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librarian-bots

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Librarian Bots 59

jiddykwon/sf_data

jiddykwon

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: input dtype: string splits: - name: train num_bytes: 500673 num_examples: 1725 download_size: 67919 dataset_size: 500673 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/oh_v1.2_alpaca_x.5

mlfoundations-dev

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 1632358254 num_examples: 859777 download_size: 882984880 dataset_size: 1632358254 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/gp_sst2_clare_differential

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 13475.948881789138 num_examples: 204 download_size: 15329 dataset_size: 13475.948881789138 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/gp_sst2_faster-alzantot_differential_original

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 19554 num_examples: 247 download_size: 15227 dataset_size: 19554 configs: - config_name: default data_files: - split: train path: data/train-* ---

kanakapriya/phi3_796_nontrunc

kanakapriya

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 3548659 num_examples: 636 - name: test num_bytes: 879056 num_examples: 160 download_size: 2105327 dataset_size: 4427715 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Quinn777/AtomMATH-SFT

Quinn777

--- license: apache-2.0 ---

kapsb2171/eng-hin-500-consistent

kapsb2171

--- dataset_info: features: - name: translation struct: - name: en dtype: string - name: hi dtype: string splits: - name: train num_bytes: 588934 num_examples: 400 - name: validation num_bytes: 163417 num_examples: 100 download_size: 309545 dataset_size: 752351 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

mlfoundations-dev/oh_v1.2_slim_orca_x.25

mlfoundations-dev

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 943141308.0 num_examples: 498381 download_size: 493821964 dataset_size: 943141308.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

kowndinya23/flan2022-zeroshot-instr-inpt-outp-25000

kowndinya23

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 24667039 num_examples: 25000 - name: validation num_bytes: 1723020 num_examples: 1806 download_size: 11471934 dataset_size: 26390059 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

modeltrainer1/finetuning_demo

modeltrainer1

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 14235 num_examples: 34 download_size: 5477 dataset_size: 14235 configs: - config_name: default data_files: - split: train path: data/train-* ---

dreamyou/ta

dreamyou

--- task_categories: - image-segmentation tags: - roboflow - roboflow2huggingface --- <div align="center"> <img width="640" alt="dreamyou/ta" src="https://huggingface.co/datasets/dreamyou/ta/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['melon'] ``` ### Number of Images ```json {'valid': 100, 'test': 100, 'train': 400} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("dreamyou/ta", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/fifialfi/skripsi2-wb4wo/dataset/1](https://universe.roboflow.com/fifialfi/skripsi2-wb4wo/dataset/1?ref=roboflow2huggingface) ### Citation ``` @misc{ skripsi2-wb4wo_dataset, title = { Skripsi2 Dataset }, type = { Open Source Dataset }, author = { fifialfi }, howpublished = { \\url{ https://universe.roboflow.com/fifialfi/skripsi2-wb4wo } }, url = { https://universe.roboflow.com/fifialfi/skripsi2-wb4wo }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2024 }, month = { oct }, note = { visited on 2024-12-06 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on October 17, 2024 at 9:17 AM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand and search unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com The dataset includes 600 images. Melon are annotated in COCO format. The following pre-processing was applied to each image: * Resize to 640x640 (Stretch) No image augmentation techniques were applied.

vedikap11/school_projj

vedikap11

--- dataset_info: features: - name: Context dtype: string - name: Response dtype: string splits: - name: train num_bytes: 1643 num_examples: 5 download_size: 4090 dataset_size: 1643 configs: - config_name: default data_files: - split: train path: data/train-* ---

sumuks/fairytales_single_shot_questions

sumuks

--- dataset_info: features: - name: title dtype: string - name: summary dtype: string - name: chunk dtype: string - name: test_audience dtype: string - name: document_analysis dtype: string - name: question_type dtype: string - name: question dtype: string - name: answer dtype: string - name: estimated_difficulty dtype: int64 - name: citations dtype: string - name: generating_model dtype: string splits: - name: train num_bytes: 13628807 num_examples: 2603 download_size: 3350848 dataset_size: 13628807 configs: - config_name: default data_files: - split: train path: data/train-* ---

feedbackagent/llama3_8b_reflection1

feedbackagent

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: my_solu dtype: string - name: old_solu dtype: string splits: - name: train num_bytes: 2711102709 num_examples: 597952 download_size: 788967540 dataset_size: 2711102709 configs: - config_name: default data_files: - split: train path: data/train-* ---

feedbackagent/llama3_8b_reflection2

feedbackagent

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: my_solu dtype: string - name: old_solu dtype: string splits: - name: train num_bytes: 3169596544 num_examples: 523208 download_size: 911054340 dataset_size: 3169596544 configs: - config_name: default data_files: - split: train path: data/train-* ---

alleina/Real-Estate-Property-Image-Dataset-Lamudi-Facebook-Ohmyhome-PerchanceAI

alleina

--- license: apache-2.0 language: - en tags: - lamudi - facebook - ohmyhomes - real-estate-property - real-estate - real-estate-properties - AI-generated-real-estate-properties - watermarked-dataset - watermarked-real-estate-properties - real-estate-images size_categories: - 1K<n<10K --- ## Real Estate Property Dataset **Overview** This dataset is designed to train and evaluate a fraud filter system and image matching system for real estate properties. It comprises a diverse range of real estate images, including authentic images and AI-generated images. **Dataset Composition** 1. **AI-Generated Images:** * **Source:** Perchance AI * **Generation Process:** - A diverse set of properties was generated using Gemini AI, covering various property types, architectural styles, and features. - Gemini AI generated a dataset for each property type by randomly choosing from various sets of architectural styles, property categories (interior/exterior), and feature names (e.g., bedroom, facade): - **Property Types (11):** Condominium, House and Lot, Townhouse, Apartment, Bungalow, Villa, Office Space, Industrial Space, Commercial Lot, Land, Farm, Hotel - **Architectural Styles (10):** Colonial, Victorian, Tudor, Ranch, Cape Cod, Mediterranean, Modern, Contemporary, Industrial, Rustic - **Property Categories (2):** Interior, Exterior - **Interior Features (13):** Living Room, Dining Room, Kitchen, Bedrooms, Bathrooms, Hallways, Stairs, Basement, Attic, Closet, Pantry, Laundry Room, Family Room, Office - **Exterior Features (14):** Lot, Facade, Roof, Windows, Doors, Porch, Patio, Deck, Balcony, Garage, Driveway, Landscaping, Fence - Image descriptions were created based on these properties. - Perchance AI was used to generate images based on these descriptions. * **Data Format:** CSV file containing: - `ID` - `Image Description` - `Image Title` - `Property Type` - `Architectural Style` - `Property Category` - `Feature Name` * **Total Images:** 1000 (training) + 1000 (testing) 2. **Authentic Images:** * **Source:** Facebook Marketplace * **Data Collection:** Scraped using Python, Beautiful Soup, and Selenium. * **Data Format:** CSV file containing: - `image_paths` - `title` - `price` - `post_url` - `location` * **Total Properties:** 1000 * **Total Images:** 8158 3. **Copyrighted Images:** * **Source:** Lamudi and Ohmyhome * **Data Collection:** Scraped using Python and Beautiful Soup. * **Data Types:** - **No EXIF Data:** - **Source:** Lamudi - **Data Format:** CSV file containing: - `ID` - `Title` - `Price` - `Address` - `Num_of_Bedrooms` - `Num_of_Bathrooms` - `Floor_Area` - `Description` - `List_of_Amenities` - `Image URLs` - `Property URL` - **Total Properties:** 1000 - **Total Images:** 3000 - **Watermarked Images:** - **Source:** Ohmyhome - **Data Collection:** Python was used to generate watermarked images by adding watermarks to the original images. Watermarks were added randomly in terms of font style, size, case, and location on a contrasting background. Three types of watermarks were created: logo, transparent logo, and text. - **Data Format:** CSV file containing: - `Image URL` - `Developer` - `Logo URL` - **Total Images:** 1500 (500 transparent logo, 500 text watermark, 500 logo watermark) **Ethical Considerations:** * **Data Privacy:** Sensitive information, such as personal addresses and contact details, was removed from the dataset. * **Copyright Compliance:** Images were used in accordance with fair use principles and relevant copyright laws. * **Data Bias:** Efforts were made to ensure a balanced and representative dataset, considering factors like property type, location, and image quality. This dataset provides a valuable resource for training and evaluating models for real estate image matching and fraud detection. By combining authentic and AI-generated images, the dataset enables the development of robust models that can accurately distinguish between real and fake images and identify potential copyright infringements.

pxyyy/rlhflow_mixture_scalebio_sampled-nolisa-250k

pxyyy

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: conversation_id dtype: int64 splits: - name: train num_bytes: 411716390.5445725 num_examples: 250000 download_size: 160285765 dataset_size: 411716390.5445725 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "rlhflow_mixture_scalebio_sampled-nolisa-600k" ``` weight = { 'SlimOrca': 0.34525978565216064, 'dart-math-uniform': 0.23386941850185394, 'GPT4-LLM-Cleaned': 0.19111572206020355, 'MathInstruct': 0.16642746329307556, 'GPTeacher-General-Instruct': 0.042891550809144974, 'ShareGPT_V3_unfiltered_cleaned_split_no_imsorry': 0.006720397621393204, 'UltraInteract_sft': 0.0042861211113631725, 'WizardLM_evol_instruct_V2_196k': 0.004021201748400927, 'Magicoder-Evol-Instruct-110K': 0.00360115640796721, 'orca-math-word-problems-200k': 0.001807231456041336, } ``` https://wandb.ai/may_i_kiss_you/bilevel-optimization/runs/fyx1mc5z?nw=nwuserwtysalt [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sicheng1806/New-energy-power-plant

sicheng1806

--- license: openrail size_categories: - 100M<n<1B task_categories: - feature-extraction --- ## New-energy-power-plant 这个数据集来自2024第十三届认证杯数学中国数学建模国际赛（小美赛）D题的原始数据，稍微转换为csv格式。

yxnd150150/uieb_llm

yxnd150150

--- dataset_info: features: - name: instruction dtype: string - name: input_image dtype: image - name: ground_truth_image dtype: image splits: - name: train num_bytes: 107627969.0 num_examples: 700 download_size: 107338962 dataset_size: 107627969.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

AhmedBadawy11/UAE18000

AhmedBadawy11

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: validation num_bytes: 54591893.0 num_examples: 874 - name: train num_bytes: 1013988237.2 num_examples: 16320 download_size: 1070319856 dataset_size: 1068580130.2 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* ---

