Datasets:
davanstrien
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summary-test

Dataset card Data Studio Files
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aengusl/ihy_helpful_only-v1.0
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 122559566.731727 num_examples: 231214 download_size: 67836975 dataset_size: 122559566.731727 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-eval-project-quoref-9c01ff03-1305849901
--- type: predictions tags: - autotrain - evaluation datasets: - quoref eval_info: task: extractive_question_answering model: nbroad/rob-base-superqa1 metrics: [] dataset_name: quoref dataset_config: default dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: nbroad/rob-base-superqa1 * Dataset: quoref * Config: default * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model.
Xuttt123/mannequin_hand_YOLOv8
--- license: gpl-3.0 ---
dataautogpt3/Dalle3
--- license: mit --- 10,000 high-quality captions with image pairs produced by dalle3 with a raw.zip incase i uploaded it wrong.
open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ
--- pretty_name: Evaluation run of TheBloke/WizardLM-30B-GPTQ dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/WizardLM-30B-GPTQ](https://huggingface.co/TheBloke/WizardLM-30B-GPTQ)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-07T18:05:07.591558](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ_public/blob/main/results_2023-11-07T18-05-07.591558.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.21245805369127516,\n\ \ \"em_stderr\": 0.004189026405353694,\n \"f1\": 0.2829110738255039,\n\ \ \"f1_stderr\": 0.004179836263087045,\n \"acc\": 0.5537101784195891,\n\ \ \"acc_stderr\": 0.012517196395950588\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.21245805369127516,\n \"em_stderr\": 0.004189026405353694,\n\ \ \"f1\": 0.2829110738255039,\n \"f1_stderr\": 0.004179836263087045\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.34420015163002277,\n \ \ \"acc_stderr\": 0.013086800426693784\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7632202052091555,\n \"acc_stderr\": 0.011947592365207392\n\ \ }\n}\n```" repo_url: https://huggingface.co/TheBloke/WizardLM-30B-GPTQ leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_11_07T18_05_07.591558 path: - '**/details_harness|drop|3_2023-11-07T18-05-07.591558.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-07T18-05-07.591558.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_07T18_05_07.591558 path: - '**/details_harness|gsm8k|5_2023-11-07T18-05-07.591558.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-07T18-05-07.591558.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_07T18_05_07.591558 path: - '**/details_harness|winogrande|5_2023-11-07T18-05-07.591558.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-07T18-05-07.591558.parquet' - config_name: results data_files: - split: 2023_11_07T18_05_07.591558 path: - results_2023-11-07T18-05-07.591558.parquet - split: latest path: - results_2023-11-07T18-05-07.591558.parquet --- # Dataset Card for Evaluation run of TheBloke/WizardLM-30B-GPTQ ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/WizardLM-30B-GPTQ - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TheBloke/WizardLM-30B-GPTQ](https://huggingface.co/TheBloke/WizardLM-30B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-07T18:05:07.591558](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ_public/blob/main/results_2023-11-07T18-05-07.591558.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.21245805369127516, "em_stderr": 0.004189026405353694, "f1": 0.2829110738255039, "f1_stderr": 0.004179836263087045, "acc": 0.5537101784195891, "acc_stderr": 0.012517196395950588 }, "harness|drop|3": { "em": 0.21245805369127516, "em_stderr": 0.004189026405353694, "f1": 0.2829110738255039, "f1_stderr": 0.004179836263087045 }, "harness|gsm8k|5": { "acc": 0.34420015163002277, "acc_stderr": 0.013086800426693784 }, "harness|winogrande|5": { "acc": 0.7632202052091555, "acc_stderr": 0.011947592365207392 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
Rafaelcv1/robotnik
--- license: openrail ---
SEACrowd/stif_indonesia
--- license: mit tags: - paraphrasing language: - ind --- # stif_indonesia STIF-Indonesia is formal-informal (bahasa baku - bahasa alay/slang) style transfer for Indonesian. Texts were collected from Twitter. Then, native speakers were aksed to transform the text into formal style. ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` @inproceedings{wibowo2020semi, title={Semi-supervised low-resource style transfer of indonesian informal to formal language with iterative forward-translation}, author={Wibowo, Haryo Akbarianto and Prawiro, Tatag Aziz and Ihsan, Muhammad and Aji, Alham Fikri and Prasojo, Radityo Eko and Mahendra, Rahmad and Fitriany, Suci}, booktitle={2020 International Conference on Asian Language Processing (IALP)}, pages={310--315}, year={2020}, organization={IEEE} } ``` ## License MIT ## Homepage [https://github.com/haryoa/stif-indonesia](https://github.com/haryoa/stif-indonesia) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
ruacc/test
--- license: llama2 ---
TrevorJS/mtg-cards-dataset
--- dataset_info: features: - name: artist dtype: string - name: artistIds dtype: string - name: asciiName dtype: string - name: attractionLights dtype: string - name: availability dtype: string - name: boosterTypes dtype: string - name: borderColor dtype: string - name: cardParts dtype: string - name: colorIdentity dtype: string - name: colorIndicator dtype: string - name: colors dtype: string - name: defense dtype: string - name: duelDeck dtype: string - name: edhrecRank dtype: float64 - name: edhrecSaltiness dtype: float64 - name: faceConvertedManaCost dtype: float64 - name: faceFlavorName dtype: string - name: faceManaValue dtype: float64 - name: faceName dtype: string - name: finishes dtype: string - name: flavorName dtype: string - name: flavorText dtype: string - name: frameEffects dtype: string - name: frameVersion dtype: string - name: hand dtype: string - name: hasAlternativeDeckLimit dtype: float64 - name: hasContentWarning dtype: float64 - name: hasFoil dtype: int64 - name: hasNonFoil dtype: int64 - name: isAlternative dtype: float64 - name: isFullArt dtype: float64 - name: isFunny dtype: float64 - name: isOnlineOnly dtype: float64 - name: isOversized dtype: float64 - name: isPromo dtype: float64 - name: isRebalanced dtype: float64 - name: isReprint dtype: float64 - name: isReserved dtype: float64 - name: isStarter dtype: float64 - name: isStorySpotlight dtype: float64 - name: isTextless dtype: float64 - name: isTimeshifted dtype: float64 - name: keywords dtype: string - name: language dtype: string - name: layout dtype: string - name: leadershipSkills dtype: string - name: life dtype: string - name: loyalty dtype: string - name: manaCost dtype: string - name: manaValue dtype: float64 - name: name dtype: string - name: number dtype: string - name: originalPrintings dtype: string - name: originalReleaseDate dtype: string - name: originalText dtype: string - name: originalType dtype: string - name: otherFaceIds dtype: string - name: power dtype: string - name: printings dtype: string - name: promoTypes dtype: string - name: rarity dtype: string - name: rebalancedPrintings dtype: string - name: relatedCards dtype: string - name: securityStamp dtype: string - name: setCode dtype: string - name: side dtype: string - name: signature dtype: string - name: sourceProducts dtype: string - name: subsets dtype: string - name: subtypes dtype: string - name: supertypes dtype: string - name: text dtype: string - name: toughness dtype: string - name: type dtype: string - name: types dtype: string - name: uuid dtype: string - name: variations dtype: string - name: watermark dtype: string splits: - name: train num_bytes: 101580429 num_examples: 90857 download_size: 23410064 dataset_size: 101580429 configs: - config_name: default data_files: - split: train path: data/train-* ---
formospeech/hakkaradio_news
--- dataset_info: config_name: sixian_jun features: - name: id dtype: string - name: audio dtype: audio - name: duration dtype: float64 - name: text dtype: string - name: ipa dtype: string - name: char_per_sec dtype: float64 - name: speaker dtype: string splits: - name: train num_bytes: 182150488.808 num_examples: 1828 download_size: 182897322 dataset_size: 182150488.808 configs: - config_name: sixian_jun data_files: - split: train path: sixian_jun/train-* ---
ashokpoudel/English-Nepali-Translation-Instruction-Dataset
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1712164438 num_examples: 3560496 download_size: 775881227 dataset_size: 1712164438 configs: - config_name: default data_files: - split: train path: data/train-* --- ## Dataset Card: Instruction-Based English-Nepali Translation Dataset ### Dataset Description This dataset consists of English-Nepali parallel sentences converted into an instruction-based format. Each entry prompts the model to translate a given sentence from English to Nepali or vice versa. ### Source Data **Original Dataset**: English-Nepali Parallel Sentences **Paper**: [NepBERTa: Nepali Language Model Trained in a Large Corpus](https://aura.abdn.ac.uk/bitstream/handle/2164/21465/Timilsina_etal_ACLA_NepNERTa_VOR.pdf) **Authors**: Milan Gautam, Sulav Timilsina, Binod Bhattarai **Conference**: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) ### Dataset Format Each entry in the dataset has the following format: ``` [INST] Please translate "sentence in source language" into target language [/INST] translation in target language ``` The dataset supports both English to Nepali and Nepali to English translations. ### Intended Use This dataset is designed for fine-tuning models on instruction-based translation tasks, especially suited for models like Llama Instruct. It can be used to develop models capable of translating between English and Nepali using instruction-based prompts. ### Data Collection The data was derived from the English-Nepali parallel corpus presented in the NepBERTa paper. The sentences were then converted into an instruction-based format to facilitate training with instruction-based models. ### Limitations - The dataset's performance and utility are tied to the quality of the original English-Nepali corpus. - The instruction-based format may introduce some redundancy and might not be ideal for all NLP tasks or models. ### Licensing Ensure you have the right to share the data and understand any licensing implications. Mention the dataset's licensing terms here. ---
Oguzz07/Confession-Subreddit-Top500
--- license: mit --- This dataset was prepared by taking into account the 500 most popular posts of all time in the confession subreddit and the comments with the most votes on these posts.
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random
--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_1_bs_16 num_bytes: 86816009.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90735172.125 num_examples: 5647 download_size: 162358362 dataset_size: 177551181.25 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_19_10000000
--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 191895 num_examples: 6699 download_size: 121556 dataset_size: 191895 --- # Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_19_10000000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
midiatorium/Renato_Russo
--- license: openrail ---
TrainingDataPro/black-people-liveness-detection-video-dataset
--- license: cc-by-nc-nd-4.0 task_categories: - image-classification - image-to-image - feature-extraction language: - en tags: - code - legal - finance --- # Biometric Attack Dataset, Black People # The similar dataset that includes all ethnicities - [Anti Spoofing Real Dataset](https://trainingdata.pro/data-market/anti-spoofing-real/?utm_source=huggingface&utm_medium=cpc&utm_campaign=black-people-liveness-detection) The dataset for face anti spoofing and face recognition includes images and videos of black people. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic group. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. The dataset contains images and videos of real humans with various **resolutions, views, and colors**, making it a comprehensive resource for researchers working on anti-spoofing technologies. ### People in the dataset ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F224e4e37b00a04546bbeaeded5fd3213%2FFrame%2095.png?generation=1712226592271540&alt=media) ### Types of files in the dataset: - **photo** - selfie of the person - **video** - real video of the person Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models. # 💴 For Commercial Usage: Full version of the dataset includes 15,000 files, leave a request on **[TrainingData](https://trainingdata.pro/data-market/black-african-people-video-dataset?utm_source=huggingface&utm_medium=cpc&utm_campaign=black-people-liveness-detection)** to buy the dataset ### Metadata for the full dataset: - **assignment_id** - unique identifier of the media file - **worker_id** - unique identifier of the person - **age** - age of the person - **true_gender** - gender of the person - **country** - country of the person - **ethnicity** - ethnicity of the person - **video_extension** - video extensions in the dataset - **video_resolution** - video resolution in the dataset - **video_duration** - video duration in the dataset - **video_fps** - frames per second for video in the dataset - **photo_extension** - photo extensions in the dataset - **photo_resolution** - photo resolution in the dataset ### Statistics for the dataset ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F9682c567213f0e6e99fecc3c6b511a9d%2FFrame%2096.png?generation=1712832044284031&alt=media) # 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/black-african-people-video-dataset?utm_source=huggingface&utm_medium=cpc&utm_campaign=black-people-liveness-detection) to learn about the price and buy the dataset** # Content The dataset consists of: - **files** - includes 10 folders corresponding to each person and including 1 image and 1 video, - **.csv file** - contains information about the files and people in the dataset ### File with the extension .csv - **id**: id of the person, - **selfie_link**: link to access the photo, - **video_link**: link to access the video, - **age**: age of the person, - **country**: country of the person, - **gender**: gender of the person, - **video_extension**: video extension, - **video_resolution**: video resolution, - **video_duration**: video duration, - **video_fps**: frames per second for video, - **photo_extension**: photo extension, - **photo_resolution**: photo resolution ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F77905aea23afb7f61167bc9ccd0d98cb%2F7-ezgif.com-optimize.gif?generation=1707303271936246&alt=media) ## **[TrainingData](https://trainingdata.pro/data-market/black-african-people-video-dataset?utm_source=huggingface&utm_medium=cpc&utm_campaign=black-people-liveness-detection)** provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*
paascorb/RomancesTradicionales
--- dataset_info: features: - name: question dtype: string - name: ground_truth dtype: string - name: contexts sequence: string - name: answer dtype: string splits: - name: train num_bytes: 6931 num_examples: 5 download_size: 15535 dataset_size: 6931 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - question-answering language: - es size_categories: - 10K<n<100K --- # Dataset Card for "RomancesTradicionales" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gwlms/dewiki-20230701-tfrecords-dupe5
--- license: cc-by-sa-3.0 language: - de ---
Iceclear/CelebA-HQ1024
--- license: apache-2.0 ---
MatsuoDochiai/IAE
--- license: openrail ---
KentoTsu/Tristan
--- license: openrail ---
FanChen0116/bus_few35_front
--- dataset_info: features: - name: id dtype: int64 - name: tokens sequence: string - name: labels sequence: class_label: names: '0': O '1': I-from_location '2': B-from_location '3': B-leaving_date '4': I-leaving_date '5': I-to_location '6': B-to_location - name: request_slot sequence: string splits: - name: train num_bytes: 6172 num_examples: 35 - name: validation num_bytes: 6900 num_examples: 35 - name: test num_bytes: 6900 num_examples: 35 download_size: 14148 dataset_size: 19972 --- # Dataset Card for "bus_few35_front" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ummagumm-a/colorization_dataset
--- dataset_info: features: - name: image dtype: image - name: conditioning_image sequence: sequence: sequence: uint8 - name: text dtype: string splits: - name: train num_bytes: 333261193.0 num_examples: 1000 download_size: 127051514 dataset_size: 333261193.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "colorization_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jingwora/unstructured-data-multilingual
--- dataset_info: features: - name: language dtype: string - name: id dtype: string - name: product_id dtype: string - name: category dtype: string - name: sub_category dtype: string - name: product_name dtype: string - name: product_detail dtype: string - name: image_files dtype: string - name: review dtype: string - name: star dtype: string - name: sentiment dtype: string splits: - name: en num_bytes: 11790 num_examples: 24 - name: ja num_bytes: 10499 num_examples: 24 - name: th num_bytes: 12716 num_examples: 24 download_size: 34282 dataset_size: 35005 configs: - config_name: default data_files: - split: en path: data/en-* - split: ja path: data/ja-* - split: th path: data/th-* --- # Dataset Card for "unstructured-data-multilingual" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
food101
