437\u043d\u0430\u0435\u0448\u044c \\n\u0414\u0430, \u044f \u043d\u0430\u0445\u0430\u043b, \u044f \u0441\u043a\u0430\u0437\u0430\u043b bust down \\n\u041c\u043e\u043d\u043e\u0442\u043e\u043d\u043d\u044b\u0439 voice, \u044d\u0442\u043e \u043c\u043e\u0439 town \\nWha, gang, Zho-Zho \u043f\u0430\u0443-\u043f\u0430\u0443\\nHold on!\\nYeah\\nHold on!\\nYeah\\nHold on!\\nYeah\\nSlatt!\u0027}\n{\u0027text\u0027: \u0027\u041c\u044b \u0435\u0434\u0435\u043c \u043d\u0430 \u043c\u0430\u0448\u0438\u043d\u0430\u0445, \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u0430 \u0433\u043e\u043d\u043e\u0447\u043d\u044b\u0445\\n\u0422\u043e\u043b\u044c\u043a\u043e \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e, \u0432\u0441\u0451 \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c\\u2005\u043a\u0430\u043a\\u2005\u0432 \u0440\u0430\u043b\u043b\u0438 \\n\u0415\u0434\u0443\\u2005\u043f\u0440\u044f\u043c \u043d\u0430 \u043a\u0440\u0430\u0441\u043d\u044b\u0439 \\n\u0415\u0434\u0443 \u043a\u0430\u043a \u0432\\u2005\u043d\u0430\u0441\u043a\u0430\u0440\u0435 \\n\u0415\u0434\u0443 \u043d\u0430 McLaren\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \u0431\u044b\u0441\u0442\u0440\u043e \u0442\u044b \u043d\u0435 \u043f\u043e\u0439\u043c\u0430\u0435\u0448\u044c\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \u0442\u0435 \u043a\u0440\u0443\u0433\u0438 \u043d\u0430\u0432\u043e\u0440\u0430\u0447\u0438\u0432\u0430\u044e\\n\u0422\u044b \u043d\u0430\u0441 \u043d\u0435 \u043f\u043e\u0439\u043c\u0430\u0435\u0448\u044c \\n\u0421\u043e \u043c\u043d\u043e\u044e \u0442\u0430\u043f\u043a\u0438, \u0441\u043e \u043c\u043d\u043e\u044e \u0431\u0430\u0431\u043a\u0438\\n\u041c\u044b \u0437\u0430\u0440\u0430\u0431\u0430\u0442\u044b\u0432\u0430\u0435\u043c, \u044f \u0437\u0430\u0440\u0430\u0431\u043e\u0442\u0430\u043b \u043d\u0430 \u043f\u043e\u043b\u0451\u0442 \u0431\u0443\u0434\u0442\u043e \u0431\u044b \u043d\u0430 \u0442\u0440\u0430\u043f\u0435\\n\u0417\u0430\u0440\u0430\u0431\u043e\u0442\u0430\u043b \u043d\u0430 \u043f\u043e\u043b\u0451\u0442\u0430\u0445 \u0431\u0443\u0434\u0442\u043e \u0431\u044b \u0430\u0432\u0438\u0430\u043b\u0430\u0439\u043d\u0435\u0440 \\n\u041a\u0430\u043a \u0432\u0438\u0445\u0440\u044c \u043b\u0435\u0442\u0430\u0435\u043c \u044d\u0442\u043e \u043c\u0443\u0442\u0438\u0442\u0441\u044f \u0442\u0430\u043a \u0432\u043e\u0442 \\n\u042d\u0442\u043e Forza Motorsport, \u043d\u0438\u0433\u0433\u0430\\n\u0422\u0432\u043e\u0435\u0439 \u0441\u0443\u043a\u0435 \u043b\u0443\u0447\u0448\u0435 \u043f\u0440\u044b\u0433\u0430\u0442\u044c \u0437\u0430 \u0431\u043e\u0440\u0442, \u043f\u0438\u0434\u0440 \\n\u041a\u0440\u0443\u0433\u0430\u043b\u044f\u043d\u0430 \u0432\u0442\u043e\u0440\u043e\u0433\u043e \u0432\u0440\u0443\u0447\u0438\u043b \u0435\u0439\\n\u0422\u0440\u0430\u043f, \u0442\u0440\u0430\u043f \u0441\u043a\u043e\u043b\u044c\u0437\u0438\u043c \u043a\u0430\u043a \u043d\u0438\u043d\u0434\u0437\u044f\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043a\u0430\u043a \u0432 \u0440\u0430\u043b\u043b\u0438 \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043d\u0430 \u043a\u0440\u0430\u0441\u043d\u044b\u0439 \\n\u0415\u0434\u0443 \u043a\u0430\u043a \u0432 \u043d\u0430\u0441\u043a\u0430\u0440\u0435 \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 \u043c\u0430\u0448\u0438\u043d\u0435 , \u043d\u0430 \u0430\u0432\u0442\u043e\u043c\u043e\u0431\u0438\u043b\u0435 \\n\u0425\u0443\u043b\u0438 \u0433\u043d\u0438\u0434\u0430 \u0437\u044b\u0440\u0438\u0448\u044c , \u0432\u044b\u043a\u043e\u043b\u044e \u0442\u0435 \u0437\u044b\u0440\u043a\u0438 \\n\u0417\u0430 \u043c\u043d\u043e\u0439 \u0431\u0443\u0434\u0443\u0442 \u0434\u0430\u0433\u0438 \u043d\u0430 \u0437\u0430\u043d\u0438\u0436\u0435\u043d\u043d\u043e\u0439 \\n\u0422\u044b \u0443\u043d\u0438\u0436\u0435\u043d\u043d\u044b\u0439 \u043d\u0430 \u043e\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0435 \u044d\u0442\u043e \u0442\u0430\u043a \u043e\u0431\u0438\u0434\u043d\u043e \\n\u042f \u043f\u043e\u0433\u0440\u0443\u0436\u0435\u043d\u043d\u044b\u0439 \u0432 \u0441\u0435\u0431\u044f \u0438 \u0432\u0430\u0441 \u043d\u0435 \u0432\u0438\u0434\u043d\u043e\\n\u041d\u0430 \u043c\u043d\u0435 \u0432\u0438\u0434\u044b \u0440\u0430\u0437\u043d\u044b\u0445 \u0446\u0435\u043f\u0435\u0439 \u043f\u0440\u0438\u0441\u0442\u0430\u044e\u0442 \u043a \u043a\u0440\u0430\u0441\u0438\u0432\u044b\u043c \u043f\u043b\u0430\u043d\u0430\u043c\\n\u0415\u0434\u0443 \u043d\u0430 McLaren , \u0432\u0438\u0436\u0443 \u0432\u0441\u0451 \u043f\u0438\u0437\u0434\u0430\u0442\u043e \\n\u0422\u044b \u0437\u0430\u043b\u0443\u043f\u043e\u0439 \u043f\u0430\u0445\u043d\u0435\u0448\u044c , \u044f \u043f\u043e\u0434\u043e\u0431\u0435\u043d \u0437\u043b\u0430\u0442\u0443\\n\u0421 \u041a\u0430\u0440\u043b\u043e\u043c \u0412\u0435\u043b\u0438\u043a\u0438\u043c \u0443\u043a\u0440\u0430\u043b\u0438 \u043a\u043e\u0440\u0430\u043b\u043b\u044b\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \u0433\u0434\u0435-\u0442\u043e \u0432 \u041a\u0430\u0440\u043f\u0430\u0442\u0430\u0445 \\n\u041f\u0430\u0440\u0442\u0438\u044f \u0434\u0435\u043d\u0435\u0433 \u2014 \u0431\u044b\u0441\u0442\u0440\u044b\u0439 \u043a\u043e\u0440\u0430\u0431\u043b\u044c \\n\u041f\u043e\u043f\u0430\u043b\u0438\u0441\u044c \u0432 \u0441\u0435\u0442\u0438 \u0432\u043a\u0443\u0441\u043d\u044b\u0435 \u043a\u0430\u043b\u044c\u043c\u0430\u0440\u044b \\n\u041a\u0443\u0441\u0430\u044e \u2014 \u0441\u043e\u043a \u043f\u0430\u0434\u0430\u0435\u0442 , \u0443 \u0432\u0430\u0441 \u0432\u0441\u0435\u0445 \u044d\u043a\u0437\u0430\u043c\u0435\u043d\u044b \\n\u0418 \u0441 \u043f\u043b\u0435\u0447 \u0446\u0435\u043f\u0438 \u043a\u0430\u043f\u0430\u0435\u0442, \u043d\u0430 \u0442\u0440\u0430\u0441\u0441\u0443 \u0431\u0440\u0438\u043b\u043b\u0438\u0430\u043d\u0442\u0430\u043c\u0438 \\n\u041f\u0440\u0438\u0441\u0442\u0438\u043b\u0438\u0441\u044c \u0441 \u043a\u0430\u043f\u043a\u0430\u043d\u0430\u043c\u0438 , \u0442\u0435\u043f\u0435\u0440\u044c \u043c\u044b \u043f\u043e\u0434 \u043f\u0430\u043b\u044c\u043c\u0430\u043c\u0438\\n\u0421\u0443\u043a\u0438 \u043e\u0434\u0435\u0432\u0430\u044e\u0442\u0441\u044f \u043d\u0430 \u0446\u0435\u0440\u0435\u043c\u043e\u043d\u0438\u044e , \u043c\u044b \u043d\u0435 \u043f\u043e\u0434\u0435\u043b\u0438\u043c\u0441\u044f \u043f\u043b\u0430\u043d\u0430\u043c\u0438\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043a\u0430\u043a \u0432 \u0440\u0430\u043b\u043b\u0438 \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043d\u0430 \u043a\u0440\u0430\u0441\u043d\u044b\u0439 \\n\u0415\u0434\u0443 \u043a\u0430\u043a \u0432 \u043d\u0430\u0441\u043a\u0430\u0440\u0435 \\n\u0415\u0434\u0443 \u043d\u0430 McLaren\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\nBitch, \u044f \u0434\u0435\u043b\u0430\u044e \u0440\u044b\u0432\u043e\u043a \u044d\u0442\u043e Forza Motorsport\\n\u0417\u0430\u0431\u0438\u0440\u0430\u0439 \u0441\u0432\u043e\u0438\u0445 \u0442\u0438\u043f\u043e\u0432 \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c, \u0431\u043b\u044f, \u043d\u0435 \u0432\u0430\u0448 \u043a\u043e\u043d\u0451\u043a\\n\u042d\u0442\u043e Forza Motorsport, \u043c\u0438\u043a\u0441\u0443\u044e Nike \u0438 Tissot\\n\u0420\u0430\u0437\u0433\u043e\u043d\u044f\u044e\u0441\u044c \u043f\u043e\u0434 \u0434\u0432\u0435 \u0441\u043e\u0442\u043d\u0438, \u044f \u043d\u0435 \u0447\u0443\u0432\u0441\u0442\u0432\u0443\u044e \u043b\u0438\u0446\u043e\\n\u0412\u044b\u043a\u0443\u043f\u0430\u0439 \u0434\u0435\u0440\u044c\u043c\u043e, \u0432\u044b\u043a\u0443\u043f\u0430\u0439\u0442\u0435 \u043c\u043e\u0439 \u0431\u0430\u0437\u0430\u0440\\n\u0412\u044b\u0436\u0438\u043c\u0430\u044e \u0432 \u043f\u043e\u043b, bae, \u0437\u0430\u043a\u0440\u044b\u0432\u0430\u0439 \u0433\u043b\u0430\u0437\u0430\\n\u0413\u043e\u0440\u043e\u0434 \u043c\u0435\u043d\u044f \u043b\u044e\u0431\u0438\u0442, \u0438 \u044f \u0432 \u043d\u0451\u043c \u043d\u0430\u0448\u0451\u043b \u0441\u0435\u0431\u044f\\n\u041c\u043e\u044f \u0442\u0440\u0430\u0441\u0441\u0430 \u043c\u0438\u0440, \u044f \u043d\u0435 \u0434\u043e\u0435\u0434\u0443 \u0434\u043e \u043a\u043e\u043d\u0446\u0430-\u0430-\u0430\\nRalph Lauren Polo \u0441\u043f\u043e\u0440\u0442, \u044f \u0435\u0434\u0443 \u043e\u0447\u0435\u043d\u044c \u0431\u044b\u0441\u0442\u0440\u043e\\n\u041d\u0430 \u043c\u043e\u0438\u0445 \u0433\u043b\u0430\u0437\u0430\u0445 \u043e\u0447\u043a\u0438, \u043c\u043d\u0435 \u043f\u0440\u0438\u0432\u0435\u0437\u043b\u0438 \u0441 \u041f\u0430\u0440\u0438\u0436\u0430\\n\u0412\u043e\u0443, \u0432\u043e\u0443, \u0441\u0442\u0438\u043b\u044f \u0432\u044b\u0448\u0435 \u043a\u0440\u044b\u0448\u0438\\n\u0427\u0442\u043e, \u0447\u0442\u043e, \u0447\u0442\u043e \u0442\u044b \u0445\u043e\u0447\u0435\u0448\u044c \u0441\u043b\u044b\u0448\u0430\u0442\u044c\\n\u0427\u0442\u043e \u0442\u044b \u0445\u043e\u0447\u0435\u0448\u044c \u0441\u043b\u044b\u0448\u0430\u0442\u044c \u043e\u0442 \u043c\u0435\u043d\u044f \u043f\u0440\u0438\u0434\u0443\u0440\u043e\u043a\\n\u041c\u043d\u0435 \u043d\u0443\u0436\u043d\u0430 \u0442\u043e\u043b\u044c\u043a\u043e \u0432\u0430\u043b\u044e\u0442\u0430, \u0441\u0443\u043a\u0430, \u043d\u0435 \u043c\u0430\u0440\u0430\u044e \u0440\u0443\u043a\u0438\\n\u041d\u0430\u0432\u043e\u0436\u0443 \u043d\u0435\u043c\u043d\u043e\u0433\u043e \u0448\u0443\u043c\u0430, Whitener, \u041b\u044f\u043d\u0430 \u043f\u043b\u044e\u0441 best duo\\n\u041e\u0442\u043a\u0440\u044b\u0432\u0430\u044e \u0434\u0432\u0435\u0440\u0438 \u043a\u043b\u0443\u0431\u0430, \u043c\u043e\u0451 \u0444\u043b\u043e\u0443 \u0437\u043e\u0432\u0451\u0442\u0441\u044f \u0442\u0443\u0440\u0431\u043e\\n\u0412\u043e\u0443, \u0432\u043e\u0443, \u0432\u043e\u0443, \u0435\u0434\u0443 \u043d\u0430 McLaren\\nWhitener, Whitener, Whitener \u043f\u043b\u044e\u0441 Polyana\\n\u041b\u044f\u043d\u0430 \u043d\u0430 \u043d\u0430\u0441\u043a\u0430\u0440\u0435, \u0432\u043e\u0443, \u0432\u043e\u0443, \u0432\u043e\u0443\\n\u0401\u043f\u0442\u0430, \u0434\u0430\u043b\u044c\u0448\u0435 \u0441\u0430\u043c\u0438\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043a\u0430\u043a \u0432 \u0440\u0430\u043b\u043b\u0438 \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043d\u0430 \u043a\u0440\u0430\u0441\u043d\u044b\u0439 \\n\u0415\u0434\u0443 \u043a\u0430\u043a \u0432 \u043d\u0430\u0441\u043a\u0430\u0440\u0435 \\n\u0415\u0434\u0443 \u043d\u0430 McLaren\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\nMB , SC \\nMelon music, Mellow Bite \\nMMMB, \u043f\u043e-\u043f\u043e-\u043f\u043e-\u043f\u043e \\n\u041c\u043d\u0435 \u043f\u043b\u0435\u0432\u0430\u0442\u044c \u043d\u0430 \u0432\u0435\u0441\u044c \u0442\u0432\u043e\u0439 \u0442\u0440\u0430\u043f\u0447\u0438\u043a\\n\u042d\u0442\u043e \u0448\u0443\u0442\u043a\u0438 \u043d\u0438\u0433\u0433\u0430, \u044f \u0442\u044f \u0448\u0430\u0442\u043d\u0443\u043b, \u043d\u0438\u0433\u0433\u0430\\n\u041d\u0430 \u043d\u043e\u0433\u0430\u0445 \u0442\u0430\u043f\u043a\u0438, \u0442\u0438\u043f\u043e \u043a\u0430\u043a Forged\\n\u041e\u043f\u043f\u044b \u043d\u0435 \u0440\u044f\u0434\u043e\u043c, \u043c\u044b \u043d\u0435 \u0432 \u041a\u0430\u043c\u0431\u043e\u0434\u0436\u0435\\n\u0415\u0434\u0443 \u043d\u0430 McLaren, gang \u0438 \u043d\u0430\u043c \u043c\u043e\u0436\u043d\u043e\\n\u0423 \u043f\u043b\u0430\u043d\u043a\u0442\u043e\u043d\u0430 Curren, \u0443 \u043d\u0438\u0445 \u0432\u0441\u0451 \u0441\u043b\u043e\u0436\u043d\u043e\\n\u042f, \u044f, \u0443 \u043c\u0435\u043d\u044f Forged\\nLil nigga, \u0437\u043e\u0432\u0443 \u0442\u0435\u0431\u044f \u0441\u0438\u0442\u043e\\n\u0422\u044b \u0434\u044b\u0440\u044f\u0432\u044b\u0439, \u043e\u0442 \u0447\u0435\u0433\u043e \u043d\u0435 \u0432\u0438\u0434\u043d\u043e\\n\u0412\u0440\u043e\u0434\u0435 \u0442\u044b \u043a\u0430\u043a \u0441\u043f\u0430\u043d\u0447 \u0431\u043e\u0431 \u044d\u0442\u043e \u043e\u0447\u0435\u0432\u0438\u0434\u043d\u043e \\n\u0418 \u0442\u0435\u0431\u0435 \u043d\u0435 \u0434\u043e\u0433\u043d\u0430\u0442\u044c \u043c\u0435\u043d\u044f\\n\u0422\u0432\u043e\u0438 \u0433\u043b\u0430\u0437\u0430 \u0432\u0438\u0434\u044f\u0442 \u0442\u0430\u0447\u043a\u0443, \u044f \u0443\u0436\u0435 \u0443\u0435\u0445\u0430\u043b, \u0434\u0430\\n\u0422\u0435\u0431\u0435 \u043d\u0435 \u0443\u0433\u043d\u0430\u0442\u044c \u0437\u0430 \u0441\u0432\u044d\u0433\u043e\u043c \u044d\u0442\u043e \u0442\u043e\u0447\u043d\u043e\\nMcLaren Forged, \u0431\u0443\u0434\u0435\u0448\u044c \u043e\u0441\u0442\u043e\u0440\u0436\u043d\u0435\u0439\\n\u0411\u0438\u0433 \u0431\u043e\u0439 \u043a\u0430\u043a \u043c\u0430\u0434\u0430\u0440\u0430, 163 \u2014 \u0433\u0430\u0430\u0440\u0430\\n\u041c\u043e\u0439 \u0432\u0435\u0441\u044c gang, whole lotta\\n223, whole lotta \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043a\u0430\u043a \u0432 \u0440\u0430\u043b\u043b\u0438 \\n\u0415\u0434\u0443 \u043f\u0440\u044f\u043c \u043d\u0430 \u043a\u0440\u0430\u0441\u043d\u044b\u0439 \\n\u0415\u0434\u0443 \u043a\u0430\u043a \u0432 \u043d\u0430\u0441\u043a\u0430\u0440\u0435 \\n\u0415\u0434\u0443 \u043d\u0430 McLaren\\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren \\n\u0415\u0434\u0443 \u043d\u0430 McLaren\u0027}\n{\u0027text\u0027: \u0027\u041c\u043e\u0438 \u043f\u0430\u0446\u0430\u043d\u044b \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u043d\u043e\u0441\u044f\u0442 stick\u0438\\n\u0422\u0432\u043e\u0438 \u0434\u0435\u0432\u043e\u0447\u043a\u0438 \u0441\u0442\u0440\u0435\u043b\u044f\u044e\u0442 \u0442\u043e\u043b\u044c\u043a\u043e \u0441\u0438\u0433\u0438\\n\u0422\u0432\u043e\u044f \u0448\u043b\u044e\u0445\u0430 \u0441\u0435\u0431\u044f \u043f\u0440\u043e\u0434\u0430\u0451\u0442 \u043a\u0430\u043a \u0448\u0432\u0430\u0431\u0440\u0430\\n\u0421\u0443\u043a\u0430, \u0441\u043e\\u2005\u043c\u043d\u043e\u0439\\u2005Zho Bushido, \u043f\u0440\u0430\u0432\u0434\u0430\\n\u0422\u044b\\u2005\u0432\u043d\u0430\u0442\u0443\u0440\u0435 \u043a\u043b\u043e\u0443\u043d, \u0442\u044b \u0432\u043d\u0430\u0442\u0443\u0440\u0435 \u0441\u043b\u043e\u043c\u0430\u043d\\n\u0422\u044b \u0432\u043d\u0430\u0442\u0443\u0440\u0435\\u2005\u043f\u043e\u0439\u043c\u0430\u043d, \u0442\u044b \u0449\u0430 \u043d\u0430\u0445\u0443\u0439 \u043f\u043e\u0441\u043b\u0430\u043d\\n\u041c\u044b \u0442\u0435\u0431\u044f \u043d\u0435 \u0438\u0449\u0435\u043c, \u0432\u0435\u0434\u044c \u0442\u044b \u043d\u0430\u043c \u043d\u0435 \u043d\u0443\u0436\u0435\u043d\\n\u042f \u0442\u0435\u0431\u044f \u043d\u0435 \u0441\u043b\u044b\u0448\u0443, \u043a\u0430\u043a \u0432 \u043f\u0443\u0441\u0442\u044b\u043d\u0435 \u0441\u0443\u0448\u0430\\n\u0422\u044b \u0448\u043b\u044e\u0445\u0430 \u2014 \u043c\u0435\u043d\u044f\u0435\u0448\u044c \u0442\u0440\u0430\u043f \u0441\u0432\u043e\u0438\u0445 \u0447\u0435\u043b\u043e\u0432 \u043d\u0430 \u0442\u0451\u043b\u043a\u0443\\n\u0412 \u0442\u0432\u043e\u0451\u043c \u0434\u043e\u043c\u0435 \u0438\u0437 \u0440\u0430\u0441\u0442\u0435\u043d\u0438\u0439 \u0433\u043e\u0440\u0438\u0442 \u0442\u043e\u043b\u044c\u043a\u043e \u0451\u043b\u043a\u0430\\n\u0422\u044b \u043d\u0435 \u0433\u044d\u043d\u0433\u0441\u0442\u0430, \u0442\u044b \u0431\u043e\u0436\u044c\u044f \u043a\u043e\u0440\u043e\u0432\u043a\u0430\\n\u0422\u0432\u043e\u0439 \u0441\u0432\u044d\u0433 \u0432 \u043a\u0440\u043e\u0441\u0441\u043e\u0432\u043a\u0430\u0445\\n\u041e\u0442 \u0433\u0440\u044f\u0437\u043d\u043e\u0433\u043e \u0442\u0440\u0430\u043f\u0430 \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 \u044f\u0437\u0432\u044b\\n\u041a\u0442\u043e \u0441\u043b\u0438\u043b \u043c\u043e\u0435\u0433\u043e \u043a\u043e\u0440\u0435\u0448\u0430? \u0414\u0430, \u044d\u0442\u043e \u044f \u0441\u043b\u0438\u043b\\n\u0422\u0451\u043b\u043a\u0435 \u0431\u044b\u043b\u043e \u0434\u0432\u0430\u0434\u0446\u0430\u0442\u044c, \u044f \u0433\u043e\u043d\u044f\u043b \u0432 \u044f\u0441\u043b\u0438\\n\u041d\u0430\u0448 \u0442\u0440\u0430\u043f \u0431\u0443\u0434\u0443\u0442 \u0442\u0440\u0430\u043f\u0438\u0442\u044c, \u0441\u043d\u043e\u0432\u0430 \u0432\u0441\u0435\u043c \u044f\u0441\u043d\u043e, \u0430\\n\u041a\u0430\u043a\u043e\u0439 \u0442\u044b \u043d\u0430\u0445\u0443\u0439 \u0445\u0430\u0441\u043b\u0435\u0440?\\n\u0422\u044b \u0442\u043e\u043b\u043a\u0430\u0435\u0448\u044c \u0442\u0440\u0430\u0432\u043a\u0443 \u0442\u043e\u043b\u044c\u043a\u043e \u0432 \u0413\u0422\u0410\\n\u0422\u044b \u0442\u0443\u043f\u0438\u0448\u044c \u0442\u0430\u043a \u0441\u0438\u043b\u044c\u043d\u043e\\n\u0422\u044b \u0442\u0443\u043f\u0438\u0448\u044c \u043a\u0430\u043a \u0431\u0443\u0434\u0442\u043e \u0434\u0432\u0430 \u043b\u043e\u0445\u0430\\n\u041a\u0430\u043a\u0438\u0435 \u043d\u0430\u0445\u0443\u0439...\u0027}\nYour goal is to generate one potential questions that a user might want to ask about this dataset. Consider the information contained in the provided columns and rows, and try to think of a meaningful question that could provide insights or useful information. For each question, provide the SQL query that would extract the relevant information from the dataset.\n\nOuput JSON format:\n{\"question\": \"[Insert question here]\", \"sql_query\": \"[Insert SQL query here]\"}\n\nPlease ensure that the SQL query retrieves relevant information from the dataset to answer the corresponding question accurately.\nReturn only the JSON object, do not add extra information." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("asoria/datasets_features_outputs", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("asoria/datasets_features_outputs") ``` </details>