Deepri24/Thirukural_EnglishMeaning_Updated.csv

Deepri24

--- license: mit ---

thexdk/wiki_ru

thexdk

--- dataset_info: features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 8196489373 num_examples: 1097341 download_size: 3983241226 dataset_size: 8196489373 configs: - config_name: default data_files: - split: train path: data/train-* ---

amuvarma/audio-in-out-10k_part1

amuvarma

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: audio dtype: audio - name: answer_audio dtype: audio - name: facodec_0 sequence: int64 - name: facodec_1 sequence: int64 - name: facodec_2 sequence: int64 - name: facodec_3 sequence: int64 - name: facodec_4 sequence: int64 - name: facodec_5 sequence: int64 - name: spk_embs sequence: float64 splits: - name: train num_bytes: 4320277178.0 num_examples: 10000 download_size: 3857520470 dataset_size: 4320277178.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

nielsr/gemini-results-2024-11-29

nielsr

--- dataset_info: features: - name: arxiv_id dtype: string - name: reached_out_link dtype: string - name: reached_out_success dtype: float64 - name: reached_out_note dtype: string - name: num_models dtype: float64 - name: num_datasets dtype: float64 - name: num_spaces dtype: float64 - name: title dtype: string - name: github dtype: string - name: github_stars dtype: float64 - name: conference_name dtype: string - name: upvotes dtype: int64 - name: num_comments dtype: int64 - name: github_mention_hf dtype: float64 - name: has_artifact dtype: bool - name: submitted_by dtype: string - name: date dtype: string - name: gemini_results struct: - name: github_url dtype: string - name: new_datasets dtype: string - name: new_model_checkpoints dtype: string - name: note dtype: string - name: project_page_url dtype: string - name: reaching_out dtype: string - name: reasoning dtype: string - name: gemini_github_url dtype: string - name: gemini_new_datasets dtype: string - name: gemini_new_model_checkpoints dtype: string - name: gemini_note dtype: string - name: gemini_project_page_url dtype: string - name: gemini_reaching_out dtype: string - name: gemini_reasoning dtype: string splits: - name: train num_bytes: 11533 num_examples: 3 download_size: 38739 dataset_size: 11533 configs: - config_name: default data_files: - split: train path: data/train-* ---

ivrejchik/medmcqa-instruction

ivrejchik

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 100074248 num_examples: 182822 - name: test num_bytes: 417957 num_examples: 6150 - name: validation num_bytes: 1491232 num_examples: 4183 download_size: 65245759 dataset_size: 101983437 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

SharanShivram/latestv2

SharanShivram

--- dataset_info: features: - name: content dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 2099252 num_examples: 1974 download_size: 779082 dataset_size: 2099252 configs: - config_name: default data_files: - split: train path: data/train-* ---

yufan/Preference_Dataset_Merged

yufan

--- license: unknown dataset_info: features: - name: prompt 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: source dtype: string splits: - name: train num_bytes: 3228930805 num_examples: 597620 download_size: 1797337660 dataset_size: 3228930805 configs: - config_name: default data_files: - split: train path: data/train-* --- Collect the famous preference dataset, and convert to unified format The dataset source are: https://huggingface.co/datasets/lmarena-ai/arena-human-preference-55k https://huggingface.co/datasets/trl-internal-testing/hh-rlhf-helpful-base-trl-style https://huggingface.co/datasets/lmsys/mt_bench_human_judgments https://huggingface.co/datasets/openbmb/UltraFeedback https://huggingface.co/datasets/allenai/llama-3.1-tulu-3-70b-preference-mixture

anthracite-org/pixmo-point-explanations-images

anthracite-org

--- language: - en license: odc-by task_categories: - visual-question-answering dataset_info: features: - name: image dtype: image - name: image_url dtype: string - name: image_sha256 dtype: string - name: question dtype: string - name: response dtype: string - name: matching_hash dtype: bool splits: - name: train num_bytes: 17801658654.26 num_examples: 71674 download_size: 19065050774 dataset_size: 17801658654.26 configs: - config_name: default data_files: - split: train path: data/train-* --- Big thanks to Ai2 for releasing the original [PixMo-Point-Explanations](https://huggingface.co/datasets/allenai/pixmo-point-explanations) dataset. To preserve the images and simplify usage of the dataset, we are releasing this version, which includes downloaded images. Note that while some of the images had mismatched hashes after downloading, the vast majority of those still visually match the question/answer pairs, so we decided to leave them in. If you want to, you can filter those out using the `matching_hash` column. # PixMo-Point-Explanations PixMo-Point-Explanations is a dataset of images, questions, and answers with explanations that can include in-line points that refer to parts of the image. It can be used to train vison language models to respond to questions through a mixture of text and points. PixMo-Point-Explanations is part of the [PixMo dataset collection](https://huggingface.co/collections/allenai/pixmo-674746ea613028006285687b) and was used to train the [Molmo family of models](https://huggingface.co/collections/allenai/molmo-66f379e6fe3b8ef090a8ca19) We consider this dataset experimental, while these explanations can be very informative we have also seen models can hallucinate more when generating outputs of this sort. For that reason, the Molmo models are trained to only generate outputs like this when specifically requested by prefixing input questions with "point_qa:". This mode can be used in the [Molmo demo](https://multimodal-29mpz7ym.vercel.app/share/2921825e-ef44-49fa-a6cb-1956da0be62a) Quick links: - 📃 [Paper](https://molmo.allenai.org/paper.pdf) - 🎥 [Blog with Videos](https://molmo.allenai.org/blog) ## Loading ```python data = datasets.load_dataset("anthracite-org/pixmo-point-explanations-images") ``` ## Data Format The in-line points use a format from the LLM/annotators that does not exactly match the Molmo format. The data includes some fields derived from these responses to make them easier to parse, these fields can be null if the original response was not parsed. - `parsed_response` responses with the text "<|POINT|>" where the inline point annotations were - `alt_text` the alt text for each point annotation in the response - `inline_text` the inline text for each point annotation in the response - `points` the list-of-list of points for each point annotation ## License This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes data generated from Claude which are subject to Anthropic [terms of service](https://www.anthropic.com/legal/commercial-terms) and [usage policy](https://www.anthropic.com/legal/aup).

HappyAIUser/Atcgpt-Fixed2

HappyAIUser

--- license: apache-2.0 task_categories: - text-generation - text2text-generation language: - en size_categories: - 1K<n<10K tags: - conversational - instruction-tuning --- # Dataset Card for Atcgpt-Fixed2 This dataset contains instruction-input-output pairs converted to ShareGPT format, designed for instruction tuning and text generation tasks. ## Dataset Description The dataset consists of carefully curated instruction-input-output pairs, formatted for conversational AI training. Each entry contains: - An instruction that specifies the task - An optional input providing context - A detailed output that addresses the instruction ## Usage This dataset is particularly suitable for: - Instruction tuning of language models - Training conversational AI systems - Fine-tuning for specific domain knowledge

RyanYr/reflect_mini8bSFTt2_mini8BSFTDPOt1_om2_it2

RyanYr

--- dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: answer dtype: string - name: problem_source dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@2_per_reflection sequence: string - name: response@3 sequence: string - name: response@4_per_reflection sequence: string - name: response@4 sequence: string splits: - name: train num_bytes: 1812369148 num_examples: 20000 download_size: 498896395 dataset_size: 1812369148 configs: - config_name: default data_files: - split: train path: data/train-* ---

RyanYr/reflect_mini8bSFTt2_mini8BSFTDPOt1_om2_it3_crtc

RyanYr

--- dataset_info: features: - name: problem dtype: string - name: generated_solution dtype: string - name: answer dtype: string - name: problem_source dtype: string - name: response@0 sequence: string - name: response@1 sequence: string - name: response@2 sequence: string - name: response@2_per_reflection sequence: string - name: response@3 sequence: string - name: response@4_per_reflection sequence: string - name: response@4 sequence: string - name: response@5 sequence: string - name: response@6_per_reflection sequence: string splits: - name: train num_bytes: 2185466209 num_examples: 20000 download_size: 622750386 dataset_size: 2185466209 configs: - config_name: default data_files: - split: train path: data/train-* ---

selfrew/filtered_data_sft

selfrew

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: level dtype: string - name: type dtype: string - name: my_solu dtype: string - name: pred sequence: string - name: turn dtype: int64 - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 1327148858.786616 num_examples: 160313 download_size: 314166841 dataset_size: 1327148858.786616 configs: - config_name: default data_files: - split: train path: data/train-* ---

artnoage/orpo2

artnoage

--- dataset_info: features: - name: id dtype: int64 - name: prompt struct: - name: content dtype: string - name: role dtype: string - name: chosen struct: - name: content dtype: string - name: role dtype: string - name: rejected struct: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: bifurcation_point dtype: int64 splits: - name: train num_bytes: 12633074 num_examples: 7719 download_size: 3897870 dataset_size: 12633074 configs: - config_name: default data_files: - split: train path: data/train-* ---

cen001/1270ganqingceshi11

cen001

--- dataset_info: features: - name: conversation list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 44061 num_examples: 98 download_size: 27407 dataset_size: 44061 configs: - config_name: default data_files: - split: train path: data/train-* ---

ADHIZ/image_leelahanu1

ADHIZ

--- dataset_info: features: - name: file_name dtype: string - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 1678972.0 num_examples: 2 download_size: 1680896 dataset_size: 1678972.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

rikioka/training_data_v1

rikioka

--- dataset_info: features: - name: query dtype: string - name: pos sequence: string - name: neg sequence: string - name: category dtype: float64 - name: type dtype: string - name: prompt dtype: string - name: pos_scores sequence: float32 - name: neg_scores sequence: float32 splits: - name: train num_bytes: 21773514 num_examples: 12862 download_size: 4560318 dataset_size: 21773514 configs: - config_name: default data_files: - split: train path: data/train-* ---

1312354o/llama-ros2

1312354o

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1236245.9398194584 num_examples: 1794 - name: test num_bytes: 137820.06018054162 num_examples: 200 download_size: 733471 dataset_size: 1374066.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

wcyat/lihkg-story-sweet-4k

wcyat

--- dataset_info: features: - name: text dtype: string - name: title dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 107906 num_examples: 19 - name: test num_bytes: 11571 num_examples: 2 download_size: 103676 dataset_size: 119477 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Fyee/yoga_kg_data

Fyee

--- license: apache-2.0 ---

amuvarma/26k-stts-duplex-convos

amuvarma

--- dataset_info: features: - name: question dtype: string - name: question_audio dtype: audio - name: answer dtype: string - name: answer_audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 19733823351.66848 num_examples: 26588 download_size: 25141534668 dataset_size: 19733823351.66848 configs: - config_name: default data_files: - split: train path: data/train-* ---

anindaghosh/cs-gy-6613-rag-instruct-set

anindaghosh

--- configs: - config_name: default data_files: - split: train path: "dataset.csv" license: apache-2.0 ---

feedbackagent/prompt_4_gen_reflection_max5

feedbackagent

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: problem dtype: string - name: response dtype: string splits: - name: train num_bytes: 37677968 num_examples: 37389 download_size: 14112497 dataset_size: 37677968 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/oh_v1.3_airoboros_x2

mlfoundations-dev

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 1481037951.0 num_examples: 893460 download_size: 817372079 dataset_size: 1481037951.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