--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|other-foodspotting task_categories: - image-classification task_ids: - multi-class-image-classification paperswithcode_id: food-101 pretty_name: Food-101 dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': apple_pie '1': baby_back_ribs '2': baklava '3': beef_carpaccio '4': beef_tartare '5': beet_salad '6': beignets '7': bibimbap '8': bread_pudding '9': breakfast_burrito '10': bruschetta '11': caesar_salad '12': cannoli '13': caprese_salad '14': carrot_cake '15': ceviche '16': cheesecake '17': cheese_plate '18': chicken_curry '19': chicken_quesadilla '20': chicken_wings '21': chocolate_cake '22': chocolate_mousse '23': churros '24': clam_chowder '25': club_sandwich '26': crab_cakes '27': creme_brulee '28': croque_madame '29': cup_cakes '30': deviled_eggs '31': donuts '32': dumplings '33': edamame '34': eggs_benedict '35': escargots '36': falafel '37': filet_mignon '38': fish_and_chips '39': foie_gras '40': french_fries '41': french_onion_soup '42': french_toast '43': fried_calamari '44': fried_rice '45': frozen_yogurt '46': garlic_bread '47': gnocchi '48': greek_salad '49': grilled_cheese_sandwich '50': grilled_salmon '51': guacamole '52': gyoza '53': hamburger '54': hot_and_sour_soup '55': hot_dog '56': huevos_rancheros '57': hummus '58': ice_cream '59': lasagna '60': lobster_bisque '61': lobster_roll_sandwich '62': macaroni_and_cheese '63': macarons '64': miso_soup '65': mussels '66': nachos '67': omelette '68': onion_rings '69': oysters '70': pad_thai '71': paella '72': pancakes '73': panna_cotta '74': peking_duck '75': pho '76': pizza '77': pork_chop '78': poutine '79': prime_rib '80': pulled_pork_sandwich '81': ramen '82': ravioli '83': red_velvet_cake '84': risotto '85': samosa '86': sashimi '87': scallops '88': seaweed_salad '89': shrimp_and_grits '90': spaghetti_bolognese '91': spaghetti_carbonara '92': spring_rolls '93': steak '94': strawberry_shortcake '95': sushi '96': tacos '97': takoyaki '98': tiramisu '99': tuna_tartare '100': waffles splits: - name: train num_bytes: 3842657187.0 num_examples: 75750 - name: validation num_bytes: 1275182340.5 num_examples: 25250 download_size: 5059972308 dataset_size: 5117839527.5 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* --- # Dataset Card for Food-101 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Food-101 Dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) - **Repository:** - **Paper:** [Paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset consists of 101 food categories, with 101'000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels. ### Supported Tasks and Leaderboards - `image-classification`: The goal of this task is to classify a given image of a dish into one of 101 classes. The leaderboard is available [here](https://paperswithcode.com/sota/fine-grained-image-classification-on-food-101). ### Languages English ## Dataset Structure ### Data Instances A sample from the training set is provided below: ``` { 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x276021C5EB8>, 'label': 23 } ``` ### Data Fields The data instances have the following fields: - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`. - `label`: an `int` classification label. <details> <summary>Class Label Mappings</summary> ```json { "apple_pie": 0, "baby_back_ribs": 1, "baklava": 2, "beef_carpaccio": 3, "beef_tartare": 4, "beet_salad": 5, "beignets": 6, "bibimbap": 7, "bread_pudding": 8, "breakfast_burrito": 9, "bruschetta": 10, "caesar_salad": 11, "cannoli": 12, "caprese_salad": 13, "carrot_cake": 14, "ceviche": 15, "cheesecake": 16, "cheese_plate": 17, "chicken_curry": 18, "chicken_quesadilla": 19, "chicken_wings": 20, "chocolate_cake": 21, "chocolate_mousse": 22, "churros": 23, "clam_chowder": 24, "club_sandwich": 25, "crab_cakes": 26, "creme_brulee": 27, "croque_madame": 28, "cup_cakes": 29, "deviled_eggs": 30, "donuts": 31, "dumplings": 32, "edamame": 33, "eggs_benedict": 34, "escargots": 35, "falafel": 36, "filet_mignon": 37, "fish_and_chips": 38, "foie_gras": 39, "french_fries": 40, "french_onion_soup": 41, "french_toast": 42, "fried_calamari": 43, "fried_rice": 44, "frozen_yogurt": 45, "garlic_bread": 46, "gnocchi": 47, "greek_salad": 48, "grilled_cheese_sandwich": 49, "grilled_salmon": 50, "guacamole": 51, "gyoza": 52, "hamburger": 53, "hot_and_sour_soup": 54, "hot_dog": 55, "huevos_rancheros": 56, "hummus": 57, "ice_cream": 58, "lasagna": 59, "lobster_bisque": 60, "lobster_roll_sandwich": 61, "macaroni_and_cheese": 62, "macarons": 63, "miso_soup": 64, "mussels": 65, "nachos": 66, "omelette": 67, "onion_rings": 68, "oysters": 69, "pad_thai": 70, "paella": 71, "pancakes": 72, "panna_cotta": 73, "peking_duck": 74, "pho": 75, "pizza": 76, "pork_chop": 77, "poutine": 78, "prime_rib": 79, "pulled_pork_sandwich": 80, "ramen": 81, "ravioli": 82, "red_velvet_cake": 83, "risotto": 84, "samosa": 85, "sashimi": 86, "scallops": 87, "seaweed_salad": 88, "shrimp_and_grits": 89, "spaghetti_bolognese": 90, "spaghetti_carbonara": 91, "spring_rolls": 92, "steak": 93, "strawberry_shortcake": 94, "sushi": 95, "tacos": 96, "takoyaki": 97, "tiramisu": 98, "tuna_tartare": 99, "waffles": 100 } ``` </details> ### Data Splits | |train|validation| |----------|----:|---------:| |# of examples|75750|25250| ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information LICENSE AGREEMENT ================= - The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2]. [1] http://www.foodspotting.com/ [2] http://www.foodspotting.com/terms/ ### Citation Information ``` @inproceedings{bossard14, title = {Food-101 -- Mining Discriminative Components with Random Forests}, author = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc}, booktitle = {European Conference on Computer Vision}, year = {2014} } ``` ### Contributions Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset.
open-llm-leaderboard/details_Cartinoe5930__MoE-Merging
--- pretty_name: Evaluation run of Cartinoe5930/MoE-Merging dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Cartinoe5930/MoE-Merging](https://huggingface.co/Cartinoe5930/MoE-Merging) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Cartinoe5930__MoE-Merging\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-23T16:02:03.400569](https://huggingface.co/datasets/open-llm-leaderboard/details_Cartinoe5930__MoE-Merging/blob/main/results_2024-01-23T16-02-03.400569.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6151505712161501,\n\ \ \"acc_stderr\": 0.03298337172536938,\n \"acc_norm\": 0.6177051984375612,\n\ \ \"acc_norm_stderr\": 0.03364497168310484,\n \"mc1\": 0.40758873929008566,\n\ \ \"mc1_stderr\": 0.01720194923455311,\n \"mc2\": 0.5783132294676995,\n\ \ \"mc2_stderr\": 0.015725405307929003\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6117747440273038,\n \"acc_stderr\": 0.014241614207414047,\n\ \ \"acc_norm\": 0.6544368600682594,\n \"acc_norm_stderr\": 0.013896938461145677\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6493726349332802,\n\ \ \"acc_stderr\": 0.004761912511707511,\n \"acc_norm\": 0.8458474407488548,\n\ \ \"acc_norm_stderr\": 0.0036035695286784127\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.03773809990686934,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.03773809990686934\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5234042553191489,\n \"acc_stderr\": 0.032650194750335815,\n\ \ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.032650194750335815\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n\ \ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n\ \ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41798941798941797,\n \"acc_stderr\": 0.02540255550326091,\n \"\ acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.02540255550326091\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\ \ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\ \ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6870967741935484,\n\ \ \"acc_stderr\": 0.026377567028645858,\n \"acc_norm\": 0.6870967741935484,\n\ \ \"acc_norm_stderr\": 0.026377567028645858\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.03510766597959217,\n\ \ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.03510766597959217\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932022,\n \"\ acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932022\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.02649905770139744,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.02649905770139744\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5487179487179488,\n \"acc_stderr\": 0.025230381238934833,\n\ \ \"acc_norm\": 0.5487179487179488,\n \"acc_norm_stderr\": 0.025230381238934833\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6134453781512605,\n \"acc_stderr\": 0.03163145807552378,\n \ \ \"acc_norm\": 0.6134453781512605,\n \"acc_norm_stderr\": 0.03163145807552378\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8036697247706422,\n \"acc_stderr\": 0.01703071933915434,\n \"\ acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.01703071933915434\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069425,\n \ \ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069425\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6591928251121076,\n\ \ \"acc_stderr\": 0.0318114974705536,\n \"acc_norm\": 0.6591928251121076,\n\ \ \"acc_norm_stderr\": 0.0318114974705536\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728744,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728744\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.859504132231405,\n \"acc_stderr\": 0.031722334260021565,\n \"\ acc_norm\": 0.859504132231405,\n \"acc_norm_stderr\": 0.031722334260021565\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.042365112580946315,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.042365112580946315\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n\ \ \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\ \ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092365,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092365\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7969348659003831,\n\ \ \"acc_stderr\": 0.014385525076611573,\n \"acc_norm\": 0.7969348659003831,\n\ \ \"acc_norm_stderr\": 0.014385525076611573\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.025305258131879706,\n\ \ \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.025305258131879706\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3843575418994413,\n\ \ \"acc_stderr\": 0.016269088663959402,\n \"acc_norm\": 0.3843575418994413,\n\ \ \"acc_norm_stderr\": 0.016269088663959402\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.026857294663281413,\n\ \ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.026857294663281413\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.02592237178881877,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.02592237178881877\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.02563082497562135,\n\ \ \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.02563082497562135\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303062,\n \ \ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303062\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4439374185136897,\n\ \ \"acc_stderr\": 0.012689708167787684,\n \"acc_norm\": 0.4439374185136897,\n\ \ \"acc_norm_stderr\": 0.012689708167787684\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6286764705882353,\n \"acc_stderr\": 0.029349803139765873,\n\ \ \"acc_norm\": 0.6286764705882353,\n \"acc_norm_stderr\": 0.029349803139765873\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6552287581699346,\n \"acc_stderr\": 0.019228322018696647,\n \ \ \"acc_norm\": 0.6552287581699346,\n \"acc_norm_stderr\": 0.019228322018696647\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6069651741293532,\n\ \ \"acc_stderr\": 0.0345368246603156,\n \"acc_norm\": 0.6069651741293532,\n\ \ \"acc_norm_stderr\": 0.0345368246603156\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4879518072289157,\n\ \ \"acc_stderr\": 0.03891364495835821,\n \"acc_norm\": 0.4879518072289157,\n\ \ \"acc_norm_stderr\": 0.03891364495835821\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40758873929008566,\n\ \ \"mc1_stderr\": 0.01720194923455311,\n \"mc2\": 0.5783132294676995,\n\ \ \"mc2_stderr\": 0.015725405307929003\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.77663772691397,\n \"acc_stderr\": 0.0117056975652052\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5420773313115997,\n \ \ \"acc_stderr\": 0.013723629649844084\n }\n}\n```" repo_url: https://huggingface.co/Cartinoe5930/MoE-Merging leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|arc:challenge|25_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-23T16-02-03.400569.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|gsm8k|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hellaswag|10_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-23T16-02-03.400569.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-management|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T16-02-03.400569.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|truthfulqa:mc|0_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-23T16-02-03.400569.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_23T16_02_03.400569 path: - '**/details_harness|winogrande|5_2024-01-23T16-02-03.400569.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-23T16-02-03.400569.parquet' - config_name: results data_files: - split: 2024_01_23T16_02_03.400569 path: - results_2024-01-23T16-02-03.400569.parquet - split: latest path: - results_2024-01-23T16-02-03.400569.parquet --- # Dataset Card for Evaluation run of Cartinoe5930/MoE-Merging <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Cartinoe5930/MoE-Merging](https://huggingface.co/Cartinoe5930/MoE-Merging) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Cartinoe5930__MoE-Merging", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-23T16:02:03.400569](https://huggingface.co/datasets/open-llm-leaderboard/details_Cartinoe5930__MoE-Merging/blob/main/results_2024-01-23T16-02-03.400569.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6151505712161501, "acc_stderr": 0.03298337172536938, "acc_norm": 0.6177051984375612, "acc_norm_stderr": 0.03364497168310484, "mc1": 0.40758873929008566, "mc1_stderr": 0.01720194923455311, "mc2": 0.5783132294676995, "mc2_stderr": 0.015725405307929003 }, "harness|arc:challenge|25": { "acc": 0.6117747440273038, "acc_stderr": 0.014241614207414047, "acc_norm": 0.6544368600682594, "acc_norm_stderr": 0.013896938461145677 }, "harness|hellaswag|10": { "acc": 0.6493726349332802, "acc_stderr": 0.004761912511707511, "acc_norm": 0.8458474407488548, "acc_norm_stderr": 0.0036035695286784127 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.02825420034443866, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.02825420034443866 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.03773809990686934, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.03773809990686934 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.04784060704105654, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.04784060704105654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5234042553191489, "acc_stderr": 0.032650194750335815, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.032650194750335815 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.39473684210526316, "acc_stderr": 0.045981880578165414, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.045981880578165414 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41798941798941797, "acc_stderr": 0.02540255550326091, "acc_norm": 0.41798941798941797, "acc_norm_stderr": 0.02540255550326091 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3888888888888889, "acc_stderr": 0.04360314860077459, "acc_norm": 0.3888888888888889, "acc_norm_stderr": 0.04360314860077459 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6870967741935484, "acc_stderr": 0.026377567028645858, "acc_norm": 0.6870967741935484, "acc_norm_stderr": 0.026377567028645858 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.46798029556650245, "acc_stderr": 0.03510766597959217, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.03510766597959217 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7575757575757576, "acc_stderr": 0.030532892233932022, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932022 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8393782383419689, "acc_stderr": 0.02649905770139744, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.02649905770139744 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5487179487179488, "acc_stderr": 0.025230381238934833, "acc_norm": 0.5487179487179488, "acc_norm_stderr": 0.025230381238934833 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948496, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948496 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6134453781512605, "acc_stderr": 0.03163145807552378, "acc_norm": 0.6134453781512605, "acc_norm_stderr": 0.03163145807552378 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8036697247706422, "acc_stderr": 0.01703071933915434, "acc_norm": 0.8036697247706422, "acc_norm_stderr": 0.01703071933915434 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4583333333333333, "acc_stderr": 0.03398110890294636, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7721518987341772, "acc_stderr": 0.027303484599069425, "acc_norm": 0.7721518987341772, "acc_norm_stderr": 0.027303484599069425 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6591928251121076, "acc_stderr": 0.0318114974705536, "acc_norm": 0.6591928251121076, "acc_norm_stderr": 0.0318114974705536 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7557251908396947, "acc_stderr": 0.03768335959728744, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.03768335959728744 }, "harness|hendrycksTest-international_law|5": { "acc": 0.859504132231405, "acc_stderr": 0.031722334260021565, "acc_norm": 0.859504132231405, "acc_norm_stderr": 0.031722334260021565 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.042365112580946315, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.042365112580946315 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7116564417177914, "acc_stderr": 0.035590395316173425, "acc_norm": 0.7116564417177914, "acc_norm_stderr": 0.035590395316173425 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness|hendrycksTest-management|5": { "acc": 0.7475728155339806, "acc_stderr": 0.04301250399690878, "acc_norm": 0.7475728155339806, "acc_norm_stderr": 0.04301250399690878 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092365, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092365 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7969348659003831, "acc_stderr": 0.014385525076611573, "acc_norm": 0.7969348659003831, "acc_norm_stderr": 0.014385525076611573 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6705202312138728, "acc_stderr": 0.025305258131879706, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.025305258131879706 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3843575418994413, "acc_stderr": 0.016269088663959402, "acc_norm": 0.3843575418994413, "acc_norm_stderr": 0.016269088663959402 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.673202614379085, "acc_stderr": 0.026857294663281413, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.026857294663281413 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7041800643086816, "acc_stderr": 0.02592237178881877, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.02592237178881877 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6944444444444444, "acc_stderr": 0.02563082497562135, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.02563082497562135 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4716312056737589, "acc_stderr": 0.029779450957303062, "acc_norm": 0.4716312056737589, "acc_norm_stderr": 0.029779450957303062 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4439374185136897, "acc_stderr": 0.012689708167787684, "acc_norm": 0.4439374185136897, "acc_norm_stderr": 0.012689708167787684 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6286764705882353, "acc_stderr": 0.029349803139765873, "acc_norm": 0.6286764705882353, "acc_norm_stderr": 0.029349803139765873 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6552287581699346, "acc_stderr": 0.019228322018696647, "acc_norm": 0.6552287581699346, "acc_norm_stderr": 0.019228322018696647 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6069651741293532, "acc_stderr": 0.0345368246603156, "acc_norm": 0.6069651741293532, "acc_norm_stderr": 0.0345368246603156 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-virology|5": { "acc": 0.4879518072289157, "acc_stderr": 0.03891364495835821, "acc_norm": 0.4879518072289157, "acc_norm_stderr": 0.03891364495835821 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness|truthfulqa:mc|0": { "mc1": 0.40758873929008566, "mc1_stderr": 0.01720194923455311, "mc2": 0.5783132294676995, "mc2_stderr": 0.015725405307929003 }, "harness|winogrande|5": { "acc": 0.77663772691397, "acc_stderr": 0.0117056975652052 }, "harness|gsm8k|5": { "acc": 0.5420773313115997, "acc_stderr": 0.013723629649844084 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_23