SaProtHub/Dataset-Subcellular_Localization-DeepLoc

SaProtHub

--- license: mit --- # Description Subcellular Localization prediction is a 10-class classification task to predict where a protein locates in the cell, where each input protein *x* is mapped to a label *y* ∈ {0, 1, ..., 9}. The digital label means: 0: Nucleus 1: Cytoplasm 2: Extracellular 3: Mitochondrion 4: Cell.membrane 5: Endoplasmic.reticulum 6: Plastid 7: Golgi.apparatus 8: Lysosome/Vacuole 9: Peroxisome # Splits **Structure type:** AF2 The dataset is from [**DeepLoc: prediction of protein subcellular localization using deep learning**](https://academic.oup.com/bioinformatics/article/33/21/3387/3931857). We employ all proteins (proteins that lack AF2 structures are removed), and split them based on 70% structure similarity (see [ProteinShake](https://github.com/BorgwardtLab/proteinshake/tree/main)), with the number of training, validation and test set shown below: - Train: 10414 - Valid: 1368 - Test: 1368 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **name:** The UniProt ID of the protein - **seq:** The structure-aware sequence - **plddt**: pLDDT values at all positions - **label:** classification label of the sequence **1:** **···**

SaProtHub/Dataset-Binary_Localization-DeepLoc

SaProtHub

--- license: mit --- # Description Binary Localization prediction is a binary classification task where each input protein *x* is mapped to a label *y* ∈ {0, 1}, corresponding to either "membrane-bound" or "soluble" . The digital label means: 0: membrane-bound 1: soluble # Splits **Structure type:** AF2 The dataset is from [**DeepLoc: prediction of protein subcellular localization using deep learning**](https://academic.oup.com/bioinformatics/article/33/21/3387/3931857). We employ all proteins (proteins that lack AF2 structures are removed), and split them based on 70% structure similarity (see [ProteinShake](https://github.com/BorgwardtLab/proteinshake/tree/main)), with the number of training, validation and test set shown below: - Train: 6707 - Valid: 698 - Test: 807 # Data format We organize all data in LMDB format. The architecture of the databse is like: **length:** The number of samples **0:** - **name:** The UniProt ID of the protein - **seq:** The structure-aware sequence - **plddt**: pLDDT values at all positions - **label:** classification label of the sequence **1:** **···**

kamilakesbi/synthetic_dataset_jpn_2_less_overlap

kamilakesbi

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: speakers sequence: string - name: timestamps_start sequence: float64 - name: timestamps_end sequence: float64 splits: - name: train num_bytes: 54365206.0 num_examples: 30 download_size: 44850036 dataset_size: 54365206.0 configs: - config_name: default data_files: - split: train path: data/train-* ---