HoangPhuc7678/OJT12

HoangPhuc7678

--- dataset_info: features: - name: img dtype: image - name: gt dtype: image splits: - name: train num_bytes: 30742007.0 num_examples: 34 download_size: 30744566 dataset_size: 30742007.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/oh_v1.3_alpaca_x8

mlfoundations-dev

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 1570543647.0 num_examples: 1132996 download_size: 866322450 dataset_size: 1570543647.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

mlfoundations-dev/oh_v1.3_camel_biology_x.125

mlfoundations-dev

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: shard_id dtype: string splits: - name: train num_bytes: 1349849643.0 num_examples: 845620 download_size: 745698744 dataset_size: 1349849643.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

underctrl/handcamera_double-block_2-colors_pick-up_80

underctrl

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "unknown", "total_episodes": 80, "total_frames": 40026, "total_tasks": 1, "total_videos": 240, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:80" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "motor_0", "motor_1", "motor_2", "motor_3", "motor_4", "motor_5" ] } }, "observation.images.android": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 30.0, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.webcam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 30.0, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.handcam": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channel" ], "video_info": { "video.fps": 30.0, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "action": { "dtype": "float32", "shape": [ 6 ], "names": { "motors": [ "motor_0", "motor_1", "motor_2", "motor_3", "motor_4", "motor_5" ] } }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "next.done": { "dtype": "bool", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

feedbackagent/reflection_eval_prompt2

feedbackagent

--- dataset_info: features: - name: gt dtype: string - name: idx dtype: int64 - name: prompt dtype: string - name: responses sequence: string - name: problem dtype: string - name: response dtype: string - name: reflection dtype: string splits: - name: train num_bytes: 9708251715 num_examples: 299072 download_size: 1050868978 dataset_size: 9708251715 configs: - config_name: default data_files: - split: train path: data/train-* ---

yin30lei/wildlife_light_image_wildme

yin30lei

--- license: other license_name: attribution-noncommercial-noderivs-license license_link: LICENSE configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: width dtype: int64 - name: height dtype: int64 - name: bboxes dtype: string - name: areas dtype: string - name: normalized_bboxes dtype: string - name: category dtype: string - name: label dtype: string splits: - name: train num_bytes: 13124983482.38 num_examples: 8188 download_size: 10426103281 dataset_size: 13124983482.38 ---

akkikiki/global_mmlu_ja_edited

akkikiki

--- dataset_info: features: - name: sample_id dtype: string - name: subject dtype: string - name: subject_category dtype: string - name: question dtype: string - name: option_a dtype: string - name: option_b dtype: string - name: option_c dtype: string - name: option_d dtype: string - name: answer dtype: string - name: required_knowledge dtype: string - name: time_sensitive dtype: string - name: reference dtype: string - name: culture dtype: string - name: region dtype: string - name: country dtype: string - name: cultural_sensitivity_label dtype: string - name: is_annotated dtype: bool - name: is_edited dtype: bool splits: - name: train num_bytes: 167465 num_examples: 285 download_size: 99115 dataset_size: 167465 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 language: - ja size_categories: - n<1K --- Associated code for this dataset is available at https://github.com/akkikiki/global_mmlu_edit ## Loading the dataset ``` from datasets import load_dataset # From JSON ds = load_dataset("akkikiki/global_mmlu_ja_edited") # If migrating with the original Global MMLU, remove additional columns ds = ds.remove_columns("is_edited") # load HF dataset global_mmlu_ja = load_dataset("CohereForAI/Global-MMLU", 'ja') global_mmlu_ja["dev"] = ds["train"] ``` ## Additional Information Authorship * Yoshinari Fujinuma Licensing Information This dataset can be used for any purpose, under the terms of the Apache 2.0 License. Citation Information The original Global-MMLU dataset is at https://huggingface.co/datasets/CohereForAI/Global-MMLU ``` Original preprint: @misc{singh2024globalmmluunderstandingaddressing, title={Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation}, author={Shivalika Singh and Angelika Romanou and Clémentine Fourrier and David I. Adelani and Jian Gang Ngui and Daniel Vila-Suero and Peerat Limkonchotiwat and Kelly Marchisio and Wei Qi Leong and Yosephine Susanto and Raymond Ng and Shayne Longpre and Wei-Yin Ko and Madeline Smith and Antoine Bosselut and Alice Oh and Andre F. T. Martins and Leshem Choshen and Daphne Ippolito and Enzo Ferrante and Marzieh Fadaee and Beyza Ermis and Sara Hooker}, year={2024}, eprint={2412.03304}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.03304}, } ``` If you use this dataset, please cite the following. No preprint as of now but let me know if I should :) ``` @misc {fujinuma2024mmluv2, author = {Fujinuma, Yoshinari}, title = {JA Revised v2 of Global-MMLU}, howpublished = {\url{https://huggingface.co/datasets/akkikiki/global_mmlu_ja_v2}}, url = {https://huggingface.co/datasets/akkikiki/global_mmlu_ja_v2}, type = {dataset}, year = {2024}, month = {Dec}, timestamp = {2024-12-07}, } ```

oceanpty/Skywork-pref-num-62296-config-lla31-70b-inst-n-sample-5-temp-07-top-p-1

oceanpty

--- dataset_info: features: - name: id dtype: int64 - name: instruction dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: source dtype: string - name: responses sequence: string - name: actions sequence: string - name: rewards sequence: float64 - name: n_prompt_tokens sequence: int64 - name: n_completion_tokens sequence: int64 - name: total_tokens sequence: int64 - name: best_reward dtype: float64 - name: best_response dtype: string - name: best_model dtype: string splits: - name: train num_bytes: 1215259520 num_examples: 62296 download_size: 555961463 dataset_size: 1215259520 configs: - config_name: default data_files: - split: train path: data/train-* ---

amyguan/newswire-20-30-labor

amyguan

--- dataset_info: features: - name: article dtype: string - name: byline dtype: string - name: dates sequence: string - name: newspaper_metadata list: - name: lccn dtype: string - name: newspaper_city dtype: string - name: newspaper_state dtype: string - name: newspaper_title dtype: string - name: antitrust dtype: int64 - name: civil_rights dtype: int64 - name: crime dtype: int64 - name: govt_regulation dtype: int64 - name: labor_movement dtype: int64 - name: politics dtype: int64 - name: protests dtype: int64 - name: ca_topic dtype: string - name: ner_words sequence: string - name: ner_labels sequence: string - name: wire_city dtype: string - name: wire_state dtype: string - name: wire_country dtype: string - name: wire_coordinates sequence: float64 - name: wire_location_notes dtype: string - name: people_mentioned list: - name: person_gender dtype: string - name: person_name dtype: string - name: person_occupation dtype: string - name: wikidata_id dtype: string - name: cluster_size dtype: int64 - name: year dtype: int64 splits: - name: train num_bytes: 93820506.88030969 num_examples: 18824 download_size: 23185111 dataset_size: 93820506.88030969 configs: - config_name: default data_files: - split: train path: data/train-* ---

antphb/TD-ViOCR-CPVQA

antphb

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: width dtype: int64 - name: height dtype: int64 - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: TD_Vintext_CPVQA num_bytes: 228364419.0 num_examples: 1056 - name: TD_MTCPVQA num_bytes: 668048263.865 num_examples: 1389 - name: TD_OpenViCPVQA num_bytes: 1403285019.368 num_examples: 7842 - name: TD_ViTextCPVQA num_bytes: 5557837266.074 num_examples: 13286 - name: TD_VisualMediaCPVQA num_bytes: 628929083.304 num_examples: 1416 download_size: 7189561745 dataset_size: 8486464051.611 configs: - config_name: default data_files: - split: TD_Vintext_CPVQA path: data/TD_Vintext_CPVQA-* - split: TD_MTCPVQA path: data/TD_MTCPVQA-* - split: TD_OpenViCPVQA path: data/TD_OpenViCPVQA-* - split: TD_ViTextCPVQA path: data/TD_ViTextCPVQA-* - split: TD_VisualMediaCPVQA path: data/TD_VisualMediaCPVQA-* language: - vi task_categories: - visual-question-answering - image-to-text tags: - vision - image-text-to-text --- ## Overview Based on our research and exploration of datasets primarily related to question-answering tasks involving Vietnamese optical character recognition (OCR), we have decided to use datasets such as ViTextVQA [1], MTVQA [2], Vintext [3], OpenViVQA [4], and a custom dataset we collected related to VisualMedia. | Dataset Name | Image | Question - Answer pairs | |-------------------------|---------|--------------------------| | ViTextVQA | 13,409 | 40,314 | | Viet-Vintext-gemini-VQA | 1,056 | 5,281 | | MTVQA | 1,389 | 4,884 | | OpenViVQA | 10,199 | 27,275 | | TD-VisualMedia-VQA | 1,480 | 6,337 | ## Enhanced Complexity of Dataset Base on these dataset above, we aim to utilize Vietnamese VQA datasets and leverage AI Gemini 1.5 to create more complex datasets by combining questions into longer, more challenging ones involving multiple subjects. Additionally, answers will be refined to include clear subjects and predicates, enhancing the precision and reasoning capability of the VQA model.  The distribution of question-answer pairs has also become more diverse after being made more complex. Let’s take a look at the charts below. | Dataset Name | Task | Image | Complex Question - Answer pairs | |--------------------------|---------|---------|----------------------------------| | TD-Vintext-CPVQA | OCR QA | 1,056 | 4,312 | | TD-MTCPVQA | OCR QA | 1,389 | 5,503 | | TD-ViTextCPVQA | OCR QA | 13,409 | 34,253 | | TD-OpenViCPVQA | OCR QA | 7,859 | 27,980 | | TD-VisualMedia-CPVQA | OCR QA | 1,480 | 4,725 | | **Total** | | **25,193** | **76,773** | ----      As you can see, the data distribution has changed, with questions tending to be longer and involving more subjects, while answers are more detailed and meaningful. This is a crucial factor in helping the model understand complex contexts. ## Example <div align="center"> <img width = 400 alt="image" src="https://res.cloudinary.com/dk2cnqatr/image/upload/v1733903889/dataset1_vc9hkx.jpg"> </div> **Original** ``` Q: khách hàng sống ở đâu sẽ được giảm 2.5% tại fpt shop? A: thành phố hồ chí minh , đồng nai và bình dương. Q: fpt shop ưu đãi giảm giá lên đến bao nhiêu phần trăm? A: fpt shop ưu đãi giảm giá lên đến 50%. ``` **Complex Enhanced** ``` Q: FPT Shop cam kết giao hàng tận nhà trong thời gian bao lâu và có tính phí không? A: Theo quảng cáo, FPT Shop cam kết giao hàng tận nhà trong vòng một giờ và miễn phí giao hàng. Q: Ngoài laptop, quảng cáo của FPT Shop còn hiển thị những loại sản phẩm điện tử nào khác? A: Bên cạnh laptop, quảng cáo của FPT Shop cũng trưng bày hình ảnh của điện thoại thông minh và đồng hồ thông minh. ``` <div align="center"> <img width = 400 alt="image" src="https://res.cloudinary.com/dk2cnqatr/image/upload/v1733903889/dataset2_er9o4r.jpg"> </div> **Original** ``` Q: Tên của công ty này là gì? A: Lộc nam việt Q: Địa chỉ của công ty này ở đâu? A: 1695 bùi ăn hòa ( quốc lộ 15 cũ ) , p . phước tân , tp . biên hòa , đồng nai ``` **Complex Enhanced** ``` Q: Công ty TNHH Lộc Nam Việt kinh doanh lĩnh vực gì, và địa chỉ trụ sở chính của công ty ở đâu? A: Công ty TNHH Lộc Nam Việt là công ty chuyên kinh doanh sơn, trụ sở chính của công ty ở 1695 Bùi Văn Hòa (Quốc lộ 15 cũ), P. Phước Tân, TP. Biên Hòa, Đồng Nai. Q: Ngoài website, khách hàng có thể liên lạc với Công ty TNHH Lộc Nam Việt bằng cách nào? A: Khách hàng có thể liên lạc với Công ty TNHH Lộc Nam Việt qua số điện thoại (061) 3939 451, 0979 938 545, 098 556 4245 hoặc email loc@locnamviet.com. ``` <div align="center"> <img width = 400 alt="image" src="https://res.cloudinary.com/dk2cnqatr/image/upload/v1733903888/dataset3_vrygux.jpg"> </div> **Original** ``` Q: Tên công ty là gì? A: Tên công ty là Công ty TNHH Dịch Vụ Giáo Dục EB Đà Nẵng. Q: Công ty hoạt động trong lĩnh vực gì? A: Công ty hoạt động trong lĩnh vực giáo dục. ``` **Complex Enhanced** ``` Q: Công ty tên gì, hoạt động trong lĩnh vực nào và địa chỉ ở đâu? A: Tên công ty là Công ty TNHH Dịch Vụ Giáo Dục EB Đà Nẵng, hoạt động trong lĩnh vực giáo dục và địa chỉ tại 113 Xuân Diệu, P.Thuận Phước, Q.Hải Châu, TP.Đà Nẵng. Q: Thông tin liên hệ của công ty bao gồm email, website và số fax là gì? A: Email của công ty là info@ebstem.edu.vn, website là http://ebstem.edu.vn và số Fax là 0511.31234567. ``` ## References 1. ViTextVQA: A Large-Scale Visual Question Answering Dataset for Evaluating Vietnamese Text Comprehension in Images. [Link](https://arxiv.org/abs/2404.10652) 2. MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering [Link](https://arxiv.org/abs/2405.11985) 3. 5CD-AI/Viet-Vintext-gemini-VQA [Link](https://huggingface.co/datasets/5CD-AI/Viet-Vintext-gemini-VQA?) 4. OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese [Link](https://arxiv.org/abs/2305.04183) ## Authors - Thanh Nguyen - thannd2462245@gmail.com - Du Nguyen - julowin2002@gmail.com