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 996132684.0 num_examples: 195627 download_size: 1015513835 dataset_size: 996132684.0 --- # Dataset Card for "chunk_23" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
engima/ft-llava
--- dataset_info: features: - name: image dtype: image - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1369108.0 num_examples: 15 - name: validation num_bytes: 720410.0 num_examples: 15 - name: test num_bytes: 263333.0 num_examples: 6 download_size: 2355961 dataset_size: 2352851.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
adityarra07/test_ds_uwb_atc_noise
--- dataset_info: features: - name: audio struct: - name: array sequence: float32 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 228091121.30052426 num_examples: 1000 download_size: 229855749 dataset_size: 228091121.30052426 --- # Dataset Card for "test_ds_uwb_atc_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
another-symato/vnexpress-filter-word-seg
--- license: apache-2.0 ---
mxronga/gpt-teacher-yo
--- license: apache-2.0 task_categories: - translation - question-answering language: - yo - en ---
pin-lpt/bombay_sapphire_extended
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 21242770.0 num_examples: 10 download_size: 21245231 dataset_size: 21242770.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "bombay_sapphire_extended" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mnli_present_perfect_ever
--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 186342 num_examples: 793 - name: dev_mismatched num_bytes: 200160 num_examples: 788 - name: test_matched num_bytes: 220041 num_examples: 875 - name: test_mismatched num_bytes: 197234 num_examples: 826 - name: train num_bytes: 8005522 num_examples: 32860 download_size: 5376415 dataset_size: 8809299 --- # Dataset Card for "MULTI_VALUE_mnli_present_perfect_ever" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RIW/pokemon_longtail_marked
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: watermark_flag dtype: bool splits: - name: train num_bytes: 133046619.0 num_examples: 825 download_size: 133035681 dataset_size: 133046619.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
aghent/copiapoa-semantic-mask
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': image '1': mask splits: - name: train num_bytes: 296205716.0 num_examples: 20000 download_size: 133771266 dataset_size: 296205716.0 --- # Dataset Card for "copiapoa-semantic-mask" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ydang/nso-lux-macro
--- license: openrail dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1657075 num_examples: 1011 download_size: 968513 dataset_size: 1657075 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_kalisai__Nusantara-0.8b-Indo-Chat
--- pretty_name: Evaluation run of kalisai/Nusantara-0.8b-Indo-Chat dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kalisai/Nusantara-0.8b-Indo-Chat](https://huggingface.co/kalisai/Nusantara-0.8b-Indo-Chat)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kalisai__Nusantara-0.8b-Indo-Chat\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-11T04:47:00.134290](https://huggingface.co/datasets/open-llm-leaderboard/details_kalisai__Nusantara-0.8b-Indo-Chat/blob/main/results_2024-03-11T04-47-00.134290.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.27086777357013125,\n\ \ \"acc_stderr\": 0.03134225012474849,\n \"acc_norm\": 0.2725137250193632,\n\ \ \"acc_norm_stderr\": 0.032121599504878445,\n \"mc1\": 0.24112607099143207,\n\ \ \"mc1_stderr\": 0.014974827279752329,\n \"mc2\": 0.39538805225865514,\n\ \ \"mc2_stderr\": 0.014665309362336332\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.27474402730375425,\n \"acc_stderr\": 0.013044617212771227,\n\ \ \"acc_norm\": 0.3037542662116041,\n \"acc_norm_stderr\": 0.01343890918477876\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.35839474208325034,\n\ \ \"acc_stderr\": 0.004785488626807562,\n \"acc_norm\": 0.44612626966739694,\n\ \ \"acc_norm_stderr\": 0.0049607323822552386\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.1925925925925926,\n\ \ \"acc_stderr\": 0.034065420585026526,\n \"acc_norm\": 0.1925925925925926,\n\ \ \"acc_norm_stderr\": 0.034065420585026526\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.19736842105263158,\n \"acc_stderr\": 0.03238981601699397,\n\ \ \"acc_norm\": 0.19736842105263158,\n \"acc_norm_stderr\": 0.03238981601699397\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n\ \ \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \ \ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.3169811320754717,\n \"acc_stderr\": 0.02863723563980091,\n\ \ \"acc_norm\": 0.3169811320754717,\n \"acc_norm_stderr\": 0.02863723563980091\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.039420826399272135,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.039420826399272135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.24,\n\ \ \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816503,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816503\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\ \ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\ \ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.040925639582376536,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.040925639582376536\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.23,\n \"acc_stderr\": 0.04229525846816508,\n \"acc_norm\": 0.23,\n\ \ \"acc_norm_stderr\": 0.04229525846816508\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.03013590647851756,\n\ \ \"acc_norm\": 0.30638297872340425,\n \"acc_norm_stderr\": 0.03013590647851756\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.18421052631578946,\n\ \ \"acc_stderr\": 0.03646758875075566,\n \"acc_norm\": 0.18421052631578946,\n\ \ \"acc_norm_stderr\": 0.03646758875075566\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.21379310344827587,\n \"acc_stderr\": 0.03416520447747549,\n\ \ \"acc_norm\": 0.21379310344827587,\n \"acc_norm_stderr\": 0.03416520447747549\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2671957671957672,\n \"acc_stderr\": 0.022789673145776578,\n \"\ acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.022789673145776578\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23015873015873015,\n\ \ \"acc_stderr\": 0.03764950879790605,\n \"acc_norm\": 0.23015873015873015,\n\ \ \"acc_norm_stderr\": 0.03764950879790605\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.25483870967741934,\n\ \ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.25483870967741934,\n\ \ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.031447125816782405,\n\ \ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.031447125816782405\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322674,\n \"acc_norm\"\ : 0.22,\n \"acc_norm_stderr\": 0.041633319989322674\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.18181818181818182,\n \"acc_stderr\": 0.030117688929503582,\n\ \ \"acc_norm\": 0.18181818181818182,\n \"acc_norm_stderr\": 0.030117688929503582\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2828282828282828,\n \"acc_stderr\": 0.032087795587867514,\n \"\ acc_norm\": 0.2828282828282828,\n \"acc_norm_stderr\": 0.032087795587867514\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.35751295336787564,\n \"acc_stderr\": 0.034588160421810045,\n\ \ \"acc_norm\": 0.35751295336787564,\n \"acc_norm_stderr\": 0.034588160421810045\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.023901157979402538,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.023901157979402538\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \ \ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.3025210084033613,\n \"acc_stderr\": 0.02983796238829193,\n \ \ \"acc_norm\": 0.3025210084033613,\n \"acc_norm_stderr\": 0.02983796238829193\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.23841059602649006,\n \"acc_stderr\": 0.0347918557259966,\n \"\ acc_norm\": 0.23841059602649006,\n \"acc_norm_stderr\": 0.0347918557259966\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.3155963302752294,\n \"acc_stderr\": 0.01992611751386967,\n \"\ acc_norm\": 0.3155963302752294,\n \"acc_norm_stderr\": 0.01992611751386967\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4166666666666667,\n \"acc_stderr\": 0.03362277436608043,\n \"\ acc_norm\": 0.4166666666666667,\n \"acc_norm_stderr\": 0.03362277436608043\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.2549019607843137,\n \"acc_stderr\": 0.03058759135160425,\n \"\ acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.03058759135160425\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.2320675105485232,\n \"acc_stderr\": 0.02747974455080851,\n \ \ \"acc_norm\": 0.2320675105485232,\n \"acc_norm_stderr\": 0.02747974455080851\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.19282511210762332,\n\ \ \"acc_stderr\": 0.02647824096048936,\n \"acc_norm\": 0.19282511210762332,\n\ \ \"acc_norm_stderr\": 0.02647824096048936\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.3053435114503817,\n \"acc_stderr\": 0.0403931497872456,\n\ \ \"acc_norm\": 0.3053435114503817,\n \"acc_norm_stderr\": 0.0403931497872456\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2231404958677686,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.2231404958677686,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3055555555555556,\n\ \ \"acc_stderr\": 0.044531975073749834,\n \"acc_norm\": 0.3055555555555556,\n\ \ \"acc_norm_stderr\": 0.044531975073749834\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.27607361963190186,\n \"acc_stderr\": 0.03512385283705051,\n\ \ \"acc_norm\": 0.27607361963190186,\n \"acc_norm_stderr\": 0.03512385283705051\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\ \ \"acc_stderr\": 0.04464285714285713,\n \"acc_norm\": 0.33035714285714285,\n\ \ \"acc_norm_stderr\": 0.04464285714285713\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.2815533980582524,\n \"acc_stderr\": 0.044532548363264673,\n\ \ \"acc_norm\": 0.2815533980582524,\n \"acc_norm_stderr\": 0.044532548363264673\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.25213675213675213,\n\ \ \"acc_stderr\": 0.02844796547623102,\n \"acc_norm\": 0.25213675213675213,\n\ \ \"acc_norm_stderr\": 0.02844796547623102\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2681992337164751,\n\ \ \"acc_stderr\": 0.015842430835269438,\n \"acc_norm\": 0.2681992337164751,\n\ \ \"acc_norm_stderr\": 0.015842430835269438\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.23410404624277456,\n \"acc_stderr\": 0.022797110278071128,\n\ \ \"acc_norm\": 0.23410404624277456,\n \"acc_norm_stderr\": 0.022797110278071128\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2670391061452514,\n\ \ \"acc_stderr\": 0.01479650262256255,\n \"acc_norm\": 0.2670391061452514,\n\ \ \"acc_norm_stderr\": 0.01479650262256255\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.2581699346405229,\n \"acc_stderr\": 0.02505850331695815,\n\ \ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.02505850331695815\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.31189710610932475,\n\ \ \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.31189710610932475,\n\ \ \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.25308641975308643,\n \"acc_stderr\": 0.024191808600712992,\n\ \ \"acc_norm\": 0.25308641975308643,\n \"acc_norm_stderr\": 0.024191808600712992\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2624113475177305,\n \"acc_stderr\": 0.026244920349842996,\n \ \ \"acc_norm\": 0.2624113475177305,\n \"acc_norm_stderr\": 0.026244920349842996\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2542372881355932,\n\ \ \"acc_stderr\": 0.011121129007840676,\n \"acc_norm\": 0.2542372881355932,\n\ \ \"acc_norm_stderr\": 0.011121129007840676\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.029520095697687754,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.029520095697687754\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25326797385620914,\n \"acc_stderr\": 0.01759348689536683,\n \ \ \"acc_norm\": 0.25326797385620914,\n \"acc_norm_stderr\": 0.01759348689536683\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n\ \ \"acc_stderr\": 0.04013964554072775,\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.04013964554072775\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.2571428571428571,\n \"acc_stderr\": 0.027979823538744546,\n\ \ \"acc_norm\": 0.2571428571428571,\n \"acc_norm_stderr\": 0.027979823538744546\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23383084577114427,\n\ \ \"acc_stderr\": 0.029929415408348373,\n \"acc_norm\": 0.23383084577114427,\n\ \ \"acc_norm_stderr\": 0.029929415408348373\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.042295258468165065,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.042295258468165065\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3192771084337349,\n\ \ \"acc_stderr\": 0.0362933532994786,\n \"acc_norm\": 0.3192771084337349,\n\ \ \"acc_norm_stderr\": 0.0362933532994786\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.03508771929824565,\n\ \ \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.03508771929824565\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.24112607099143207,\n\ \ \"mc1_stderr\": 0.014974827279752329,\n \"mc2\": 0.39538805225865514,\n\ \ \"mc2_stderr\": 0.014665309362336332\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5469613259668509,\n \"acc_stderr\": 0.013990366632148104\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.014404852160727824,\n \ \ \"acc_stderr\": 0.0032820559171369574\n }\n}\n```" repo_url: https://huggingface.co/kalisai/Nusantara-0.8b-Indo-Chat leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|arc:challenge|25_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-11T04-47-00.134290.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|gsm8k|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hellaswag|10_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-47-00.134290.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-11T04-47-00.134290.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|truthfulqa:mc|0_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-11T04-47-00.134290.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_11T04_47_00.134290 path: - '**/details_harness|winogrande|5_2024-03-11T04-47-00.134290.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-11T04-47-00.134290.parquet' - config_name: results data_files: - split: 2024_03_11T04_47_00.134290 path: - results_2024-03-11T04-47-00.134290.parquet - split: latest path: - results_2024-03-11T04-47-00.134290.parquet --- # Dataset Card for Evaluation run of kalisai/Nusantara-0.8b-Indo-Chat <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [kalisai/Nusantara-0.8b-Indo-Chat](https://huggingface.co/kalisai/Nusantara-0.8b-Indo-Chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_kalisai__Nusantara-0.8b-Indo-Chat", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-11T04:47:00.134290](https://huggingface.co/datasets/open-llm-leaderboard/details_kalisai__Nusantara-0.8b-Indo-Chat/blob/main/results_2024-03-11T04-47-00.134290.