RichMiguel/Continuation

RichMiguel

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: Bills dtype: image - name: Values dtype: string splits: - name: train num_bytes: 1767216.0 num_examples: 150 download_size: 1752148 dataset_size: 1767216.0 ---

lucaelin/generic_situational_action_dialogs_v1

lucaelin

--- dataset_info: features: - name: profession dtype: string - name: process dtype: string - name: situation_json dtype: string - name: action dtype: string - name: request dtype: string - name: requests dtype: string - name: function dtype: string - name: arguments dtype: string - name: result dtype: string - name: response dtype: string splits: - name: train num_bytes: 7869729.0 num_examples: 3383 download_size: 2334868 dataset_size: 7869729.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

lucaelin/generic_covas_commentary_v2

lucaelin

--- dataset_info: features: - name: profession dtype: string - name: process dtype: string - name: tools dtype: string - name: messages dtype: string splits: - name: train num_bytes: 6875073 num_examples: 708 download_size: 2268451 dataset_size: 6875073 configs: - config_name: default data_files: - split: train path: data/train-* ---

pnsahoo/30-40-macro-embedding

pnsahoo

--- dataset_info: features: - name: article dtype: string - name: byline dtype: string - name: dates sequence: string - name: newspaper_metadata list: - name: lccn dtype: string - name: newspaper_city dtype: string - name: newspaper_state dtype: string - name: newspaper_title dtype: string - name: antitrust dtype: int64 - name: civil_rights dtype: int64 - name: crime dtype: int64 - name: govt_regulation dtype: int64 - name: labor_movement dtype: int64 - name: politics dtype: int64 - name: protests dtype: int64 - name: ca_topic dtype: string - name: ner_words sequence: string - name: ner_labels sequence: string - name: wire_city dtype: string - name: wire_state dtype: string - name: wire_country dtype: string - name: wire_coordinates sequence: float64 - name: wire_location_notes dtype: string - name: people_mentioned list: - name: person_gender dtype: string - name: person_name dtype: string - name: person_occupation dtype: string - name: wikidata_id dtype: string - name: cluster_size dtype: int64 - name: year dtype: int64 - name: embedding sequence: float64 splits: - name: train num_bytes: 173155054 num_examples: 14524 download_size: 94896485 dataset_size: 173155054 configs: - config_name: default data_files: - split: train path: data/train-* ---

pnsahoo/30-40-civil-rights-embedding

pnsahoo

--- dataset_info: features: - name: article dtype: string - name: byline dtype: string - name: dates sequence: string - name: newspaper_metadata list: - name: lccn dtype: string - name: newspaper_city dtype: string - name: newspaper_state dtype: string - name: newspaper_title dtype: string - name: antitrust dtype: int64 - name: civil_rights dtype: int64 - name: crime dtype: int64 - name: govt_regulation dtype: int64 - name: labor_movement dtype: int64 - name: politics dtype: int64 - name: protests dtype: int64 - name: ca_topic dtype: string - name: ner_words sequence: string - name: ner_labels sequence: string - name: wire_city dtype: string - name: wire_state dtype: string - name: wire_country dtype: string - name: wire_coordinates sequence: float64 - name: wire_location_notes dtype: string - name: people_mentioned list: - name: person_gender dtype: string - name: person_name dtype: string - name: person_occupation dtype: string - name: wikidata_id dtype: string - name: cluster_size dtype: int64 - name: year dtype: int64 - name: embedding sequence: float64 splits: - name: train num_bytes: 72415700 num_examples: 6991 download_size: 43265124 dataset_size: 72415700 configs: - config_name: default data_files: - split: train path: data/train-* ---

pnsahoo/40-50-macro-embedding

pnsahoo

--- dataset_info: features: - name: article dtype: string - name: byline dtype: string - name: dates sequence: string - name: newspaper_metadata list: - name: lccn dtype: string - name: newspaper_city dtype: string - name: newspaper_state dtype: string - name: newspaper_title dtype: string - name: antitrust dtype: int64 - name: civil_rights dtype: int64 - name: crime dtype: int64 - name: govt_regulation dtype: int64 - name: labor_movement dtype: int64 - name: politics dtype: int64 - name: protests dtype: int64 - name: ca_topic dtype: string - name: ner_words sequence: string - name: ner_labels sequence: string - name: wire_city dtype: string - name: wire_state dtype: string - name: wire_country dtype: string - name: wire_coordinates sequence: float64 - name: wire_location_notes dtype: string - name: people_mentioned list: - name: person_gender dtype: string - name: person_name dtype: string - name: person_occupation dtype: string - name: wikidata_id dtype: string - name: cluster_size dtype: int64 - name: year dtype: int64 - name: embedding sequence: float64 splits: - name: train num_bytes: 118959700 num_examples: 9763 download_size: 63558500 dataset_size: 118959700 configs: - config_name: default data_files: - split: train path: data/train-* ---

pnsahoo/40-50-civil-rights-embedding

pnsahoo

--- dataset_info: features: - name: article dtype: string - name: byline dtype: string - name: dates sequence: string - name: newspaper_metadata list: - name: lccn dtype: string - name: newspaper_city dtype: string - name: newspaper_state dtype: string - name: newspaper_title dtype: string - name: antitrust dtype: int64 - name: civil_rights dtype: int64 - name: crime dtype: int64 - name: govt_regulation dtype: int64 - name: labor_movement dtype: int64 - name: politics dtype: int64 - name: protests dtype: int64 - name: ca_topic dtype: string - name: ner_words sequence: string - name: ner_labels sequence: string - name: wire_city dtype: string - name: wire_state dtype: string - name: wire_country dtype: string - name: wire_coordinates sequence: float64 - name: wire_location_notes dtype: string - name: people_mentioned list: - name: person_gender dtype: string - name: person_name dtype: string - name: person_occupation dtype: string - name: wikidata_id dtype: string - name: cluster_size dtype: int64 - name: year dtype: int64 - name: embedding sequence: float64 splits: - name: train num_bytes: 57689765 num_examples: 5471 download_size: 34287928 dataset_size: 57689765 configs: - config_name: default data_files: - split: train path: data/train-* ---

NAMAA-Space/mteb-eval-mrtydi

NAMAA-Space

--- dataset_info: features: - name: query dtype: string - name: positive sequence: string - name: negative sequence: string splits: - name: test num_bytes: 2362241 num_examples: 918 download_size: 1162857 dataset_size: 2362241 configs: - config_name: default data_files: - split: test path: data/test-* ---

selfrew/no_fitered_3epoch_2e6_bz128_tmp10_scaling_time

selfrew

--- dataset_info: features: - name: idx dtype: int64 - name: gt dtype: string - name: prompt dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string - name: rewards sequence: bool - name: my_solu sequence: string splits: - name: train num_bytes: 1444504458 num_examples: 355000 download_size: 452313835 dataset_size: 1444504458 configs: - config_name: default data_files: - split: train path: data/train-* ---

ashwin-4000/ros_instruct_dataset

ashwin-4000

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: metadata_total_chunks_retrieved dtype: int64 - name: metadata_total_batches_processed dtype: int64 - name: metadata_batch_size dtype: int64 - name: metadata_total_pairs dtype: int64 - name: metadata_total_tokens dtype: int64 - name: metadata_max_workers dtype: int64 - name: uploaded_at dtype: string splits: - name: train num_bytes: 1078713 num_examples: 1792 download_size: 502330 dataset_size: 1078713 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZCM5115/so100_test

ZCM5115

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "so100", "total_episodes": 5, "total_frames": 4042, "total_tasks": 1, "total_videos": 10, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:5" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 360, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 360, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.hikvision": { "dtype": "video", "shape": [ 360, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 360, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

KMH158/Dataset_150x10_t1ce

KMH158

--- dataset_info: features: - name: messages struct: - name: text dtype: string - name: images dtype: image splits: - name: train num_bytes: 4444644.4 num_examples: 1200 - name: eval num_bytes: 1066728.0 num_examples: 290 download_size: 3662529 dataset_size: 5511372.4 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* ---

feedbackagent/subset

feedbackagent

--- dataset_info: features: - name: gt dtype: string - name: idx dtype: int64 - name: prompt dtype: string - name: completions sequence: string - name: problem dtype: string - name: response dtype: string - name: reflection dtype: string splits: - name: train num_bytes: 1902121542 num_examples: 74768 download_size: 620758742 dataset_size: 1902121542 configs: - config_name: default data_files: - split: train path: data/train-* ---

disl/musinsa-snap-final

disl

--- dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 190190600.625 num_examples: 9667 download_size: 171116866 dataset_size: 190190600.625 configs: - config_name: default data_files: - split: train path: data/train-* ---

prezent-ml/lilly-augmented-clean

prezent-ml

--- dataset_info: features: - name: problem dtype: string - name: solution dtype: string splits: - name: train num_bytes: 3339203 num_examples: 2323 download_size: 1224498 dataset_size: 3339203 configs: - config_name: default data_files: - split: train path: data/train-* ---

Deepnoid/phrases_2

Deepnoid

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 15961690 num_examples: 288199 download_size: 4779478 dataset_size: 15961690 configs: - config_name: default data_files: - split: train path: data/train-* ---

vasu718/traders_questions

vasu718

--- license: mit ---

feecha/chatbot_zh_dataset

feecha

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 6660730789 num_examples: 6750047 download_size: 3996954739 dataset_size: 6660730789 configs: - config_name: default data_files: - split: train path: data/train-* ---

oceanpty/Self-J-score-w-ref-skywork-pref-ref-lla31-70b-inst-model-lla-31-70b-inst-thre-1

oceanpty

--- dataset_info: features: - name: rejected list: - name: content dtype: string - name: role dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: chosen_score dtype: float64 - name: rejected_score dtype: float64 splits: - name: train num_bytes: 217352360 num_examples: 44669 download_size: 110082438 dataset_size: 217352360 configs: - config_name: default data_files: - split: train path: data/train-* ---

Negev900/control_test_1

Negev900

--- license: mit task_categories: - controlled-image-generation language: - en tags: - controlnet - image-to-image --- # Negev900/control_test_1 ControlNet dataset with conditioning images. ## Dataset Description A dataset for training ControlNet models, containing original images paired with their conditioning images. ## Usage ```python from datasets import load_dataset dataset = load_dataset("Negev900/control_test_1") ``` ## Dataset Structure Each example contains: - `text`: The caption/text description - `image`: The original image - `conditioning_image`: The conditioning image for ControlNet

juliadollis/dataset_nlp_test

juliadollis

--- dataset_info: features: - name: pergunta dtype: string - name: resposta dtype: string splits: - name: train num_bytes: 167150.29881293493 num_examples: 100 download_size: 101853 dataset_size: 167150.29881293493 configs: - config_name: default data_files: - split: train path: data/train-* ---

gdurkin/s1_nlcd_sfbay_plus

gdurkin

--- dataset_info: features: - name: pixel_values dtype: image - name: labels dtype: image splits: - name: group_40_46 num_bytes: 145273040.072 num_examples: 1738 - name: group_30_39 num_bytes: 225842135.0 num_examples: 2500 - name: group_0_9 num_bytes: 212875058.256 num_examples: 2499 - name: group_10_19 num_bytes: 204868215.0 num_examples: 2500 - name: group_20_29 num_bytes: 226897607.0 num_examples: 2500 download_size: 1015859359 dataset_size: 1015756055.3280001 configs: - config_name: default data_files: - split: group_40_46 path: data/group_40_46-* - split: group_30_39 path: data/group_30_39-* - split: group_0_9 path: data/group_0_9-* - split: group_10_19 path: data/group_10_19-* - split: group_20_29 path: data/group_20_29-* ---

ferrazzipietro/IK_llama3.1-8b_e3c_16_16_0.05

ferrazzipietro

--- dataset_info: features: - name: inference_prompt dtype: string - name: sentence dtype: string - name: model_responses dtype: string - name: ground_truth dtype: string splits: - name: validation num_bytes: 88345 num_examples: 106 - name: test num_bytes: 712632 num_examples: 666 download_size: 271519 dataset_size: 800977 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* ---

ferrazzipietro/IK_llama3.1-8b_e3c_16_32_0.01

ferrazzipietro

--- dataset_info: features: - name: inference_prompt dtype: string - name: sentence dtype: string - name: model_responses dtype: string - name: ground_truth dtype: string splits: - name: validation num_bytes: 88529 num_examples: 106 - name: test num_bytes: 713174 num_examples: 666 download_size: 271274 dataset_size: 801703 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* ---

grahamakercustom/pbllm

grahamakercustom

--- license: apache-2.0 tags: - not-for-all-audiences size_categories: - 1K<n<10K ---

ahmedsamirtarjama/misconception_multiple_choice_quiz

ahmedsamirtarjama

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 1430747 num_examples: 1619 download_size: 631801 dataset_size: 1430747 configs: - config_name: default data_files: - split: train path: data/train-* ---

code-planning/deepseek-family

code-planning

--- dataset_info: features: - name: model_name dtype: string - name: benchmark_name dtype: string - name: benchmark_q_id dtype: string - name: input dtype: string - name: code_output dtype: string - name: is_correct_base dtype: string - name: is_correct_plus dtype: string - name: variant dtype: string - name: is_correct dtype: string splits: - name: train num_bytes: 19198447 num_examples: 7938 download_size: 7608631 dataset_size: 19198447 configs: - config_name: default data_files: - split: train path: data/train-* ---

albinandersson/Nordisk-Familjebok-Headword-Extraction-Dataset

albinandersson

--- license: cc-by-nc-sa-4.0 language: - sv train_size: 300075 test_size: 5000 content_types: - biographical - geographical - scientific - historical - cultural - technical pretty_name: "Nordisk Familjebok Headword Extraction Dataset" task_categories: - text-classification size_categories: - 100K<n<1M libraries: - datasets - pandas - polars - croissant modalities: - text formats: - jsonl date_published: "2024-12-09" version: "1.0.0" cite_as: "@dataset{albinandersson2024, title = {Nordisk Familjebok Headword Extraction Dataset}, author = {Albin Andersson and Salam Jonasson and Fredrik Wastring}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/albinandersson/Nordisk-Familjebok-Headword-Extraction-Dataset} }" croissant: "@context": "@vocab": "http://mlcommons.org/croissant/" schema: "https://schema.org/" citeAs: "http://mlcommons.org/croissant/citeAs" "@type": "Dataset" name: "Nordisk Familjebok Headword Extraction Dataset" description: "A dataset extracted from Nordisk Familjebok used for training a headword extraction model." url: "https://huggingface.co/datasets/albinandersson/Nordisk-Familjebok-Headword-Extraction-Dataset" version: "1.0.0" datePublished: "2024-12-09" citeAs: "@dataset{albinandersson2024, title = {Nordisk Familjebok Headword Extraction Dataset}, author = {Albin Andersson and Salam Jonasson and Fredrik Wastring}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/albinandersson/Nordisk-Familjebok-Headword-Extraction-Dataset} }" license: "https://creativecommons.org/licenses/by-nc-sa/4.0/" creator: "@type": "Organization" name: "Albin Andersson, Salam Jonasson, Fredrik Wastring" --- # Dataset Name: Nordisk Familjebok Headword Extraction Dataset ## Description This dataset contains pairs of extracted text and their corresponding headwords from all editions of Nordisk Familjebok. Each entry includes a `text` and a `headword` field. If a text has a corresponding headword, the `headword` field is populated; otherwise, it contains an empty string. This dataset is designed for training and evaluating headword extraction models. ## Files - **train_set.jsonl**: Contains text-headword pairs from the scraped version of Nordisk Familjebok. Entries are not manually verified. - **test_set.jsonl**: Contains manually verified entries ensuring higher accuracy and quality for evaluation purposes. ## Data Fields - **text**: A string representing an extraction of text (max 500 characters) from the scraped digitalized version of Nordisk Familjebok with all HTML tags removed. - **headword**: A string representing the corresponding headword. If no headword is present, this field is an empty string. ## Licensing This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Users can use, modify, and share this dataset for non-commercial purposes with appropriate credit and under the same license. ## Citation If you use this dataset, please cite it as follows: ```bibtex @misc{albinandersson2024, title = {Nordisk Familjebok Headword Extraction Dataset}, author = {Albin Andersson and Salam Jonasson and Fredrik Wastring}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/albinandersson/Nordisk-Familjebok-Headword-Extraction-Dataset} }

seanwhen/dasso100_test

seanwhen

--- license: apache-2.0 task_categories: - robotics tags: - LeRobot - so100 - tutorial configs: - config_name: default data_files: data/*/*.parquet --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot). ## Dataset Description - **Homepage:** [More Information Needed] - **Paper:** [More Information Needed] - **License:** apache-2.0 ## Dataset Structure [meta/info.json](meta/info.json): ```json { "codebase_version": "v2.0", "robot_type": "so100", "total_episodes": 2, "total_frames": 479, "total_tasks": 1, "total_videos": 4, "total_chunks": 1, "chunks_size": 1000, "fps": 30, "splits": { "train": "0:2" }, "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet", "video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4", "features": { "action": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.state": { "dtype": "float32", "shape": [ 6 ], "names": [ "main_shoulder_pan", "main_shoulder_lift", "main_elbow_flex", "main_wrist_flex", "main_wrist_roll", "main_gripper" ] }, "observation.images.laptop": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "observation.images.phone": { "dtype": "video", "shape": [ 480, 640, 3 ], "names": [ "height", "width", "channels" ], "info": { "video.fps": 30.0, "video.height": 480, "video.width": 640, "video.channels": 3, "video.codec": "av1", "video.pix_fmt": "yuv420p", "video.is_depth_map": false, "has_audio": false } }, "timestamp": { "dtype": "float32", "shape": [ 1 ], "names": null }, "frame_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "episode_index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "index": { "dtype": "int64", "shape": [ 1 ], "names": null }, "task_index": { "dtype": "int64", "shape": [ 1 ], "names": null } } } ``` ## Citation **BibTeX:** ```bibtex [More Information Needed] ```