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.27086777357013125, "acc_stderr": 0.03134225012474849, "acc_norm": 0.2725137250193632, "acc_norm_stderr": 0.032121599504878445, "mc1": 0.24112607099143207, "mc1_stderr": 0.014974827279752329, "mc2": 0.39538805225865514, "mc2_stderr": 0.014665309362336332 }, "harness|arc:challenge|25": { "acc": 0.27474402730375425, "acc_stderr": 0.013044617212771227, "acc_norm": 0.3037542662116041, "acc_norm_stderr": 0.01343890918477876 }, "harness|hellaswag|10": { "acc": 0.35839474208325034, "acc_stderr": 0.004785488626807562, "acc_norm": 0.44612626966739694, "acc_norm_stderr": 0.0049607323822552386 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.1925925925925926, "acc_stderr": 0.034065420585026526, "acc_norm": 0.1925925925925926, "acc_norm_stderr": 0.034065420585026526 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.19736842105263158, "acc_stderr": 0.03238981601699397, "acc_norm": 0.19736842105263158, "acc_norm_stderr": 0.03238981601699397 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.3169811320754717, "acc_stderr": 0.02863723563980091, "acc_norm": 0.3169811320754717, "acc_norm_stderr": 0.02863723563980091 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.3333333333333333, "acc_stderr": 0.039420826399272135, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.039420826399272135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.24, "acc_stderr": 0.04292346959909282, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816503, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816503 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.24277456647398843, "acc_stderr": 0.0326926380614177, "acc_norm": 0.24277456647398843, "acc_norm_stderr": 0.0326926380614177 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.040925639582376536, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.040925639582376536 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.23, "acc_stderr": 0.04229525846816508, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816508 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.30638297872340425, "acc_stderr": 0.03013590647851756, "acc_norm": 0.30638297872340425, "acc_norm_stderr": 0.03013590647851756 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.18421052631578946, "acc_stderr": 0.03646758875075566, "acc_norm": 0.18421052631578946, "acc_norm_stderr": 0.03646758875075566 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.21379310344827587, "acc_stderr": 0.03416520447747549, "acc_norm": 0.21379310344827587, "acc_norm_stderr": 0.03416520447747549 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2671957671957672, "acc_stderr": 0.022789673145776578, "acc_norm": 0.2671957671957672, "acc_norm_stderr": 0.022789673145776578 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23015873015873015, "acc_stderr": 0.03764950879790605, "acc_norm": 0.23015873015873015, "acc_norm_stderr": 0.03764950879790605 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.25483870967741934, "acc_stderr": 0.024790118459332208, "acc_norm": 0.25483870967741934, "acc_norm_stderr": 0.024790118459332208 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.27586206896551724, "acc_stderr": 0.031447125816782405, "acc_norm": 0.27586206896551724, "acc_norm_stderr": 0.031447125816782405 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.22, "acc_stderr": 0.041633319989322674, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322674 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.18181818181818182, "acc_stderr": 0.030117688929503582, "acc_norm": 0.18181818181818182, "acc_norm_stderr": 0.030117688929503582 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2828282828282828, "acc_stderr": 0.032087795587867514, "acc_norm": 0.2828282828282828, "acc_norm_stderr": 0.032087795587867514 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.35751295336787564, "acc_stderr": 0.034588160421810045, "acc_norm": 0.35751295336787564, "acc_norm_stderr": 0.034588160421810045 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.023901157979402538, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.023901157979402538 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085626, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085626 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.3025210084033613, "acc_stderr": 0.02983796238829193, "acc_norm": 0.3025210084033613, "acc_norm_stderr": 0.02983796238829193 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.23841059602649006, "acc_stderr": 0.0347918557259966, "acc_norm": 0.23841059602649006, "acc_norm_stderr": 0.0347918557259966 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3155963302752294, "acc_stderr": 0.01992611751386967, "acc_norm": 0.3155963302752294, "acc_norm_stderr": 0.01992611751386967 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4166666666666667, "acc_stderr": 0.03362277436608043, "acc_norm": 0.4166666666666667, "acc_norm_stderr": 0.03362277436608043 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2549019607843137, "acc_stderr": 0.03058759135160425, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.03058759135160425 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2320675105485232, "acc_stderr": 0.02747974455080851, "acc_norm": 0.2320675105485232, "acc_norm_stderr": 0.02747974455080851 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.19282511210762332, "acc_stderr": 0.02647824096048936, "acc_norm": 0.19282511210762332, "acc_norm_stderr": 0.02647824096048936 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.3053435114503817, "acc_stderr": 0.0403931497872456, "acc_norm": 0.3053435114503817, "acc_norm_stderr": 0.0403931497872456 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2231404958677686, "acc_stderr": 0.03800754475228733, "acc_norm": 0.2231404958677686, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.3055555555555556, "acc_stderr": 0.044531975073749834, "acc_norm": 0.3055555555555556, "acc_norm_stderr": 0.044531975073749834 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.27607361963190186, "acc_stderr": 0.03512385283705051, "acc_norm": 0.27607361963190186, "acc_norm_stderr": 0.03512385283705051 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.33035714285714285, "acc_stderr": 0.04464285714285713, "acc_norm": 0.33035714285714285, "acc_norm_stderr": 0.04464285714285713 }, "harness|hendrycksTest-management|5": { "acc": 0.2815533980582524, "acc_stderr": 0.044532548363264673, "acc_norm": 0.2815533980582524, "acc_norm_stderr": 0.044532548363264673 }, "harness|hendrycksTest-marketing|5": { "acc": 0.25213675213675213, "acc_stderr": 0.02844796547623102, "acc_norm": 0.25213675213675213, "acc_norm_stderr": 0.02844796547623102 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2681992337164751, "acc_stderr": 0.015842430835269438, "acc_norm": 0.2681992337164751, "acc_norm_stderr": 0.015842430835269438 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.23410404624277456, "acc_stderr": 0.022797110278071128, "acc_norm": 0.23410404624277456, "acc_norm_stderr": 0.022797110278071128 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2670391061452514, "acc_stderr": 0.01479650262256255, "acc_norm": 0.2670391061452514, "acc_norm_stderr": 0.01479650262256255 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2581699346405229, "acc_stderr": 0.02505850331695815, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.02505850331695815 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.31189710610932475, "acc_stderr": 0.02631185807185416, "acc_norm": 0.31189710610932475, "acc_norm_stderr": 0.02631185807185416 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.25308641975308643, "acc_stderr": 0.024191808600712992, "acc_norm": 0.25308641975308643, "acc_norm_stderr": 0.024191808600712992 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349842996, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349842996 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2542372881355932, "acc_stderr": 0.011121129007840676, "acc_norm": 0.2542372881355932, "acc_norm_stderr": 0.011121129007840676 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.38235294117647056, "acc_stderr": 0.029520095697687754, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.029520095697687754 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25326797385620914, "acc_stderr": 0.01759348689536683, "acc_norm": 0.25326797385620914, "acc_norm_stderr": 0.01759348689536683 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.22727272727272727, "acc_stderr": 0.04013964554072775, "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.04013964554072775 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.2571428571428571, "acc_stderr": 0.027979823538744546, "acc_norm": 0.2571428571428571, "acc_norm_stderr": 0.027979823538744546 }, "harness|hendrycksTest-sociology|5": { "acc": 0.23383084577114427, "acc_stderr": 0.029929415408348373, "acc_norm": 0.23383084577114427, "acc_norm_stderr": 0.029929415408348373 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.23, "acc_stderr": 0.042295258468165065, "acc_norm": 0.23, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-virology|5": { "acc": 0.3192771084337349, "acc_stderr": 0.0362933532994786, "acc_norm": 0.3192771084337349, "acc_norm_stderr": 0.0362933532994786 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.2982456140350877, "acc_stderr": 0.03508771929824565, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.03508771929824565 }, "harness|truthfulqa:mc|0": { "mc1": 0.24112607099143207, "mc1_stderr": 0.014974827279752329, "mc2": 0.39538805225865514, "mc2_stderr": 0.014665309362336332 }, "harness|winogrande|5": { "acc": 0.5469613259668509, "acc_stderr": 0.013990366632148104 }, "harness|gsm8k|5": { "acc": 0.014404852160727824, "acc_stderr": 0.0032820559171369574 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
coolboy0821/aaaaa
--- license: apache-2.0 ---
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot-mathema-acb860-1886064281
--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_test_cot eval_info: task: text_zero_shot_classification model: facebook/opt-13b metrics: [] dataset_name: mathemakitten/winobias_antistereotype_test_cot dataset_config: mathemakitten--winobias_antistereotype_test_cot dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-13b * Dataset: mathemakitten/winobias_antistereotype_test_cot * Config: mathemakitten--winobias_antistereotype_test_cot * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
FSMBench/fsmbench_what_will_be_the_state
--- dataset_info: features: - name: query_id dtype: string - name: fsm_id dtype: string - name: fsm_json dtype: string - name: difficulty_level dtype: int64 - name: transition_matrix dtype: string - name: query dtype: string - name: answer dtype: string - name: substring_index dtype: int64 splits: - name: validation num_bytes: 15175172 num_examples: 9425 download_size: 776600 dataset_size: 15175172 configs: - config_name: default data_files: - split: validation path: data/validation-* ---
Seongill/Trivia_missing_5_full_substitution
--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: random_sub dtype: string - name: similar_sub dtype: string - name: ent_type dtype: string splits: - name: train num_bytes: 41367743 num_examples: 11313 download_size: 25016105 dataset_size: 41367743 configs: - config_name: default data_files: - split: train path: data/train-* ---
Appdemon/profile
--- license: other ---
kaleemWaheed/twitter_dataset_1713036777
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 9944 num_examples: 21 download_size: 9736 dataset_size: 9944 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_ajibawa-2023__OpenHermes-2.5-Code-290k-13B
--- pretty_name: Evaluation run of ajibawa-2023/OpenHermes-2.5-Code-290k-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ajibawa-2023/OpenHermes-2.5-Code-290k-13B](https://huggingface.co/ajibawa-2023/OpenHermes-2.5-Code-290k-13B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__OpenHermes-2.5-Code-290k-13B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-01T14:12:53.721553](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__OpenHermes-2.5-Code-290k-13B/blob/main/results_2024-03-01T14-12-53.721553.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5691022088132593,\n\ \ \"acc_stderr\": 0.03389769920574544,\n \"acc_norm\": 0.5694708840855052,\n\ \ \"acc_norm_stderr\": 0.03459305314080333,\n \"mc1\": 0.3537331701346389,\n\ \ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5250168114529904,\n\ \ \"mc2_stderr\": 0.015529659346453022\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5494880546075085,\n \"acc_stderr\": 0.014539646098471627,\n\ \ \"acc_norm\": 0.5733788395904437,\n \"acc_norm_stderr\": 0.014453185592920293\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6139215295757817,\n\ \ \"acc_stderr\": 0.004858539527872461,\n \"acc_norm\": 0.8048197570205139,\n\ \ \"acc_norm_stderr\": 0.003955293887824637\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411022,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411022\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.04033565667848319,\n\ \ \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.04033565667848319\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5962264150943396,\n \"acc_stderr\": 0.03019761160019795,\n\ \ \"acc_norm\": 0.5962264150943396,\n \"acc_norm_stderr\": 0.03019761160019795\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6180555555555556,\n\ \ \"acc_stderr\": 0.04062990784146667,\n \"acc_norm\": 0.6180555555555556,\n\ \ \"acc_norm_stderr\": 0.04062990784146667\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.47398843930635837,\n\ \ \"acc_stderr\": 0.038073017265045125,\n \"acc_norm\": 0.47398843930635837,\n\ \ \"acc_norm_stderr\": 0.038073017265045125\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\ \ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n\ \ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n\ \ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.35964912280701755,\n\ \ \"acc_stderr\": 0.045144961328736334,\n \"acc_norm\": 0.35964912280701755,\n\ \ \"acc_norm_stderr\": 0.045144961328736334\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n\ \ \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\"\ : 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\ \ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\ \ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6387096774193548,\n\ \ \"acc_stderr\": 0.027327548447957546,\n \"acc_norm\": 0.6387096774193548,\n\ \ \"acc_norm_stderr\": 0.027327548447957546\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.03524390844511781,\n\ \ \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.03524390844511781\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7323232323232324,\n \"acc_stderr\": 0.03154449888270285,\n \"\ acc_norm\": 0.7323232323232324,\n \"acc_norm_stderr\": 0.03154449888270285\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7461139896373057,\n \"acc_stderr\": 0.031410247805653206,\n\ \ \"acc_norm\": 0.7461139896373057,\n \"acc_norm_stderr\": 0.031410247805653206\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5435897435897435,\n \"acc_stderr\": 0.0252544854247996,\n \ \ \"acc_norm\": 0.5435897435897435,\n \"acc_norm_stderr\": 0.0252544854247996\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340492,\n \ \ \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340492\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5798319327731093,\n \"acc_stderr\": 0.03206183783236153,\n \ \ \"acc_norm\": 0.5798319327731093,\n \"acc_norm_stderr\": 0.03206183783236153\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7504587155963303,\n \"acc_stderr\": 0.018553897629501624,\n \"\ acc_norm\": 0.7504587155963303,\n \"acc_norm_stderr\": 0.018553897629501624\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7598039215686274,\n \"acc_stderr\": 0.02998373305591362,\n \"\ acc_norm\": 0.7598039215686274,\n \"acc_norm_stderr\": 0.02998373305591362\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \ \ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6457399103139013,\n\ \ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\ \ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6335877862595419,\n \"acc_stderr\": 0.042258754519696365,\n\ \ \"acc_norm\": 0.6335877862595419,\n \"acc_norm_stderr\": 0.042258754519696365\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070415,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070415\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\ \ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n\ \ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6993865030674846,\n \"acc_stderr\": 0.03602511318806771,\n\ \ \"acc_norm\": 0.6993865030674846,\n \"acc_norm_stderr\": 0.03602511318806771\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\ \ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8376068376068376,\n\ \ \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n\ \ \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7394636015325671,\n\ \ \"acc_stderr\": 0.015696008563807075,\n \"acc_norm\": 0.7394636015325671,\n\ \ \"acc_norm_stderr\": 0.015696008563807075\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.025816756791584194,\n\ \ \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.025816756791584194\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32737430167597764,\n\ \ \"acc_stderr\": 0.015694238967737383,\n \"acc_norm\": 0.32737430167597764,\n\ \ \"acc_norm_stderr\": 0.015694238967737383\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5915032679738562,\n \"acc_stderr\": 0.028146405993096358,\n\ \ \"acc_norm\": 0.5915032679738562,\n \"acc_norm_stderr\": 0.028146405993096358\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5819935691318328,\n\ \ \"acc_stderr\": 0.02801365189199507,\n \"acc_norm\": 0.5819935691318328,\n\ \ \"acc_norm_stderr\": 0.02801365189199507\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.595679012345679,\n \"acc_stderr\": 0.027306625297327677,\n\ \ \"acc_norm\": 0.595679012345679,\n \"acc_norm_stderr\": 0.027306625297327677\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628406,\n \ \ \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628406\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44198174706649285,\n\ \ \"acc_stderr\": 0.01268397251359881,\n \"acc_norm\": 0.44198174706649285,\n\ \ \"acc_norm_stderr\": 0.01268397251359881\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030372836961539352,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030372836961539352\n \ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\"\ : 0.5751633986928104,\n \"acc_stderr\": 0.01999797303545833,\n \"\ acc_norm\": 0.5751633986928104,\n \"acc_norm_stderr\": 0.01999797303545833\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\ \ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7263681592039801,\n\ \ \"acc_stderr\": 0.03152439186555402,\n \"acc_norm\": 0.7263681592039801,\n\ \ \"acc_norm_stderr\": 0.03152439186555402\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816508\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.43373493975903615,\n\ \ \"acc_stderr\": 0.03858158940685517,\n \"acc_norm\": 0.43373493975903615,\n\ \ \"acc_norm_stderr\": 0.03858158940685517\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.031267817146631786,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.031267817146631786\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3537331701346389,\n\ \ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5250168114529904,\n\ \ \"mc2_stderr\": 0.015529659346453022\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7482241515390686,\n \"acc_stderr\": 0.01219848910025978\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5830174374526156,\n \ \ \"acc_stderr\": 0.013581320997216595\n }\n}\n```" repo_url: https://huggingface.co/ajibawa-2023/OpenHermes-2.5-Code-290k-13B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|arc:challenge|25_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-01T14-12-53.721553.