amuvarma/eliasfiz-audio_pretrain_10m-facodec-1dups

amuvarma

--- dataset_info: features: - name: transcript dtype: string - name: facodec_0 sequence: int64 - name: facodec_1 sequence: int64 - name: facodec_2 sequence: int64 - name: facodec_3 sequence: int64 - name: facodec_4 sequence: int64 - name: facodec_5 sequence: int64 - name: spk_embs sequence: float64 splits: - name: train num_bytes: 147579503014 num_examples: 3243931 download_size: 28596207138 dataset_size: 147579503014 configs: - config_name: default data_files: - split: train path: data/train-* ---

amuvarma/CanopyLabs-audio_pretrain_10m-facodec-1dups

amuvarma

--- dataset_info: features: - name: transcript dtype: string - name: facodec_0 sequence: int64 - name: facodec_1 sequence: int64 - name: facodec_2 sequence: int64 - name: facodec_3 sequence: int64 - name: facodec_4 sequence: int64 - name: facodec_5 sequence: int64 - name: spk_embs sequence: float64 splits: - name: train num_bytes: 179995591117 num_examples: 3953797 download_size: 34870388777 dataset_size: 179995591117 configs: - config_name: default data_files: - split: train path: data/train-* ---

davidr99/blackjack-more-questions

davidr99

--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: multiple_choice_answer dtype: string splits: - name: train num_bytes: 452861865.261 num_examples: 1177 download_size: 71099230 dataset_size: 452861865.261 configs: - config_name: default data_files: - split: train path: data/train-* ---

seongil-dn/korean_retrieval_451949

seongil-dn

--- dataset_info: features: - name: anchor dtype: string - name: positive dtype: string - name: negative dtype: string - name: subset dtype: string splits: - name: train num_bytes: 1546738570 num_examples: 451949 download_size: 857531101 dataset_size: 1546738570 configs: - config_name: default data_files: - split: train path: data/train-* ---

yin30lei/wildlife_very_dark

yin30lei

--- dataset_info: features: - name: file_name dtype: string - name: image_id dtype: int64 - name: width dtype: int64 - name: height dtype: int64 - name: image dtype: image - name: labels dtype: string splits: - name: train num_bytes: 40595743.742 num_examples: 1638 - name: test num_bytes: 17273301.0 num_examples: 702 download_size: 36387229 dataset_size: 57869044.742 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

feedbackagent/train_reflection_eval1_with_rewards

feedbackagent

--- dataset_info: features: - name: gt dtype: string - name: idx dtype: int64 - name: prompt dtype: string - name: completions sequence: string - name: problem dtype: string - name: response dtype: string - name: reflection dtype: string - name: rewards sequence: bool - name: preds sequence: string splits: - name: train num_bytes: 1069622536 num_examples: 49998 download_size: 344998480 dataset_size: 1069622536 configs: - config_name: default data_files: - split: train path: data/train-* ---

feedbackagent/train_reflection_eval2_with_rewards2

feedbackagent

--- dataset_info: features: - name: gt dtype: string - name: idx dtype: int64 - name: prompt dtype: string - name: completions sequence: string - name: problem dtype: string - name: response dtype: string - name: reflection dtype: string - name: rewards sequence: bool - name: preds sequence: string splits: - name: train num_bytes: 3819118429 num_examples: 149071 download_size: 1242757096 dataset_size: 3819118429 configs: - config_name: default data_files: - split: train path: data/train-* ---

feedbackagent/train_reflection_eval3_with_rewards

feedbackagent

--- dataset_info: features: - name: gt dtype: string - name: idx dtype: int64 - name: prompt dtype: string - name: completions sequence: string - name: problem dtype: string - name: response dtype: string - name: reflection dtype: string - name: rewards sequence: bool - name: preds sequence: string splits: - name: train num_bytes: 1438976901 num_examples: 50000 download_size: 462282281 dataset_size: 1438976901 configs: - config_name: default data_files: - split: train path: data/train-* ---

feedbackagent/subset_with_rewards

feedbackagent

--- dataset_info: features: - name: gt dtype: string - name: idx dtype: int64 - name: prompt dtype: string - name: completions sequence: string - name: problem dtype: string - name: response dtype: string - name: reflection dtype: string - name: rewards sequence: bool - name: preds sequence: string splits: - name: train num_bytes: 1917204623 num_examples: 74768 download_size: 624059932 dataset_size: 1917204623 configs: - config_name: default data_files: - split: train path: data/train-* ---

feedbackagent/train_reflection_eval4_with_rewards

feedbackagent

--- dataset_info: features: - name: gt dtype: string - name: idx dtype: int64 - name: prompt dtype: string - name: completions sequence: string - name: problem dtype: string - name: response dtype: string - name: reflection dtype: string - name: rewards sequence: bool - name: preds sequence: string splits: - name: train num_bytes: 3281174028 num_examples: 124304 download_size: 1065772357 dataset_size: 3281174028 configs: - config_name: default data_files: - split: train path: data/train-* ---

mostafaashahin/common_voice_Arabic_12.0_Augmented_SWS_catt_new_split

mostafaashahin

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: score dtype: int64 - name: q_indx dtype: int64 - name: start_word_indx dtype: int64 - name: end_word_indx dtype: int64 - name: tashkeel_sentence dtype: string - name: ID dtype: string splits: - name: train num_bytes: 13955666205.535368 num_examples: 71391 - name: dev num_bytes: 75785042.00785968 num_examples: 2588 download_size: 12169157511 dataset_size: 14031451247.543228 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* ---

junsashihara/response-dataset-qwen-instruct

junsashihara

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 116929626 num_examples: 50782 download_size: 53160260 dataset_size: 116929626 configs: - config_name: default data_files: - split: train path: data/train-* ---

seand0101/manggarai-watergate-binary

seand0101

--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 6016508709.488 num_examples: 8752 download_size: 6065824739 dataset_size: 6016508709.488 configs: - config_name: default data_files: - split: train path: data/train-* ---