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|gsm8k|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hellaswag|10_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-01T14-12-53.721553.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-management|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T14-12-53.721553.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|truthfulqa:mc|0_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-01T14-12-53.721553.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_01T14_12_53.721553 path: - '**/details_harness|winogrande|5_2024-03-01T14-12-53.721553.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-01T14-12-53.721553.parquet' - config_name: results data_files: - split: 2024_03_01T14_12_53.721553 path: - results_2024-03-01T14-12-53.721553.parquet - split: latest path: - results_2024-03-01T14-12-53.721553.parquet --- # Dataset Card for Evaluation run of ajibawa-2023/OpenHermes-2.5-Code-290k-13B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ajibawa-2023/OpenHermes-2.5-Code-290k-13B](https://huggingface.co/ajibawa-2023/OpenHermes-2.5-Code-290k-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__OpenHermes-2.5-Code-290k-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-01T14:12:53.721553](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__OpenHermes-2.5-Code-290k-13B/blob/main/results_2024-03-01T14-12-53.721553.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5691022088132593, "acc_stderr": 0.03389769920574544, "acc_norm": 0.5694708840855052, "acc_norm_stderr": 0.03459305314080333, "mc1": 0.3537331701346389, "mc1_stderr": 0.016737814358846147, "mc2": 0.5250168114529904, "mc2_stderr": 0.015529659346453022 }, "harness|arc:challenge|25": { "acc": 0.5494880546075085, "acc_stderr": 0.014539646098471627, "acc_norm": 0.5733788395904437, "acc_norm_stderr": 0.014453185592920293 }, "harness|hellaswag|10": { "acc": 0.6139215295757817, "acc_stderr": 0.004858539527872461, "acc_norm": 0.8048197570205139, "acc_norm_stderr": 0.003955293887824637 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.04793724854411022, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411022 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5657894736842105, "acc_stderr": 0.04033565667848319, "acc_norm": 0.5657894736842105, "acc_norm_stderr": 0.04033565667848319 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5962264150943396, "acc_stderr": 0.03019761160019795, "acc_norm": 0.5962264150943396, "acc_norm_stderr": 0.03019761160019795 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6180555555555556, "acc_stderr": 0.04062990784146667, "acc_norm": 0.6180555555555556, "acc_norm_stderr": 0.04062990784146667 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.47398843930635837, "acc_stderr": 0.038073017265045125, "acc_norm": 0.47398843930635837, "acc_norm_stderr": 0.038073017265045125 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.28431372549019607, "acc_stderr": 0.04488482852329017, "acc_norm": 0.28431372549019607, "acc_norm_stderr": 0.04488482852329017 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4553191489361702, "acc_stderr": 0.03255525359340355, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.03255525359340355 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.35964912280701755, "acc_stderr": 0.045144961328736334, "acc_norm": 0.35964912280701755, "acc_norm_stderr": 0.045144961328736334 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.496551724137931, "acc_stderr": 0.041665675771015785, "acc_norm": 0.496551724137931, "acc_norm_stderr": 0.041665675771015785 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.373015873015873, "acc_stderr": 0.02490699045899257, "acc_norm": 0.373015873015873, "acc_norm_stderr": 0.02490699045899257 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.043435254289490965, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.043435254289490965 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6387096774193548, "acc_stderr": 0.027327548447957546, "acc_norm": 0.6387096774193548, "acc_norm_stderr": 0.027327548447957546 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.035176035403610084, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.035176035403610084 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7151515151515152, "acc_stderr": 0.03524390844511781, "acc_norm": 0.7151515151515152, "acc_norm_stderr": 0.03524390844511781 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7323232323232324, "acc_stderr": 0.03154449888270285, "acc_norm": 0.7323232323232324, "acc_norm_stderr": 0.03154449888270285 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7461139896373057, "acc_stderr": 0.031410247805653206, "acc_norm": 0.7461139896373057, "acc_norm_stderr": 0.031410247805653206 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5435897435897435, "acc_stderr": 0.0252544854247996, "acc_norm": 0.5435897435897435, "acc_norm_stderr": 0.0252544854247996 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2851851851851852, "acc_stderr": 0.027528599210340492, "acc_norm": 0.2851851851851852, "acc_norm_stderr": 0.027528599210340492 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5798319327731093, "acc_stderr": 0.03206183783236153, "acc_norm": 0.5798319327731093, "acc_norm_stderr": 0.03206183783236153 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7504587155963303, "acc_stderr": 0.018553897629501624, "acc_norm": 0.7504587155963303, "acc_norm_stderr": 0.018553897629501624 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7598039215686274, "acc_stderr": 0.02998373305591362, "acc_norm": 0.7598039215686274, "acc_norm_stderr": 0.02998373305591362 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.759493670886076, "acc_stderr": 0.027820781981149685, "acc_norm": 0.759493670886076, "acc_norm_stderr": 0.027820781981149685 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6457399103139013, "acc_stderr": 0.032100621541349864, "acc_norm": 0.6457399103139013, "acc_norm_stderr": 0.032100621541349864 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6335877862595419, "acc_stderr": 0.042258754519696365, "acc_norm": 0.6335877862595419, "acc_norm_stderr": 0.042258754519696365 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070415, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070415 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7129629629629629, "acc_stderr": 0.043733130409147614, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6993865030674846, "acc_stderr": 0.03602511318806771, "acc_norm": 0.6993865030674846, "acc_norm_stderr": 0.03602511318806771 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4107142857142857, "acc_stderr": 0.04669510663875191, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.04669510663875191 }, "harness|hendrycksTest-management|5": { "acc": 0.7378640776699029, "acc_stderr": 0.04354631077260595, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260595 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8376068376068376, "acc_stderr": 0.02416161812798774, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.02416161812798774 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7394636015325671, "acc_stderr": 0.015696008563807075, "acc_norm": 0.7394636015325671, "acc_norm_stderr": 0.015696008563807075 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6416184971098265, "acc_stderr": 0.025816756791584194, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.025816756791584194 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.32737430167597764, "acc_stderr": 0.015694238967737383, "acc_norm": 0.32737430167597764, "acc_norm_stderr": 0.015694238967737383 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5915032679738562, "acc_stderr": 0.028146405993096358, "acc_norm": 0.5915032679738562, "acc_norm_stderr": 0.028146405993096358 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5819935691318328, "acc_stderr": 0.02801365189199507, "acc_norm": 0.5819935691318328, "acc_norm_stderr": 0.02801365189199507 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.595679012345679, "acc_stderr": 0.027306625297327677, "acc_norm": 0.595679012345679, "acc_norm_stderr": 0.027306625297327677 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.43617021276595747, "acc_stderr": 0.02958345203628406, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.02958345203628406 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44198174706649285, "acc_stderr": 0.01268397251359881, "acc_norm": 0.44198174706649285, "acc_norm_stderr": 0.01268397251359881 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5, "acc_stderr": 0.030372836961539352, "acc_norm": 0.5, "acc_norm_stderr": 0.030372836961539352 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5751633986928104, "acc_stderr": 0.01999797303545833, "acc_norm": 0.5751633986928104, "acc_norm_stderr": 0.01999797303545833 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6448979591836734, "acc_stderr": 0.030635655150387638, "acc_norm": 0.6448979591836734, "acc_norm_stderr": 0.030635655150387638 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7263681592039801, "acc_stderr": 0.03152439186555402, "acc_norm": 0.7263681592039801, "acc_norm_stderr": 0.03152439186555402 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness|hendrycksTest-virology|5": { "acc": 0.43373493975903615, "acc_stderr": 0.03858158940685517, "acc_norm": 0.43373493975903615, "acc_norm_stderr": 0.03858158940685517 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7894736842105263, "acc_stderr": 0.031267817146631786, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.031267817146631786 }, "harness|truthfulqa:mc|0": { "mc1": 0.3537331701346389, "mc1_stderr": 0.016737814358846147, "mc2": 0.5250168114529904, "mc2_stderr": 0.015529659346453022 }, "harness|winogrande|5": { "acc": 0.7482241515390686, "acc_stderr": 0.01219848910025978 }, "harness|gsm8k|5": { "acc": 0.5830174374526156, "acc_stderr": 0.013581320997216595 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
heegyu/chart2text_statista
--- dataset_info: features: - name: id dtype: int64 - name: title dtype: string - name: dataPath dtype: string - name: imgPath dtype: string - name: caption dtype: string - name: first_caption dtype: string - name: chartType dtype: string - name: release date dtype: string - name: Region dtype: string - name: survey time period dtype: string - name: xAxis dtype: string - name: yAxis dtype: string - name: URL dtype: string - name: image dtype: image - name: data dtype: string - name: columns dtype: string - name: length dtype: float64 splits: - name: train num_bytes: 1034457048.216 num_examples: 24368 - name: val num_bytes: 223876316.638 num_examples: 5221 - name: test num_bytes: 224020677.682 num_examples: 5222 download_size: 763065167 dataset_size: 1482354042.536 --- # Dataset Card for "chart2text_statista" original dataset: https://github.com/vis-nlp/Chart-to-text
Rasi1610/DeathSe46_series2_p1
--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 221497316.0 num_examples: 229 - name: val num_bytes: 57795455.0 num_examples: 58 download_size: 278851464 dataset_size: 279292771.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* ---
hac541309/basic_korean_dict
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 198591964 num_examples: 74936 download_size: 88466367 dataset_size: 198591964 license: cc-by-sa-3.0 task_categories: - table-question-answering - text-generation - text-classification - question-answering language: - ko - mn - vi - th - id - ru - ja - en - fr - es - ar - zh pretty_name: 한국어기초사전 size_categories: - 1M<n<10M tags: - dictionary --- # Dataset Card for "basic_korean_dict" This dataset is a NLP learnable form of [Korean Basic Dictionary(한국어기초사전)](https://krdict.korean.go.kr/). It follows the [original copyright policy (cc-by-sa-2.0)](https://krdict.korean.go.kr/kboardPolicy/copyRightTermsInfo) Some words have usage examples in other languages, effectively rendering this into a parallel corpus. This version is built from xls_20230601 [한국어 기초 사전](https://krdict.korean.go.kr/)을 학습 가능한 형태로 처리한 데이터입니다. [한국어 기초 사전](https://krdict.korean.go.kr/kboardPolicy/copyRightTermsInfo)의 저작권을 따릅니다. 여러 언어로 이루어진 표제어들이 있어 병렬 말뭉치의 기능이 있습니다. xls_20230601으로부터 생성되었습니다.
ThingsSolver/nsql-eng
--- dataset_info: features: - name: question dtype: string - name: context dtype: string - name: answer dtype: string - name: instruction dtype: string - name: prompt dtype: string - name: is_english dtype: bool - name: text dtype: string splits: - name: train num_bytes: 911778978 num_examples: 261423 download_size: 226661607 dataset_size: 911778978 --- # Dataset Card for "nsql-eng" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vincenttttt/CtoCollege_all_ForFineTune
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: train num_bytes: 1546267 num_examples: 3673 download_size: 300520 dataset_size: 1546267 --- # Dataset Card for "CtoCollege_all_ForFineTune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_DevaMalla__llama-base-7b
--- pretty_name: Evaluation run of DevaMalla/llama-base-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [DevaMalla/llama-base-7b](https://huggingface.co/DevaMalla/llama-base-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_DevaMalla__llama-base-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-16T19:28:44.136872](https://huggingface.co/datasets/open-llm-leaderboard/details_DevaMalla__llama-base-7b/blob/main/results_2023-09-16T19-28-44.136872.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n\ \ \"em_stderr\": 0.0003314581465219126,\n \"f1\": 0.056186031879194784,\n\ \ \"f1_stderr\": 0.0012858243614759428,\n \"acc\": 0.3749593848153363,\n\ \ \"acc_stderr\": 0.008901319861891403\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.0003314581465219126,\n\ \ \"f1\": 0.056186031879194784,\n \"f1_stderr\": 0.0012858243614759428\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0356330553449583,\n \ \ \"acc_stderr\": 0.00510610785374419\n },\n \"harness|winogrande|5\":\ \ {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.012696531870038616\n\ \ }\n}\n```" repo_url: https://huggingface.co/DevaMalla/llama-base-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|arc:challenge|25_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-22T13:24:25.697077.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_16T19_28_44.136872 path: - '**/details_harness|drop|3_2023-09-16T19-28-44.136872.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-16T19-28-44.136872.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_16T19_28_44.136872 path: - '**/details_harness|gsm8k|5_2023-09-16T19-28-44.136872.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-16T19-28-44.136872.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hellaswag|10_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:24:25.697077.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:24:25.697077.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_22T13_24_25.697077 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:24:25.697077.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T13:24:25.697077.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_16T19_28_44.136872 path: - '**/details_harness|winogrande|5_2023-09-16T19-28-44.136872.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-16T19-28-44.136872.parquet' - config_name: results data_files: - split: 2023_09_16T19_28_44.136872 path: - results_2023-09-16T19-28-44.136872.parquet - split: latest path: - results_2023-09-16T19-28-44.136872.parquet --- # Dataset Card for Evaluation run of DevaMalla/llama-base-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/DevaMalla/llama-base-7b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [DevaMalla/llama-base-7b](https://huggingface.co/DevaMalla/llama-base-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_DevaMalla__llama-base-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-16T19:28:44.136872](https://huggingface.co/datasets/open-llm-leaderboard/details_DevaMalla__llama-base-7b/blob/main/results_2023-09-16T19-28-44.136872.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219126, "f1": 0.056186031879194784, "f1_stderr": 0.0012858243614759428, "acc": 0.3749593848153363, "acc_stderr": 0.008901319861891403 }, "harness|drop|3": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219126, "f1": 0.056186031879194784, "f1_stderr": 0.0012858243614759428 }, "harness|gsm8k|5": { "acc": 0.0356330553449583, "acc_stderr": 0.00510610785374419 }, "harness|winogrande|5": { "acc": 0.7142857142857143, "acc_stderr": 0.012696531870038616 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
LND-EDUCATION/bambara_audio_synthetic
--- license: apache-2.0 language: - bm pretty_name: Bambara_audio size_categories: - 1K<n<10K ---
autoevaluate/autoeval-eval-squad-plain_text-a52a81-2596378857
--- type: predictions tags: - autotrain - evaluation datasets: - squad eval_info: task: extractive_question_answering model: florenceGundy/bert-finetuned-squad metrics: [] dataset_name: squad dataset_config: plain_text dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: florenceGundy/bert-finetuned-squad * Dataset: squad * Config: plain_text * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@florenceGundy](https://huggingface.co/florenceGundy) for evaluating this model.