gabrielmbmb/logprobs

gabrielmbmb

--- size_categories: n<1K dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: source dtype: string - name: score dtype: float64 - name: messages list: - name: content dtype: string - name: role dtype: string - name: generation dtype: string - name: distilabel_metadata struct: - name: raw_input_text_generation_vllm list: - name: content dtype: string - name: role dtype: string - name: raw_output_text_generation_vllm dtype: string - name: statistics_text_generation_vllm struct: - name: input_tokens dtype: int64 - name: output_tokens dtype: int64 - name: llm_text_generation_vllm_logprobs list: list: - name: logprob dtype: float64 - name: token dtype: string - name: model_name dtype: string splits: - name: train num_bytes: 3158554 num_examples: 20 download_size: 1507282 dataset_size: 3158554 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 logprobs 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/gabrielmbmb/logprobs/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/logprobs/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "conversations": [ { "from": "human", "value": "Explain what boolean operators are, what they do, and provide examples of how they can be used in programming. Additionally, describe the concept of operator precedence and provide examples of how it affects the evaluation of boolean expressions. Discuss the difference between short-circuit evaluation and normal evaluation in boolean expressions and demonstrate their usage in code. \n\nFurthermore, add the requirement that the code must be written in a language that does not support short-circuit evaluation natively, forcing the test taker to implement their own logic for short-circuit evaluation.\n\nFinally, delve into the concept of truthiness and falsiness in programming languages, explaining how it affects the evaluation of boolean expressions. Add the constraint that the test taker must write code that handles cases where truthiness and falsiness are implemented differently across different programming languages." }, { "from": "gpt", "value": "Boolean operators are logical operators used in programming to manipulate boolean values. They operate on one or more boolean operands and return a boolean result. The three main boolean operators are \"AND\" (\u0026\u0026), \"OR\" (||), and \"NOT\" (!).\n\nThe \"AND\" operator returns true if both of its operands are true, and false otherwise. For example:\n\n```python\nx = 5\ny = 10\nresult = (x \u003e 0) and (y \u003c 20) # This expression evaluates to True\n```\n\nThe \"OR\" operator returns true if at least one of its operands is true, and false otherwise. For example:\n\n```python\nx = 5\ny = 10\nresult = (x \u003e 0) or (y \u003c 20) # This expression evaluates to True\n```\n\nThe \"NOT\" operator negates the boolean value of its operand. It returns true if the operand is false, and false if the operand is true. For example:\n\n```python\nx = 5\nresult = not (x \u003e 10) # This expression evaluates to True\n```\n\nOperator precedence refers to the order in which operators are evaluated in an expression. It ensures that expressions are evaluated correctly. In most programming languages, logical AND has higher precedence than logical OR. For example:\n\n```python\nresult = True or False and False # This expression is evaluated as (True or (False and False)), which is True\n```\n\nShort-circuit evaluation is a behavior where the second operand of a logical operator is not evaluated if the result can be determined based on the value of the first operand. In short-circuit evaluation, if the first operand of an \"AND\" operator is false, the second operand is not evaluated because the result will always be false. Similarly, if the first operand of an \"OR\" operator is true, the second operand is not evaluated because the result will always be true.\n\nIn programming languages that support short-circuit evaluation natively, you can use it to improve performance or avoid errors. For example:\n\n```python\nif x != 0 and (y / x) \u003e 10:\n # Perform some operation\n```\n\nIn languages without native short-circuit evaluation, you can implement your own logic to achieve the same behavior. Here\u0027s an example in pseudocode:\n\n```\nif x != 0 {\n if (y / x) \u003e 10 {\n // Perform some operation\n }\n}\n```\n\nTruthiness and falsiness refer to how non-boolean values are evaluated in boolean contexts. In many programming languages, non-zero numbers and non-empty strings are considered truthy, while zero, empty strings, and null/None values are considered falsy.\n\nWhen evaluating boolean expressions, truthiness and falsiness come into play. For example:\n\n```python\nx = 5\nresult = x # The value of x is truthy, so result is also truthy\n```\n\nTo handle cases where truthiness and falsiness are implemented differently across programming languages, you can explicitly check the desired condition. For example:\n\n```python\nx = 5\nresult = bool(x) # Explicitly converting x to a boolean value\n```\n\nThis ensures that the result is always a boolean value, regardless of the language\u0027s truthiness and falsiness rules." } ], "distilabel_metadata": { "raw_input_text_generation_vllm": [ { "content": "Explain what boolean operators are, what they do, and provide examples of how they can be used in programming. Additionally, describe the concept of operator precedence and provide examples of how it affects the evaluation of boolean expressions. Discuss the difference between short-circuit evaluation and normal evaluation in boolean expressions and demonstrate their usage in code. \n\nFurthermore, add the requirement that the code must be written in a language that does not support short-circuit evaluation natively, forcing the test taker to implement their own logic for short-circuit evaluation.\n\nFinally, delve into the concept of truthiness and falsiness in programming languages, explaining how it affects the evaluation of boolean expressions. Add the constraint that the test taker must write code that handles cases where truthiness and falsiness are implemented differently across different programming languages.", "role": "user" } ], "raw_output_text_generation_vllm": "Boolean operators are operators used to combine conditions in programming, returning a boolean value (true or false) based on the evaluation of those conditions. There are six primary boolean operators:\n\n1. `AND` (\u0026\u0026): Returns true if both conditions are true.\n2. `OR` (||): Returns true if either condition is true.\n3. `NOT` (!): Returns the opposite of the condition, i.e., true if the condition is false, and false if the condition is true.\n\nHere are some examples of how boolean operators work in programming:\n\n1. `(x == 5) \u0026\u0026 (y \u003e= 10)` returns true only if both `x` is 5 and `y` is greater than or equal to 10.\n2. `(x \u003e 10) || (y \u003c 0)` returns true if either `x` is greater than 10 or `y` is less than 0.\n3. `!(x == y)` returns true if `x` is not equal to `y`.\n\nOperator precedence in boolean expressions determines the order in which the conditions are evaluated. It is similar to operator precedence in arithmetic expressions. Here is the order of precedence:\n\n1. `NOT` (!)\n2. `||` (OR)\n3. `\u0026\u0026` (AND)\n\nFor example, `(x \u003e 10) || (y \u003c 0)` has higher precedence than `!(x == y)`, so the expression `(x \u003e 10) || (y \u003c 0)` is evaluated before `!(x == y)`.\n\nShort-circuit evaluation is a feature of some programming languages that allows the evaluation of a boolean expression to stop as soon as the truth value has been determined. This means that if the first condition in the boolean expression is false, the second condition will not be evaluated.\n\nFor example, in C#, the expression `(x \u003e 10) || (y \u003c 0)` is short-circuit evaluated, so `y` is not evaluated if `x` is greater than 10.\n\nNormal evaluation, on the other hand, evaluates both conditions even if one of them is false.\n\nTo implement short-circuit evaluation in a language that does not support it natively, you can use a technique called \"parenthesizing\" the boolean expression, which forces the language to evaluate the expression in a way that mimics short-circuit evaluation. This can be done by using parentheses to group the conditions in a way that causes the language to evaluate the expression in a short-circuit manner.\n\nHere is an example of how to implement short-circuit evaluation in Python:\n\n```python\ndef short_circuit(x, y):\n return (x \u003e 0) or (y \u003c 10)\n```\n\nIn this example, the function `short_circuit` takes two arguments `x` and `y`. The expression `(x \u003e 0) or (y \u003c 10)` is short-circuit evaluated, so `y` is not evaluated if `x` is greater than 0.\n\nTruthiness and falsiness in programming languages are concepts that determine whether a value is considered \"true\" or \"false\" in a boolean context. The truthiness of a value depends on its type and the language\u0027s boolean implementation.\n\nFor example, in Python, the following values are considered falsy:\n\n- Empty strings (`\"\"`),\n- Empty lists (`[]`),\n- Empty dictionaries (`{}`),\n- Empty sets (`set()`),\n- None,\n- Zero (integers, floats, etc.),\n- False.\n\nOn the other hand, the following values are considered truthy:\n\n- Non-empty strings (`\"hello\"`),\n- Non-empty lists (`[1, 2, 3]`),\n- Non-empty dictionaries (`{\"a\": 1, \"b\": 2}`),\n- Non-empty sets (`{1, 2, 3}`),\n- True,\n- All non-empty values.\n\nTo handle cases where truthiness and falsiness are implemented differently across different programming languages, you can write code that checks the type of the value and handles it accordingly. For example, in Python:\n\n```python\ndef handle_truthiness(value):\n if not isinstance(value, (str, list, dict, set)):\n return False\n if value:\n return True\n return False\n```\n\nThis function checks the type of the value and returns `True` if the value is a string, list, dictionary, or set, and `False` otherwise. It then checks the value for truthyness by returning `True` if the value is not empty", "statistics_text_generation_vllm": { "input_tokens": 193, "output_tokens": 1024 } }, "generation": "Boolean operators are operators used to combine conditions in programming, returning a boolean value (true or false) based on the evaluation of those conditions. There are six primary boolean operators:\n\n1. `AND` (\u0026\u0026): Returns true if both conditions are true.\n2. `OR` (||): Returns true if either condition is true.\n3. `NOT` (!): Returns the opposite of the condition, i.e., true if the condition is false, and false if the condition is true.\n\nHere are some examples of how boolean operators work in programming:\n\n1. `(x == 5) \u0026\u0026 (y \u003e= 10)` returns true only if both `x` is 5 and `y` is greater than or equal to 10.\n2. `(x \u003e 10) || (y \u003c 0)` returns true if either `x` is greater than 10 or `y` is less than 0.\n3. `!(x == y)` returns true if `x` is not equal to `y`.\n\nOperator precedence in boolean expressions determines the order in which the conditions are evaluated. It is similar to operator precedence in arithmetic expressions. Here is the order of precedence:\n\n1. `NOT` (!)\n2. `||` (OR)\n3. `\u0026\u0026` (AND)\n\nFor example, `(x \u003e 10) || (y \u003c 0)` has higher precedence than `!(x == y)`, so the expression `(x \u003e 10) || (y \u003c 0)` is evaluated before `!(x == y)`.\n\nShort-circuit evaluation is a feature of some programming languages that allows the evaluation of a boolean expression to stop as soon as the truth value has been determined. This means that if the first condition in the boolean expression is false, the second condition will not be evaluated.\n\nFor example, in C#, the expression `(x \u003e 10) || (y \u003c 0)` is short-circuit evaluated, so `y` is not evaluated if `x` is greater than 10.\n\nNormal evaluation, on the other hand, evaluates both conditions even if one of them is false.\n\nTo implement short-circuit evaluation in a language that does not support it natively, you can use a technique called \"parenthesizing\" the boolean expression, which forces the language to evaluate the expression in a way that mimics short-circuit evaluation. This can be done by using parentheses to group the conditions in a way that causes the language to evaluate the expression in a short-circuit manner.\n\nHere is an example of how to implement short-circuit evaluation in Python:\n\n```python\ndef short_circuit(x, y):\n return (x \u003e 0) or (y \u003c 10)\n```\n\nIn this example, the function `short_circuit` takes two arguments `x` and `y`. The expression `(x \u003e 0) or (y \u003c 10)` is short-circuit evaluated, so `y` is not evaluated if `x` is greater than 0.\n\nTruthiness and falsiness in programming languages are concepts that determine whether a value is considered \"true\" or \"false\" in a boolean context. The truthiness of a value depends on its type and the language\u0027s boolean implementation.\n\nFor example, in Python, the following values are considered falsy:\n\n- Empty strings (`\"\"`),\n- Empty lists (`[]`),\n- Empty dictionaries (`{}`),\n- Empty sets (`set()`),\n- None,\n- Zero (integers, floats, etc.),\n- False.\n\nOn the other hand, the following values are considered truthy:\n\n- Non-empty strings (`\"hello\"`),\n- Non-empty lists (`[1, 2, 3]`),\n- Non-empty dictionaries (`{\"a\": 1, \"b\": 2}`),\n- Non-empty sets (`{1, 2, 3}`),\n- True,\n- All non-empty values.