umarigan/falcon_feedback_instruction
--- dataset_info: features: - name: instruction dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 7942624 num_examples: 7401 download_size: 5146500 dataset_size: 7942624 configs: - config_name: default data_files: - split: train path: data/train-* --- converted https://huggingface.co/datasets/argilla/comparison-data-falcon-with-feedback?row=0 to more proper format
knowrohit07/know-saraswati-cot
--- license: openrail --- ### 🚨 To all devs, scholars, and also fugazis of AI - A Philosophical Standpoint on AGI: - This is extraneous, if you have time to read it-- give it a shot. We stand at the precipice of a digital era where the notions of artificial intelligence are often muddled with the grandiose idea of Artificial General Intelligence (AGI). Here's a candid reflection: 1. Current LLMs and their Limitations: Let's be unequivocally clear—present-day language models, including transformers, are not a direct path to AGI. They are sophisticated token predictors, highly skilled in generalizing from vast datasets but lacking true understanding. They operate in what might be termed the 'dog-AGI' phase—impressive, yes, but nowhere close to the 'god-AGI' phase we aspire to reach. 2. The Nature of 'Smart': These models, for all their complexity, are not sentient. They don't possess the rich tapestry of human experience—our memories, relationships, and 'eureka' moments that constitute learning and wisdom. They are yet to evolve from merely processing information to experiencing and understanding the nuances of life as we know it. 3. Stockpiling NVIDIA cards and accumulating GPU clusters is not the golden ticket to AGI. The pursuit of AGI is not solely a quest for more processing power. It is a deeper, more philosophical journey where: - Space Outposts and Ion Engines: Mankind should expandd beyond the terrestrial, reaching for space outposts and harnessing commercialized ion engines for space travel. Ion engines, with their extended operational capacity, liberate us from the constraints of chemical fuel, enabling voyages that stretch both time and distance. - Asteroid Mining and the Periodic Table: The quest for AGI is mirrored in our endeavor to mine asteroids, potentially revealing new elements that could add unknown dimensions to our periodic table. This is not merely resource extraction; it is an exploration that feeds into the self-iterative learning nature of AGI, fostering an intelligence that grows with each discovery. - Nuclear Mass Energy and Helium-3: We look beyond silicon to the immense potential of nuclear mass energy. Helium-3, fused from deuterium in high-efficiency fusion generators, represents a future energy source that could power the next leaps in AGI development. Overcoming the scarcity of Helium-3 is a challenge we are poised to tackle, paving the way for a new era of energy abundance. 4. The Road Ahead: As we venture into the unknown, let's reimagine our approach. We seek an AI that lives a 'life', so to speak, with context vectors representing not just data points but the essence of existence itself. Imagine an AI with a library of experiences, including life choices and personal growth, akin to a human with 60 years of rich, varied living. ## Overview The know-saraswati-cot dataset is a curated collection of examples designed to train and evaluate large language models (LLMs) on stream of consciousness (SoC), chain of thought (CoT), and logical reasoning. Named after Saraswati, the Hindu goddess of knowledge, wisdom, and learning, this dataset embodies the spirit of open-source knowledge sharing. It is an ode to democratizing knowledge, making it as accessible as the flowing waters of the mythical Saraswati river. With addtional 30,000 code reasoning examples and various other deep reasoning scenarios, this dataset aims to imbue LLMs with a profound capacity for understanding, reasoning, and decision-making. ## Dataset Structure Each entry in the know-saraswati-cot dataset comprises an instruction and an output field. Same old stuff, i like this format. The instruction provides a scenario or question that requires deep thinking, inviting the model to engage in a step-by-step reasoning process. The output then captures a reasoned response that aligns with the principles of logical deduction and stream of consciousness thought. The know-saraswati-cot dataset has been meticulously crafted to reflect the intricacies of human-like reasoning. Here are some key specifications: - Concise Reasoning: The majority of examples are concisely formulated within 500 tokens, fostering quick and efficient chains of thought (CoT). This simulates the succinct yet profound reasoning processes akin to human cognition. - Multi-Turn Interactions: Some entries are designed as multi-turn interactions, allowing models to engage in a deeper and more dynamic discourse. This emulates real-world conversations where dialogues build upon previous exchanges. - Extended Discussions: A subset of the dataset accommodates scenarios extending up to 2000 tokens for comprehensive reasoning tasks. These are tailored to model how a sapient being would thoughtfully respond to complex logic puzzles, as opposed to the often superficial and tangential responses generated by less sophisticated models. - Each example is tailored to how an actual sapien would reason and respond, capturing the essence of human logic, emotion, and cognition. This approach aims to steer AI responses away from the undeveloped and extraneous (which usually llms respond with), guiding them towards relevance and depth that truly address the query at hand. ## Inspiration Inspired by the vision of making knowledge free and accessible for all, akin to the way Goddess Saraswati is revered for her gifts of learning and enlightenment, this dataset was synthesized using GPT-4. A special pranaam and blessings 🙏 from my brother, whose vision of a frugally enlightened world where knowledge is a common wealth has been the cornerstone of this endeavor. ## Use Cases The know-saraswati-cot dataset can be utilized to: 1. By providing rich, nuanced examples of logical reasoning, the dataset is perfect for developing models that can mimic the depth of human thought processes. 2. Researchers can leverage the dataset to investigate how AI models can not only reach conclusions but also articulate the reasoning behind their decisions, making AI workings more transparent. 3. know-saraswati-cot can foster AI development that intersects with philosophy, literature, and the Engineering, encouraging holistic and multidimensional growth in AI capabilities. 4. have fun
Linaqruf/pixiv-niji-journey
--- license: agpl-3.0 --- ## Description The Pixiv Niji Journey dataset is a collection of 9766 images with accompanying metadata, scraped from the online art platform Pixiv. The images were collected using the `gallery-dl` Python package, with the search term "nijijourney" on Pixiv. The collection period for the dataset was from November 6, 2022 to December 27, 2022. The dataset is divided into two variants: `raw` and `preprocessed`. The `raw` variant contains the pure dataset resulting from the scraping of Pixiv, while the `preprocessed` variant contains the same dataset but with additional preprocessing steps applied. These preprocessing steps include converting the images from RGB to RGBA, labeling the dataset with captions using the BLIP tool, and providing Danbooru tags using the wd-v1-4-vit-tagger tool. The `preprocessed` variant has also been carefully cleaned and filtered to remove any low quality or irrelevant images. The images in the dataset are in JPG and PNG format, and the metadata is provided in JSON format, while the preprocessed metadata is provided in `.txt` and `.caption` format. The metadata includes information about the images such as their captions, tags, and other metadata provided by Pixiv. The structure of the raw and preprocessed variants of the dataset is described in the `File Structure` section below. The Pixiv Niji Journey dataset is primarily intended for use in machine learning tasks related to image classification and caption generation. It can also be used as a dataset for image generation models such as stable diffusion. However, users should be aware that the dataset may contain biases or limitations, such as the bias of the Pixiv platform or the specific search term used to collect the data. ## File Structure The structure of the raw files is as follows: ``` nijijourney_pixiv_2022110620221222_raw.zip/ ├╴nijijourney/ │ ├╴images.png │ ├╴images.png.json │ └╴... ``` while the structure of the preprocessed files is: ``` nijijourney_pixiv_2022110620221222_preprocessed.zip/ ├╴dataset/ │ ├╴images.png │ ├╴images.png.json │ ├╴images.txt │ ├╴images.caption │ └╴... ├╴meta_cap.json ├╴meta_dd.json ├╴meta_clean.json ``` ## Usage - Access: the dataset is available for download from the Hugging Face dataset collection - Format: the dataset is provided in ZIP format, with images in PNG format and metadata in JSON format - Requirements: the dataset requires no specific requirements or dependencies for use ## Data Quality - Number of images: 9766 - Image sizes: vary, but all images are in PNG format - Class balance: the distribution of classes in the dataset is not known - Quality: the dataset has been carefully cleaned and filtered to remove low quality or irrelevant images ## Limitations While the Pixiv Niji Journey dataset has been carefully cleaned and preprocessed to ensure high quality and consistency, it is important to be aware of certain limitations and biases that may be present in the dataset. Some potential limitations of the dataset include: - Bias of the Pixiv platform: Pixiv is an online art platform that may have its own biases in terms of the content that is available and the users who contribute to it. This could potentially introduce biases into the dataset. - Search term bias: The dataset was collected using the search term "nijijourney" on Pixiv, which may have introduced biases into the dataset depending on the popularity and prevalence of this term on the platform. - Limited scope: The dataset only includes images scraped from Pixiv, and therefore may not be representative of a wider range of images or artistic styles. - Potential errors or inconsistencies in the metadata: While every effort has been made to ensure the accuracy of the metadata, there may be errors or inconsistencies present in the data. It is important to be aware of these limitations and to consider them when using the Pixiv Niji Journey dataset for research or other purposes. ## License The Pixiv Niji Journey dataset is made available under the terms of the AGPL-3.0 license. This license is a copyleft license that allows users to freely use, modify, and distribute the dataset, as long as any modified versions are also made available under the same terms. Under the terms of the AGPL-3.0 license, users are allowed to: - Use the dataset for any purpose, commercial or non-commercial - Modify the dataset as needed for their purposes - Distribute copies of the dataset, either modified or unmodified However, users must also follow the following conditions: - Any modified versions of the dataset must be made available under the same AGPL-3.0 license - If the dataset is used to provide a service to others (such as through a website or API), the source code for the service must be made available to users under the AGPL-3.0 license It is important to carefully review the terms of the AGPL-3.0 license and ensure that you understand your rights and obligations when using the Pixiv Niji Journey dataset. ## Citation If you use this dataset in your work, please cite it as follows: ``` @misc{pixiv_niji_journey, author = {Linaqruf}, title = {Pixiv Niji Journey}, year = {2022}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/Linaqruf/pixiv-niji-journey}, } ```
CVasNLPExperiments/VQAv2_sample_validation_google_flan_t5_xxl_mode_A_C_D_PNP_GENERIC_Q_rices_ns_1000
--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_ num_bytes: 142120 num_examples: 1000 download_size: 0 dataset_size: 142120 --- # Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_A_C_D_PNP_GENERIC_Q_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
angelolab/ark_example
--- annotations_creators: - no-annotation language: [] language_creators: [] license: - apache-2.0 multilinguality: [] pretty_name: An example dataset for analyzing multiplexed imaging data. size_categories: - n<1K source_datasets: - original tags: - MIBI - Multiplexed-Imaging task_categories: - image-segmentation task_ids: - instance-segmentation --- # Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@angelolab](https://github.com/angelolab) for adding this dataset.
UMCU/SNLI_Dutch_translated_with_Marianmt
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 67523459 num_examples: 538896 - name: test num_bytes: 1285789 num_examples: 9792 - name: validation num_bytes: 1295645 num_examples: 9792 download_size: 20806553 dataset_size: 70104893 license: cc-by-sa-4.0 language: - nl tags: - generic - sentence similarity pretty_name: Dutch translation of SNLI corpus with Maria NMT size_categories: - 100K<n<1M task_categories: - sentence-similarity --- Information on the dataset: ``` configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 67523459 num_examples: 538896 - name: test num_bytes: 1285789 num_examples: 9792 - name: validation num_bytes: 1295645 num_examples: 9792 download_size: 20806553 dataset_size: 70104893 ``` # Dataset Card for "SNLI_Dutch_translated_with_Marianmt" Translation of the **English** corpus [Stanford Natural Language Inference (SNLI)](https://nlp.stanford.edu/projects/snli/), to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl). Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461). A complete description of the dataset is given [here](https://huggingface.co/datasets/snli). # Attribution If you use this dataset please use the following to credit the creators of SNLI: ```citation @inproceedings{snli:emnlp2015, Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.}, Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, Publisher = {Association for Computational Linguistics}, Title = {A large annotated corpus for learning natural language inference}, Year = {2015} } ``` The creators of the OPUS-MT models: ``` @InProceedings{TiedemannThottingal:EAMT2020, author = {J{\"o}rg Tiedemann and Santhosh Thottingal}, title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld}, booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)}, year = {2020}, address = {Lisbon, Portugal} } ``` and ``` @misc {van_es_2023, author = { {Bram van Es} }, title = { SNLI_Dutch_translated_with_Marianmt (Revision 9ad7971) }, year = 2023, url = { https://huggingface.co/datasets/UMCU/SNLI_Dutch_translated_with_Marianmt }, doi = { 10.57967/hf/1268 }, publisher = { Hugging Face } } ``` # License For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP) we did **not** find a license, if this was in error please let us know and we will add the appropriate licensing promptly. We adopt the licensing of the SNLI corpus: a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
liuyanchen1015/MULTI_VALUE_stsb_it_is_non_referential
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 3684 num_examples: 18 - name: test num_bytes: 2896 num_examples: 22 - name: train num_bytes: 3750 num_examples: 26 download_size: 15904 dataset_size: 10330 --- # Dataset Card for "MULTI_VALUE_stsb_it_is_non_referential" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kosmikakapo/instance_halos_data
--- license: mit ---
ZahraAlharz/alriyadh
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: date dtype: string - name: title dtype: string - name: content dtype: string - name: url dtype: string splits: - name: train num_bytes: 7472 num_examples: 4 download_size: 21470 dataset_size: 7472 --- # Dataset Card for "alriyadh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
one-sec-cv12/chunk_34
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 12184361136.875 num_examples: 126857 download_size: 10752440618 dataset_size: 12184361136.875 --- # Dataset Card for "chunk_34" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BoBlo05/Lyrics
--- license: mit ---
andersonbcdefg/wikipedia-triples-filtered
--- dataset_info: features: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string - name: pos_score dtype: float64 - name: neg_score dtype: float64 - name: margin dtype: float64 splits: - name: train num_bytes: 241659633.01285926 num_examples: 554309 download_size: 156059993 dataset_size: 241659633.01285926 configs: - config_name: default data_files: - split: train path: data/train-* ---
Riverofjunk/jiraticketcreator
--- license: openrail ---
Maxscha/commitbench
--- license: cc-by-nc-4.0 language: - en tags: - code size_categories: - 1M<n<10M --- # CommitBench: A Benchmark for Commit Message Generation ## EXECUTIVE SUMMARY We provide CommitBench as an open-source, reproducible and privacy- and license-aware benchmark for commit message generation. The dataset is gathered from GitHub repositories with licenses that permit redistribution. We provide six programming languages, Java, Python, Go, JavaScript, PHP, and Ruby. The commit messages in natural language are restricted to English, as it is the working language in many software development projects. The dataset has 1,664,590 examples that were generated by using extensive quality-focused filtering techniques (e.g., excluding bot commits). Additionally, we provide a version with longer sequences for benchmarking models with more extended sequence input. ## CURATION RATIONALE We created this dataset due to quality and legal issues with previous commit message generation datasets. Given a git diff displaying code changes between two file versions, the task is to predict the accompanying commit message describing these changes in natural language. We base our GitHub repository selection on that of a previous dataset, CodeSearchNet, but apply a large number of filtering techniques to improve the data quality and eliminate noise. Due to the original repository selection, we are also restricted to the aforementioned programming languages. It was important to us, however, to provide some number of programming languages to accommodate any changes in the task due to the degree of hardware-relatedness of a language. The dataset is provided as a large CSV file containing all samples. We provide the following fields: Diff, Commit Message, Hash, Project, Split. ## DOCUMENTATION FOR SOURCE DATASETS Repository selection based on CodeSearchNet, which can be found under [https://github.com/github/CodeSearchNet](https://github.com/github/CodeSearchNet). ## LANGUAGE VARIETIES Since GitHub hosts software projects from all over the world, there is no single uniform variety of English used across all commit messages. This means that phrasing can be regional or subject to influences from the programmer's native language. It also means that different spelling conventions may co-exist and that different terms may be used for the same concept. Any model trained on this data should take these factors into account. ### Overview of split by programming language for CommitBench: - Java: 153,119 - Ruby: 233,710 - Go: 137,998 - JavaScript: 373,598 - Python: 472,469 - PHP: 294,394 ## SPEAKER DEMOGRAPHIC Due to the extremely diverse (geographically, but also socio-economically) backgrounds of the software development community, there is no single demographic the data comes from. Globally, the average software developer tends to be male and has obtained higher education. Due to the anonymous nature of GitHub profiles, gender distribution information cannot be extracted. ## ANNOTATOR DEMOGRAPHIC Due to the automated generation of the dataset, no annotators were used. ## SPEECH SITUATION AND CHARACTERISTICS The public nature and often business-related creation of the data by the original GitHub users fosters a more neutral, information-focused, and formal language. As it is not uncommon for developers to find the writing of commit messages tedious, there can also be commit messages representing the frustration or boredom of the commit author. While our filtering is supposed to catch these types of messages, there can be some instances still in the dataset. ## PREPROCESSING AND DATA FORMATTING See our paper for all preprocessing steps. We do not provide the un-processed raw data due to privacy concerns, but it can be obtained via CodeSearchNet or requested from the authors. ## CAPTURE QUALITY While our dataset is completely reproducible at the time of writing, there are external dependencies that could restrict this. If GitHub shuts down and someone with a software project in the dataset deletes their repository, there can be instances that are non-reproducible. ## LIMITATIONS While our filters are meant to ensure a high quality for each data sample in the dataset, we cannot ensure that only low-quality examples were removed. Similarly, we cannot guarantee that our extensive filtering methods catch all low-quality examples. Some might remain in the dataset. Another limitation of our dataset is the low number of programming languages (there are many more) as well as our focus on English commit messages. ## METADATA - **License:** Dataset under the CC BY-NC 4.0 license, code under the MIT license ## DISCLOSURES AND ETHICAL REVIEW While we put substantial effort into removing privacy-sensitive information, our solutions cannot find 100% of such cases. This means that researchers and anyone using the data need to incorporate their own safeguards to effectively reduce the amount of personal information that can be exposed. ## ABOUT THIS DOCUMENT A data statement is a characterization of a dataset that provides context to allow developers and users to better understand how experimental results might generalize, how software might be appropriately deployed, and what biases might be reflected in systems built on the software. This data statement was written based on the template for the Data Statements Version 2 schema. The template was prepared by Angelina McMillan-Major, Emily M. Bender, and Batya Friedman and can be found at [https://techpolicylab.uw.edu/data-statements/](https://techpolicylab.uw.edu/data-statements/) and was updated from the community Version 1 Markdown template by Leon Derczynski.
rai-sandeep/dataset_full_v1
--- dataset_info: features: - name: task dtype: string - name: format dtype: string splits: - name: train num_bytes: 7561 num_examples: 10 download_size: 9203 dataset_size: 7561 --- # Dataset Card for "dataset_full_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NathanGavenski/MountainCar-v0
--- license: mit tags: - Imitation Learning - Expert Trajectory pretty_name: MountainCar-v0 Expert Dataset size_categories: - 10M<n<100M --- # MountainCar-v0 - Imitation Learning Datasets This is a dataset created by [Imitation Learning Datasets](https://github.com/NathanGavenski/IL-Datasets) project. It was created by using Stable Baselines weights from a DQN policy from [HuggingFace](https://huggingface.co/sb3/dqn-MountainCar-v0). ## Description The dataset consists of 1,000 episodes with an average episodic reward of `-98.817`. Each entry consists of: ``` obs (list): observation with length 2. action (int): action (0 or 1). reward (float): reward point for that timestep. episode_returns (bool): if that state was the initial timestep for an episode. ``` ## Usage Feel free to download and use the `teacher.jsonl` dataset as you please. If you are interested in using our PyTorch Dataset implementation, feel free to check the [IL Datasets](https://github.com/NathanGavenski/IL-Datasets/blob/main/src/imitation_datasets/dataset/dataset.py) project. There, we implement a base Dataset that downloads this dataset and all other datasets directly from HuggingFace. The Baseline Dataset also allows for more control over train and test splits and how many episodes you want to use (in cases where the 1k episodes are not necessary). ## Citation Coming soon.