\n\nTo handle cases where truthiness and falsiness are implemented differently across different programming languages, you can write code that checks the type of the value and handles it accordingly. For example, in Python:\n\n```python\ndef handle_truthiness(value):\n if not isinstance(value, (str, list, dict, set)):\n return False\n if value:\n return True\n return False\n```\n\nThis function checks the type of the value and returns `True` if the value is a string, list, dictionary, or set, and `False` otherwise. It then checks the value for truthyness by returning `True` if the value is not empty", "llm_text_generation_vllm_logprobs": [ [ { "logprob": -0.06034397706389427, "token": "Boolean" }, { "logprob": -4.082690238952637, "token": "Sure" }, { "logprob": -4.082690238952637, "token": "I" }, { "logprob": -4.887158393859863, "token": "B" }, { "logprob": -5.5128583908081055, "token": "Hello" }, { "logprob": -5.87039852142334, "token": "In" }, { "logprob": -6.317326545715332, "token": "A" }, { "logprob": -6.764254570007324, "token": "boolean" }, { "logprob": -6.764254570007324, "token": "1" }, { "logprob": -6.943024635314941, "token": "As" }, { "logprob": -7.121796607971191, "token": "**" }, { "logprob": -7.568722724914551, "token": "What" }, { "logprob": -7.568722724914551, "token": "Here" }, { "logprob": -7.747494697570801, "token": "Certainly" }, { "logprob": -7.926264762878418, "token": "Let" }, { "logprob": -7.926264762878418, "token": "#" }, { "logprob": -8.820120811462402, "token": "Absol" }, { "logprob": -8.909504890441895, "token": "Welcome" }, { "logprob": -9.088276863098145, "token": " Boolean" }, { "logprob": -9.17766284942627, "token": "\u003c" } ], [ { "logprob": -0.1830323338508606, "token": " operators" }, { "logprob": -1.7919704914093018, "token": " Oper" }, { "logprob": -8.406495094299316, "token": " expressions" }, { "logprob": -8.585265159606934, "token": " operations" }, { "logprob": -9.479121208190918, "token": " values" }, { "logprob": -10.283589363098145, "token": " logic" }, { "logprob": -10.641131401062012, "token": " Operations" }, { "logprob": -10.819903373718262, "token": " operator" }, { "logprob": -11.445601463317871, "token": " Logic" }, { "logprob": -11.713757514953613, "token": " algebra" }, { "logprob": -11.803143501281738, "token": " oper" }, { "logprob": -11.89252758026123, "token": "Oper" }, { "logprob": -11.981913566589355, "token": " Algebra" }, { "logprob": -12.07129955291748, "token": " O" }, { "logprob": -12.339455604553223, "token": " variables" }, { "logprob": -12.87576961517334, "token": "oper" }, { "logprob": -13.50146770477295, "token": " Values" }, { "logprob": -13.769623756408691, "token": " Expressions" }, { "logprob": -13.859009742736816, "token": " and" }, { "logprob": -13.948393821716309, "token": " Variables" } ], [ { "logprob": -0.05496400222182274, "token": " are" }, { "logprob": -3.2728404998779297, "token": "," }, { "logprob": -4.881780624389648, "token": ":" }, { "logprob": -5.596864700317383, "token": " in" }, { "logprob": -5.95440673828125, "token": "\n" }, { "logprob": -6.848260879516602, "token": " and" }, { "logprob": -8.814741134643555, "token": " allow" }, { "logprob": -9.172283172607422, "token": " perform" }, { "logprob": -9.172283172607422, "token": " (" }, { "logprob": -10.066137313842773, "token": "!" }, { "logprob": -10.244909286499023, "token": " can" }, { "logprob": -10.60245132446289, "token": " play" }, { "logprob": -10.60245132446289, "token": " -" }, { "logprob": -11.138763427734375, "token": " or" }, { "logprob": -11.138763427734375, "token": " refer" }, { "logprob": -11.138763427734375, "token": " form" }, { "logprob": -11.317535400390625, "token": "\n\n" }, { "logprob": -11.496305465698242, "token": " act" }, { "logprob": -11.496305465698242, "token": " take" }, { "logprob": -11.67507553100586, "token": " have" } ], [ { "logprob": -1.8189913034439087, "token": " operators" }, { "logprob": -1.1039072275161743, "token": " a" }, { "logprob": -1.1039072275161743, "token": " logical" }, { "logprob": -3.070387363433838, "token": " special" }, { "logprob": -3.070387363433838, "token": " used" }, { "logprob": -3.249159336090088, "token": " symbols" }, { "logprob": -4.3217854499816895, "token": " fundamental" }, { "logprob": -5.394411563873291, "token": " logic" }, { "logprob": -5.573181629180908, "token": " simple" }, { "logprob": -5.751951694488525, "token": " operations" }, { "logprob": -5.751951694488525, "token": " the" }, { "logprob": -5.930723667144775, "token": " binary" }, { "logprob": -6.109493732452393, "token": " expressions" }, { "logprob": -6.46703577041626, "token": " programming" }, { "logprob": -6.64580774307251, "token": " functions" }, { "logprob": -7.003349781036377, "token": " basic" }, { "logprob": -7.182119846343994, "token": " shorthand" }, { "logprob": -7.360891819000244, "token": " an" }, { "logprob": -7.539661884307861, "token": " mathematical" }, { "logprob": -8.075974464416504, "token": " control" } ], [ { "logprob": -1.5109121799468994, "token": " used" }, { "logprob": -0.25951603055000305, "token": " that" }, { "logprob": -4.9075608253479, "token": " in" }, { "logprob": -8.125436782836914, "token": " which" }, { "logprob": -9.376832962036133, "token": " designed" }, { "logprob": -10.628231048583984, "token": " specifically" }, { "logprob": -11.164543151855469, "token": " or" }, { "logprob": -11.343315124511719, "token": " for" }, { "logprob": -11.522085189819336, "token": "," }, { "logprob": -12.05839729309082, "token": " whose" }, { "logprob": -12.23716926574707, "token": " commonly" }, { "logprob": -12.23716926574707, "token": " utilized" }, { "logprob": -12.23716926574707, "token": " with" }, { "logprob": -12.952253341674805, "token": " within" }, { "logprob": -13.309795379638672, "token": " such" }, { "logprob": -13.488565444946289, "token": " like" }, { "logprob": -13.846107482910156, "token": " applied" }, { "logprob": -13.846107482910156, "token": " (" }, { "logprob": -14.024879455566406, "token": " to" }, { "logprob": -14.024879455566406, "token": " of" } ], [ { "logprob": -0.1652197688817978, "token": " to" }, { "logprob": -1.952928066253662, "token": " in" }, { "logprob": -4.63449239730835, "token": " for" }, { "logprob": -7.6735968589782715, "token": " with" }, { "logprob": -8.388680458068848, "token": " within" }, { "logprob": -11.78532886505127, "token": " on" }, { "logprob": -11.96410083770752, "token": " primarily" }, { "logprob": -12.142870903015137, "token": " by" }, { "logprob": -12.500412940979004, "token": " as" }, { "logprob": -13.394267082214355, "token": " extensively" }, { "logprob": -13.394267082214355, "token": " when" }, { "logprob": -13.394267082214355, "token": " specifically" }, { "logprob": -13.930581092834473, "token": " together" }, { "logprob": -13.930581092834473, "token": " exclusively" }, { "logprob": -15.003207206726074, "token": " inside" }, { "logprob": -15.181977272033691, "token": " solely" }, { "logprob": -15.181977272033691, "token": " along" }, { "logprob": -15.360749244689941, "token": " mainly" }, { "logprob": -15.539519309997559, "token": " during" }, { "logprob": -16.612146377563477, "token": " commonly" } ], [ { "logprob": -2.2875277996063232, "token": " combine" }, { "logprob": -1.0361316204071045, "token": " manipulate" }, { "logprob": -1.0361316204071045, "token": " perform" }, { "logprob": -2.4662997722625732, "token": " evaluate" }, { "logprob": -3.717695951461792, "token": " compare" }, { "logprob": -3.717695951461792, "token": " form" }, { "logprob": -4.611550331115723, "token": " test" }, { "logprob": -4.611550331115723, "token": " create" }, { "logprob": -4.96909236907959, "token": " control" }, { "logprob": -5.14786434173584, "token": " make" }, { "logprob": -5.684176445007324, "token": " produce" }, { "logprob": -5.862946510314941, "token": " determine" }, { "logprob": -6.041718482971191, "token": " work" }, { "logprob": -6.220488548278809, "token": " connect" }, { "logprob": -6.220488548278809, "token": " execute" }, { "logprob": -6.578030586242676, "token": " return" }, { "logprob": -6.935572624206543, "token": " construct" }, { "logprob": -7.114344596862793, "token": " check" }, { "logprob": -7.114344596862793, "token": " operate" }, { "logprob": -7.47188663482666, "token": " express" } ], [ { "logprob": -0.7143782377243042, "token": " conditions" }, { "logprob": -1.965774416923523, "token": " values" }, { "logprob": -2.1445465087890625, "token": " boolean" }, { "logprob": -2.5020885467529297, "token": " or" }, { "logprob": -3.038400650024414, "token": " one" }, { "logprob": -3.3959426879882812, "token": " two" }, { "logprob": -3.9322547912597656, "token": " conditional" }, { "logprob": -3.9322547912597656, "token": " and" }, { "logprob": -3.9322547912597656, "token": " expressions" }, { "logprob": -4.82611083984375, "token": " multiple" }, { "logprob": -5.004880905151367, "token": " statements" }, { "logprob": -5.541194915771484, "token": " Boolean" }, { "logprob": -5.541194915771484, "token": " simple" }, { "logprob": -5.719964981079102, "token": " logical" }, { "logprob": -7.3289031982421875, "token": " relational" }, { "logprob": -7.3289031982421875, "token": " true" }, { "logprob": -7.686445236206055, "token": " logic" }, { "logprob": -7.686445236206055, "token": " condition" }, { "logprob": -7.686445236206055, "token": " the" }, { "logprob": -7.865217208862305, "token": " truth" } ], [ { "logprob": -0.11622606217861176, "token": " in" }, { "logprob": -2.619018316268921, "token": " or" }, { "logprob": -4.406726837158203, "token": " and" }, { "logprob": -4.585498809814453, "token": " to" }, { "logprob": -5.6581268310546875, "token": "." }, { "logprob": -5.836894989013672, "token": " using" }, { "logprob": -5.836894989013672, "token": "," }, { "logprob": -6.730751037597656, "token": " into" }, { "logprob": -6.909521102905273, "token": " within" }, { "logprob": -7.267063140869141, "token": " with" }, { "logprob": -7.267063140869141, "token": " that" }, { "logprob": -7.445835113525391, "token": " based" }, { "logprob": -7.803377151489258, "token": " for" }, { "logprob": -8.51845932006836, "token": " of" }, { "logprob": -9.591085433959961, "token": " from" }, { "logprob": -9.591085433959961, "token": " together" }, { "logprob": -9.769857406616211, "token": " (" }, { "logprob": -11.200023651123047, "token": " when" }, { "logprob": -11.200023651123047, "token": " inside" }, { "logprob": -11.378795623779297, "token": " by" } ], [ { "logprob": -0.46720749139785767, "token": " programming" }, { "logprob": -1.1822915077209473, "token": " a" }, { "logprob": -3.1487717628479004, "token": " conditional" }, { "logprob": -4.221397876739502, "token": " an" }, { "logprob": -6.545420169830322, "token": " programs" }, { "logprob": -6.7241902351379395, "token": " logic" }, { "logprob": -6.7241902351379395, "token": " order" }, { "logprob": -6.9029622077941895, "token": " logical" }, { "logprob": -7.081732273101807, "token": " code" }, { "logprob": -7.081732273101807, "token": " boolean" }, { "logprob": -7.796816349029541, "token": " if" }, { "logprob": -7.975588321685791, "token": " expressions" }, { "logprob": -8.154358863830566, "token": " the" }, { "logprob": -8.511900901794434, "token": " program" }, { "logprob": -8.511900901794434, "token": " Boolean" }, { "logprob": -8.69067096710205, "token": " computer" }, { "logprob": -8.8694429397583, "token": " your" }, { "logprob": -9.048213005065918, "token": " control" }, { "logprob": -9.048213005065918, "token": " Python" }, { "logprob": -9.226984977722168, "token": " conditions" } ], "... (truncated - showing 10 of 1024 elements)" ], "messages": [ { "content": "Explain what boolean operators are, what they do, and provide examples of how they can be used in programming. Additionally, describe the concept of operator precedence and provide examples of how it affects the evaluation of boolean expressions. Discuss the difference between short-circuit evaluation and normal evaluation in boolean expressions and demonstrate their usage in code. \n\nFurthermore, add the requirement that the code must be written in a language that does not support short-circuit evaluation natively, forcing the test taker to implement their own logic for short-circuit evaluation.\n\nFinally, delve into the concept of truthiness and falsiness in programming languages, explaining how it affects the evaluation of boolean expressions. Add the constraint that the test taker must write code that handles cases where truthiness and falsiness are implemented differently across different programming languages.", "role": "user" } ], "model_name": "HuggingFaceTB/SmolLM2-1.7B-Instruct", "score": 5.212620735168457, "source": "infini-instruct-top-500k" } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("gabrielmbmb/logprobs", "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("gabrielmbmb/logprobs") ``` </details>

maniro-ai/20241210_validation_cover-workspace_rotmat

maniro-ai

--- task_categories: - robotics tags: - LeRobot --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

haydn-jones/PubChem

haydn-jones

--- dataset_info: features: - name: CID dtype: int64 - name: SMILES dtype: large_string - name: SELFIES dtype: string splits: - name: train num_bytes: 36600584436.0 num_examples: 119009905 download_size: 9179713025 dataset_size: 36600584436.0 configs: - config_name: default data_files: - split: train path: data/train-* tags: - chemistry - biology - medical size_categories: - 100M<n<1B ---

ziyou-li/cantonese_processed_daily

ziyou-li

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 3899650216 num_examples: 4060 download_size: 623179139 dataset_size: 3899650216 --- # Dataset Card for "cantonese_processed_daily" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

athugodage/legal_simplification_corpus

athugodage

--- dataset_info: features: - name: Текст dtype: string - name: Комментарий РГ dtype: string splits: - name: train num_bytes: 118357564.79574679 num_examples: 1819 - name: test num_bytes: 13208678.204253215 num_examples: 203 download_size: 42722724 dataset_size: 131566243.0 --- # Dataset Card for "legal_simplification_corpus" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)