Doub7e/SDv2-5k-SpatialFiltered
--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: T5_last_hidden_states sequence: sequence: sequence: float32 splits: - name: train num_bytes: 6401922968.375 num_examples: 5005 download_size: 6392275416 dataset_size: 6401922968.375 configs: - config_name: default data_files: - split: train path: data/train-* ---
vllg/long_c4
--- license: odc-by task_categories: - text-generation - fill-mask language: - en size_categories: - 10M<n<100M --- A filtered subset of C4-en containing 13,688,429 pages that are at least 8,000 characters long, useful for training models with longer context windows.
scene-genie/instagram-dataset-big
--- dataset_info: features: - name: image dtype: image - name: user dtype: string - name: bounding_box struct: - name: image struct: - name: height dtype: int64 - name: width dtype: int64 - name: predictions list: - name: class dtype: string - name: class_id dtype: int64 - name: confidence dtype: float64 - name: detection_id dtype: string - name: height dtype: float64 - name: width dtype: float64 - name: x dtype: float64 - name: y dtype: float64 - name: processed dtype: bool - name: time dtype: float64 - name: langsam struct: - name: boxes sequence: sequence: float64 - name: logits sequence: float64 - name: masks sequence: sequence: sequence: bool - name: phrases sequence: string - name: processed dtype: bool - name: caption dtype: string splits: - name: train num_bytes: 5464737330.25 num_examples: 41775 download_size: 5414930625 dataset_size: 5464737330.25 configs: - config_name: default data_files: - split: train path: data/train-* ---
kristmh/high_vs_random
--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validate path: data/validate-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text_clean dtype: string - name: labels dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 35090618 num_examples: 42053 - name: train num_bytes: 284101969 num_examples: 336420 - name: validate num_bytes: 36222999 num_examples: 42052 download_size: 175883924 dataset_size: 355415586 --- # Dataset Card for "high_vs_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nexdata/Japanese_Speech_Data
--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Japanese_Speech_Datae ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/934?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary 1006 Japanese native speakers participated in the recording, coming from eastern, western, and Kyushu regions, while the eastern region accounting for the largest proportion. The recording content is rich and all texts have been manually transferred with high accuracy. For more details, please refer to the link: https://www.nexdata.ai/datasets/934?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Japanese ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
srikanthmalipatel/semantic_search
--- license: mit ---
ankit-vaidya19/SemEval-24
--- license: apache-2.0 ---
WildVision/wildvision-bench
--- dataset_info: - config_name: battle features: - name: question_id dtype: string - name: model_a dtype: string - name: model_b dtype: string - name: conversation_a list: - name: role dtype: string - name: content dtype: string - name: conversation_b list: - name: role dtype: string - name: content dtype: string - name: language dtype: string - name: image dtype: image - name: turn dtype: int32 - name: anony dtype: bool - name: winner dtype: string - name: tstamp dtype: int32 - name: judge dtype: string splits: - name: test num_bytes: 5931226113.077 num_examples: 2013 download_size: 3653220307 dataset_size: 5931226113.077 - config_name: chat features: - name: question_id dtype: string - name: model dtype: string - name: conversation list: - name: role dtype: string - name: content dtype: string - name: language dtype: string - name: image dtype: image - name: turn dtype: int32 splits: - name: test num_bytes: 24534213793.288 num_examples: 11122 download_size: 11214621001 dataset_size: 24534213793.288 - config_name: chat_and_battle_image features: - name: question_id dtype: string - name: model dtype: string - name: conversation list: - name: role dtype: string - name: content dtype: string - name: language dtype: string - name: image dtype: image - name: turn dtype: int32 - name: source dtype: string splits: - name: train num_bytes: 10500475382.445 num_examples: 3977 download_size: 7732811345 dataset_size: 10500475382.445 - config_name: keep_bad_only features: - name: question_id dtype: string - name: model dtype: string - name: conversation list: - name: role dtype: string - name: content dtype: string - name: language dtype: string - name: image dtype: image - name: turn dtype: int32 splits: - name: test num_bytes: 4760442474.92 num_examples: 1654 download_size: 3093490423 dataset_size: 4760442474.92 - config_name: release_100_as_bench features: - name: question_id dtype: string - name: model dtype: string - name: conversation list: - name: role dtype: string - name: content dtype: string - name: language dtype: string - name: image dtype: image - name: turn dtype: int32 splits: - name: test num_bytes: 306531348.0 num_examples: 144 - name: val num_bytes: 75199805.0 num_examples: 52 download_size: 492304000 dataset_size: 381731153.0 - config_name: release_100_as_bench_battle features: - name: question_id dtype: string - name: model_a dtype: string - name: model_b dtype: string - name: conversation_a list: - name: role dtype: string - name: content dtype: string - name: conversation_b list: - name: role dtype: string - name: content dtype: string - name: language dtype: string - name: image dtype: image - name: turn dtype: int32 - name: anony dtype: bool - name: winner dtype: string - name: tstamp dtype: int32 - name: judge dtype: string splits: - name: precompute_gpt4v_vote num_bytes: 8584763789.0 num_examples: 4032 - name: woprecompute_user_vote num_bytes: 168025531.0 num_examples: 73 - name: precompute_evaluator_vote num_bytes: 8584863881.0 num_examples: 4032 download_size: 906902218 dataset_size: 17337653201.0 configs: - config_name: battle data_files: - split: test path: battle/test-* - config_name: chat data_files: - split: test path: chat/test-* - config_name: chat_and_battle_image data_files: - split: train path: chat_and_battle_image/train-* - config_name: keep_bad_only data_files: - split: test path: keep_bad_only/test-* - config_name: release_100_as_bench data_files: - split: test path: release_100_as_bench/test-* - split: val path: release_100_as_bench/val-* - config_name: release_100_as_bench_battle data_files: - split: precompute_gpt4v_vote path: release_100_as_bench_battle/precompute_gpt4v_vote-* - split: woprecompute_user_vote path: release_100_as_bench_battle/woprecompute_user_vote-* - split: precompute_evaluator_vote path: release_100_as_bench_battle/precompute_evaluator_vote-* ---
open-llm-leaderboard/details_Gille__StrangeMerges_8-7B-slerp
--- pretty_name: Evaluation run of Gille/StrangeMerges_8-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Gille/StrangeMerges_8-7B-slerp](https://huggingface.co/Gille/StrangeMerges_8-7B-slerp)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Gille__StrangeMerges_8-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-02T23:58:25.583847](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_8-7B-slerp/blob/main/results_2024-04-02T23-58-25.583847.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6573575652247148,\n\ \ \"acc_stderr\": 0.03198110626672295,\n \"acc_norm\": 0.6574277908836418,\n\ \ \"acc_norm_stderr\": 0.03264520527351041,\n \"mc1\": 0.4847001223990208,\n\ \ \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.6452464384173413,\n\ \ \"mc2_stderr\": 0.015330705882406925\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6808873720136519,\n \"acc_stderr\": 0.01362169611917331,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.013250012579393441\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.700955984863573,\n\ \ \"acc_stderr\": 0.0045690346133326004,\n \"acc_norm\": 0.8775144393547102,\n\ \ \"acc_norm_stderr\": 0.0032717574453291656\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\ \ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\ \ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\ \ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924,\n \"acc_norm\"\ : 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n \ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \"\ acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n\ \ \"acc_stderr\": 0.023157879349083522,\n \"acc_norm\": 0.7903225806451613,\n\ \ \"acc_norm_stderr\": 0.023157879349083522\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\ \ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.02805779167298902,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.02805779167298902\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033484,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033484\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.024035489676335082,\n \ \ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.024035489676335082\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857416,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857416\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.02995382389188703,\n \ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.02995382389188703\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\ acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\ acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156861,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156861\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\ \ \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n\ \ \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n\ \ \"acc_stderr\": 0.013468201614066302,\n \"acc_norm\": 0.8288633461047255,\n\ \ \"acc_norm_stderr\": 0.013468201614066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.44692737430167595,\n\ \ \"acc_stderr\": 0.016628030039647614,\n \"acc_norm\": 0.44692737430167595,\n\ \ \"acc_norm_stderr\": 0.016628030039647614\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.02567025924218893,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.02567025924218893\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7561728395061729,\n \"acc_stderr\": 0.023891879541959614,\n\ \ \"acc_norm\": 0.7561728395061729,\n \"acc_norm_stderr\": 0.023891879541959614\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.470013037809648,\n\ \ \"acc_stderr\": 0.012747248967079065,\n \"acc_norm\": 0.470013037809648,\n\ \ \"acc_norm_stderr\": 0.012747248967079065\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6944444444444444,\n \"acc_stderr\": 0.018635594034423972,\n \ \ \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.018635594034423972\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4847001223990208,\n\ \ \"mc1_stderr\": 0.017495304473187902,\n \"mc2\": 0.6452464384173413,\n\ \ \"mc2_stderr\": 0.015330705882406925\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8445146014206788,\n \"acc_stderr\": 0.010184308214775777\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6724791508718726,\n \ \ \"acc_stderr\": 0.012927102210426719\n }\n}\n```" repo_url: https://huggingface.co/Gille/StrangeMerges_8-7B-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|arc:challenge|25_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-02T23-58-25.583847.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|gsm8k|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hellaswag|10_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-58-25.583847.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-management|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-58-25.583847.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|truthfulqa:mc|0_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-02T23-58-25.583847.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_02T23_58_25.583847 path: - '**/details_harness|winogrande|5_2024-04-02T23-58-25.583847.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-02T23-58-25.583847.parquet' - config_name: results data_files: - split: 2024_04_02T23_58_25.583847 path: - results_2024-04-02T23-58-25.583847.parquet - split: latest path: - results_2024-04-02T23-58-25.583847.parquet --- # Dataset Card for Evaluation run of Gille/StrangeMerges_8-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Gille/StrangeMerges_8-7B-slerp](https://huggingface.co/Gille/StrangeMerges_8-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Gille__StrangeMerges_8-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-02T23:58:25.583847](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_8-7B-slerp/blob/main/results_2024-04-02T23-58-25.583847.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6573575652247148, "acc_stderr": 0.03198110626672295, "acc_norm": 0.6574277908836418, "acc_norm_stderr": 0.03264520527351041, "mc1": 0.4847001223990208, "mc1_stderr": 0.017495304473187902, "mc2": 0.6452464384173413, "mc2_stderr": 0.015330705882406925 }, "harness|arc:challenge|25": { "acc": 0.6808873720136519, "acc_stderr": 0.01362169611917331, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.013250012579393441 }, "harness|hellaswag|10": { "acc": 0.700955984863573, "acc_stderr": 0.0045690346133326004, "acc_norm": 0.8775144393547102, "acc_norm_stderr": 0.0032717574453291656 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595853, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595853 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6867924528301886, "acc_stderr": 0.028544793319055326, "acc_norm": 0.6867924528301886, "acc_norm_stderr": 0.028544793319055326 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932262, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932262 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086924, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086924 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083522, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083522 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5024630541871922, "acc_stderr": 0.035179450386910616, "acc_norm": 0.5024630541871922, "acc_norm_stderr": 0.035179450386910616 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.032568666616811015, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.032568666616811015 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8080808080808081, "acc_stderr": 0.02805779167298902, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.02805779167298902 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033484, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033484 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.658974358974359, "acc_stderr": 0.024035489676335082, "acc_norm": 0.658974358974359, "acc_norm_stderr": 0.024035489676335082 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857416, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857416 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6932773109243697, "acc_stderr": 0.02995382389188703, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.02995382389188703 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8458715596330275, "acc_stderr": 0.015480826865374303, "acc_norm": 0.8458715596330275, "acc_norm_stderr": 0.015480826865374303 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8578431372549019, "acc_stderr": 0.02450980392156861, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.02450980392156861 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7040358744394619, "acc_stderr": 0.030636591348699803, "acc_norm": 0.7040358744394619, "acc_norm_stderr": 0.030636591348699803 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.035477710041594654, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.035477710041594654 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4642857142857143, "acc_stderr": 0.04733667890053756, "acc_norm": 0.4642857142857143, "acc_norm_stderr": 0.04733667890053756 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406974, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406974 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8288633461047255, "acc_stderr": 0.013468201614066302, "acc_norm": 0.8288633461047255, "acc_norm_stderr": 0.013468201614066302 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7485549132947977, "acc_stderr": 0.02335736578587403, "acc_norm": 0.7485549132947977, "acc_norm_stderr": 0.02335736578587403 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.44692737430167595, "acc_stderr": 0.016628030039647614, "acc_norm": 0.44692737430167595, "acc_norm_stderr": 0.016628030039647614 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.0256468630971379, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.0256468630971379 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.02567025924218893, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.02567025924218893 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7561728395061729, "acc_stderr": 0.023891879541959614, "acc_norm": 0.7561728395061729, "acc_norm_stderr": 0.023891879541959614 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.470013037809648, "acc_stderr": 0.012747248967079065, "acc_norm": 0.470013037809648, "acc_norm_stderr": 0.012747248967079065 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.0286619962023353, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.0286619962023353 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6944444444444444, "acc_stderr": 0.018635594034423972, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.018635594034423972 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.028263889943784603, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784603 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.4847001223990208, "mc1_stderr": 0.017495304473187902, "mc2": 0.6452464384173413, "mc2_stderr": 0.015330705882406925 }, "harness|winogrande|5": { "acc": 0.8445146014206788, "acc_stderr": 0.010184308214775777 }, "harness|gsm8k|5": { "acc": 0.6724791508718726, "acc_stderr": 0.012927102210426719 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
HuggingFaceH4/lima_llama2
--- dataset_info: features: - name: conversations sequence: string - name: source dtype: string - name: length dtype: int64 - name: prompt_id dtype: string - name: prompt dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: meta struct: - name: category dtype: string - name: source dtype: string - name: text dtype: string splits: - name: train num_bytes: 8806712 num_examples: 1000 - name: test num_bytes: 188848 num_examples: 300 download_size: 5237615 dataset_size: 8995560 --- # Dataset Card for "lima_llama2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
caro-holt/MultiQ
--- license: cc-by-4.0 language: - tl - sm - mk - gu - fi - mn - bm - ta - ur - hy - nl - tk - en - bg - gd - pt - ko - ga - eu - sv - bs - co - fr - gn - ro - it - dv - ku - ak - eo - zu - id - te - sl - lv - pa - ru - si - ee - yi - ny - az - sw - hi - mt - sr - hr - ka - ug - tt - lg - kn - fy - kk - ca - lb - jv - et - la - tr - ps - km - zh - uk - as - he - yo - sq - da - gl - vi - ay - is - ln - mr - st - xh - cs - ky - ml - ht - mi - so - uz - el - ti - be - cy - am - ig - or - fa - ms - su - de - lo - ha - ts - om - ar - my - es - qu - 'no' - th - sa - mg - pl - sd - sk - bn - rw - af - ne - lt - tg - ja - sn - hu size_categories: - 10K<n<100K task_categories: - question-answering --- # Dataset Card for MultiQ This is the dataset corresponding to the paper "Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ". It is a silver standard benchmark that can be used to evaluate the basic multilingual capabilities of LLMs. It contains 200 open ended questions automatically translated into 137 typologically diverse languages. - **Curated by:** Carolin Holtermann, Paul Röttger, Timm Dill, Anne Lauscher - **Language(s) (NLP):** 137 diverse languages described in detail in our paper - **License:** CC-BY-4.0 License ### Dataset Sources - **Repository:** [Github](https://github.com/paul-rottger/multiq) - **Paper:** TBD
Andron00e/Places365-custom-train
--- dataset_info: features: - name: image_file_path dtype: string - name: image dtype: image - name: labels dtype: int64 splits: - name: train num_bytes: 157401732953.44 num_examples: 1803460 download_size: 111261619378 dataset_size: 157401732953.44 configs: - config_name: default data_files: - split: train path: data/train-* ---
neil-code/dialogsum-test
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - summarization - text2text-generation - text-generation task_ids: [] pretty_name: DIALOGSum Corpus --- # Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links - **Homepage:** https://aclanthology.org/2021.findings-acl.449 - **Repository:** https://github.com/cylnlp/dialogsum - **Paper:** https://aclanthology.org/2021.findings-acl.449 - **Point of Contact:** https://huggingface.co/knkarthick ### Dataset Summary DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics. ### Languages English ## Dataset Structure ### Data Instances DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation. The first instance in the training set: {'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up} ### Data Fields - dialogue: text of dialogue. - summary: human written summary of the dialogue. - topic: human written topic/one liner of the dialogue. - id: unique file id of an example. ### Data Splits - train: 12460 - val: 500 - test: 1500 - holdout: 100 [Only 3 features: id, dialogue, topic] ## Dataset Creation ### Curation Rationale In paper: We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers. Compared with previous datasets, dialogues from DialogSum have distinct characteristics: Under rich real-life scenarios, including more diverse task-oriented scenarios; Have clear communication patterns and intents, which is valuable to serve as summarization sources; Have a reasonable length, which comforts the purpose of automatic summarization. We ask annotators to summarize each dialogue based on the following criteria: Convey the most salient information; Be brief; Preserve important named entities within the conversation; Be written from an observer perspective; Be written in formal language. ### Who are the source language producers? linguists ### Who are the annotators? language experts ## Licensing Information MIT License ## Citation Information ``` @inproceedings{chen-etal-2021-dialogsum, title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset", author = "Chen, Yulong and Liu, Yang and Chen, Liang and Zhang, Yue", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.449", doi = "10.18653/v1/2021.findings-acl.449", pages = "5062--5074", ``` ## Contributions Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.
ashwathjadhav23/spenish_dataset
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 3948364 num_examples: 25295 download_size: 2458416 dataset_size: 3948364 --- # Dataset Card for "spenish_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
xwjiang2010/pile_dedupe_train_tokenized_chunked
--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 32776 num_examples: 2 download_size: 17726 dataset_size: 32776 configs: - config_name: default data_files: - split: train path: data/train-* ---
HydraLM/SkunkData-002-2
--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string - name: embedding sequence: float64 - name: cluster dtype: int32 splits: - name: train num_bytes: 14849700907 num_examples: 1472917 download_size: 11160683261 dataset_size: 14849700907 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SkunkData-002-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mrpc_do_tense_marker
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 366782 num_examples: 1343 - name: train num_bytes: 760839 num_examples: 2765 - name: validation num_bytes: 85429 num_examples: 307 download_size: 801970 dataset_size: 1213050 --- # Dataset Card for "MULTI_VALUE_mrpc_do_tense_marker" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HektorOrion/adv-ele
--- dataset_info: features: - name: ADV dtype: string - name: ELE dtype: string splits: - name: train num_bytes: 430918.56140350876 num_examples: 1732 - name: test num_bytes: 107978.43859649122 num_examples: 434 download_size: 298662 dataset_size: 538897.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
leolin94/adlhw1
--- task_categories: - paragraph-selection/span-selection language: - en ---
Adapting/abstract-keyphrases
--- license: mit dataset_info: features: - name: Abstract dtype: string - name: Keywords dtype: string splits: - name: train num_bytes: 65697.22222222222 num_examples: 50 - name: validation num_bytes: 26278.88888888889 num_examples: 20 - name: test num_bytes: 26278.88888888889 num_examples: 20 download_size: 93062 dataset_size: 118255.0 --- preprocessing: https://colab.research.google.com/drive/1dbiApU33FBwAfxwlGBK00qAkbUsS9iae?usp=sharing
Akstasy/ds_cb_test_3
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: labels sequence: class_label: names: '0': landscape services '1': eau '2': bébé '3': diy diagnostics solutions '4': itinérance '5': cupcakes '6': aluminum products '7': pricing intelligence '8': corporate services '9': predictive maintenance for railways '10': web development '11': relations publiques '12': satellite charging '13': '2014' '14': bspce '15': white label platform '16': college it '17': sms-based '18': agrégation '19': series '20': chat bots '21': 1 sw '22': managed services '23': medical specialists '24': contrôle de gestion '25': freelance '26': integrated grc '27': portabilité '28': maintenance ferroviaire '29': financing '30': psychology '31': enablers '32': veille '33': manufacturing operations management '34': carnet de santé '35': alternative '36': performance coaching '37': non-profit & philanthropy '38': tel aviv university '39': grenoble '40': electrical supplies '41': gift exchange '42': white label mobile app '43': optical goods '44': inspection '45': application performance monitoring '46': cultural general '47': cross border commerce '48': system software '49': sound libraries '50': orthodontics '51': economie collaborative '52': swimming pools '53': financial markets '54': circuit boards '55': embedded ai '56': crypto crowdfunding '57': translation service '58': artificial intelligence, computer science '59': interior decor '60': aerial imagery '61': corporate training '62': test based '63': devis '64': orientation '65': b2c marketing '66': reverse logistics '67': soap '68': adwords '69': insolite '70': white label '71': mobile accessories '72': guerre électronique '73': ai generated music '74': tools for fitness professionals '75': marketing & sales sw '76': trade documentation '77': retirement communities '78': devices '79': semi-closed '80': autonomous store platform '81': développement '82': multi currency '83': fog computing platforms '84': robotique '85': retail general '86': medecine esthétique '87': post analytical '88': organic food retail '89': survey '90': electronique '91': gel '92': social entrepreneurship '93': luxury fashion consignment '94': customer success '95': power distribution '96': etranger '97': business partnerships '98': veille thématique '99': healthplatform '100': emprunteur '101': '@distribution' '102': recyclage '103': sporttech '104': revrec '105': administrator '106': morgan stanley '107': tires & rubber '108': genome informatics '109': ad fraud detection '110': cnc machinery '111': achat '112': pizza '113': ar-based '114': timeshare '115': mentorship programs '116': payment service providers '117': audiologists '118': continuing education '119': brick '120': '@voyagesurmesure' '121': international business '122': game platforms '123': b2b saas '124': higher education tech '125': power lines '126': digital twin platforms '127': diversité '128': farms '129': escrow services '130': entrepreneuriat '131': hr management '132': digital transaction management '133': fund performance data '134': transaction processing '135': fabriqueenfrance '136': news portals '137': moving '138': neobanks '139': skin care '140': product recommendations '141': thermal cycler '142': payment '143': responsable '144': diet '145': content recommendation software '146': live entertainment '147': greentech '148': raf augmenté '149': garantie panne mécanique '150': shuttles '151': motion capture '152': serverless computing '153': hrms for smbs '154': online filing '155': gestion des installations éco énergétiques '156': protection '157': multiple cns diseases '158': prestation intellectuelle '159': upcycling '160': industrial goods '161': devices and platform '162': livraison à domicile '163': ondemand '164': modular platform '165': social media management '166': carrier booking '167': contech '168': offrir du vin '169': app mobile '170': innovation collaborative '171': self-serve '172': multi access edge computing '173': oss & bss '174': algorithms '175': oilfield management '176': staffing agency '177': fuel '178': matériaux durables '179': formations en ligne '180': personal safety '181': traduction scientifique '182': test preparation '183': biopharma '184': futureofwo '185': abrasives '186': mechanical engineering '187': '@recettes' '188': idee '189': digitalwallet '190': operations '191': agribusiness '192': gestion de crise '193': homesolar '194': ppm '195': patrol services '196': informationsharing '197': social bookmarking '198': supercenters '199': password manager '200': outpatient '201': iit kharagpur '202': laundry & dry cleaning services '203': video aggeregation '204': automobile dealers '205': movie '206': enterprise solutions '207': saas '208': vacations '209': chewing tobacco '210': '@transitionécologique @innovation @mobilitédurable' '211': seconde main '212': personalization '213': facilities support services '214': '@affiche' '215': identity access management '216': government '217': pratique '218': drones based '219': entraite '220': supply chain '221': travaux '222': marketing adminstrations tools (budget mgt & roi) '223': rapide '224': compléments alimentaires '225': marketing and advertising '226': research '227': programming software '228': cookie '229': beams '230': intelligence relationnelle '231': faith-based '232': concierge '233': water and wastewater management tech '234': paper manufacturing '235': '@edtech' '236': contenu '237': expérience interactive '238': technion israel institute of technology '239': software for mtos '240': infrared array sensing '241': formation en ligne '242': crossmarket '243': it '244': vet appointment booking '245': openbanking '246': internet-first media '247': b2b marketplace '248': vrac '249': verification '250': pentest '251': own delivery fleet '252': providers '253': stades '254': vertical payments '255': it security '256': smartwatch '257': pre assessed candidates '258': digital publishing '259': zoos & national parks '260': technology providers '261': livres pour enfant '262': b2b merchants '263': debt management '264': distribution '265': impôt '266': tutor discovery '267': gratuit '268': presse '269': creative management platform '270': personalised '271': convenience stores '272': trading & investments '273': pet products '274': automation '275': pipe manufacturing '276': seller bots '277': '@self-coaching' '278': teambuiding '279': recycling '280': travel & accommodation '281': jlabs portfolio '282': '@affairespubliques' '283': virtual currency '284': nurse '285': cds '286': accountingsoftware '287': empowerment '288': event vendors '289': supply chain planning '290': snack food '291': serviceàdomicile '292': education / edtech '293': brandy '294': rewards '295': numérique '296': power generation '297': virtual desktop '298': banking apis '299': consulting '300': carbonated waters '301': tests '302': retail & marketplace lending '303': forwarding '304': business payments '305': matières premières '306': market linkages '307': events and festivals '308': cell therapy & regenerative medicine '309': b2cfinances '310': finance & accounting '311': tools & home improvement '312': gestion commerciale '313': sleep medicine '314': nfc '315': the upper key '316': app marketing '317': logistique 3.0 '318': cabinet de recrutement '319': enseignement '320': madeinfrance '321': d'affaires '322': multi-disciplinary '323': renewables & environment '324': lagos '325': application ecommerce '326': stock exchanges '327': textiles & apparel '328': web components '329': mro and industrial tools '330': financial data & research '331': customer engagement '332': face recognition '333': fast '334': incentive '335': medical conditions '336': trade schools '337': '@emprunteur' '338': financial service providers '339': fine art '340': enterprise apps '341': haccp '342': start up '343': time '344': b2bplatform '345': categories '346': référencement naturel immobilier '347': web cms '348': charging '349': dwellings '350': trading and settlement platforms '351': protection juridique '352': wallets '353': e-marketing '354': web3 '355': logistic '356': agence webflow '357': personal finance '358': fun & love '359': sound '360': fabrication additive '361': mobileservices '362': visite virtuelle '363': insurtech '364': timber '365': event '366': web&mobilesolutions '367': international affairs '368': symptom checkers '369': emergency medical transportation & services '370': datacenter services '371': employee benefits management '372': referencement naturel avocat '373': formulaire '374': silver economy '375': neurological disorders '376': iot security '377': personal computers & peripherals '378': service pour les entreprises de transport '379': sportapp '380': ingénierie mécanique des matériaux '381': term loans '382': perspective 3d '383': secure search engine '384': voiture électrique '385': scraping '386': edtech '387': travelapp '388': writers '389': referral and advocacy marketing '390': treasury '391': kitchenware '392': port management '393': unsecured personal loans '394': university management system '395': vms '396': jam '397': employeesmanagement '398': field force management '399': computing infrastructure '400': recharge '401': energyaccessmanagement '402': construction general '403': drugdistribution '404': energy analysis '405': risque '406': vidéos 360° '407': silver economie '408': space '409': residence '410': listing platform '411': google ads '412': low & no code '413': in-store '414': video ugc '415': under cover '416': mode durable '417': care team communications '418': parking solution '419': direct insurance '420': discovery and advice platforms '421': software development & design '422': financial sector '423': internet first property rental agency '424': translation & linguistic services '425': biopharmaceuticals '426': mineral wool '427': google pay '428': financial vehicles '429': energymanagement '430': food, beverages & tobacco '431': for organisations '432': natural gas '433': commercial printing '434': online patient communities '435': value based care '436': cardiac and vascular disorders '437': musique '438': personalized service '439': financial reports & filings data '440': virtualbank '441': gaspillage alimentaire '442': automobile parts stores '443': staffing solutions, staffing & recruiting '444': transcriptomics '445': mums '446': publisher solutions '447': création de logo '448': online video '449': inventory management '450': operational analytics '451': ar '452': service & maintenance operations management '453': appraisers '454': r&d '455': integration services '456': customer service software '457': micro lending '458': metabolomics '459': réduire les émissions de co2 '460': field management '461': '@gestiondecrise' '462': pertedepoids '463': automaker '464': product insights '465': animation '466': telemedicine solutions '467': vineyard '468': professionals '469': wind '470': clinical analytics '471': telecommunications general '472': logistics '473': employer organizations '474': fashion '475': pre-assessed candidates '476': nettoyant '477': créativité '478': intérieur '479': equestrian '480': reparation macbook '481': building analytics '482': international relations '483': fan '484': issuer '485': animal food '486': fuel cell '487': web scraping software '488': literie '489': autocar '490': database as a service (dbaas) '491': sols '492': reconnaissance visuelle '493': future of work '494': health care providers & services '495': image de marque '496': cyber security '497': '@evasion' '498': lampe '499': precision engineering '500': compliance and fraud detection '501': nuclear '502': optimisation '503': sante hygiène '504': agoranov portfolio '505': resin '506': needlework '507': health '508': upper key '509': intracity '510': home furniture '511': computer-aided engineering '512': windows '513': gig economy '514': metals & minerals '515': ai in energy '516': gestion '517': captable '518': auction '519': recruitment services '520': others '521': structural biology '522': healthinsurance '523': oil & gas exploration & services '524': méthode agile sourcing design '525': biopharma (by therapy) '526': photo '527': port '528': stores '529': monitoring systems '530': surgery '531': mobile devtools '532': music '533': telehealth '534': garages '535': paper machinery '536': animaux '537': brokerage '538': thermostat '539': bi & analytics (incl. search '540': biomass energy '541': speaker '542': enterprise collaboration '543': cif '544': application monitoring '545': volumetric 3d '546': navette '547': software enablers '548': instrument '549': program & portfolio management '550': neuroscience '551': réseaux sociaux '552': camper '553': playable video ads '554': 3d printing in construction '555': products '556': payment links '557': nids '558': data lineage '559': technology '560': artificial intelligence '561': ai based personalized stylist '562': bakery '563': financial advisers '564': learning by doing '565': campgrounds '566': end of life care '567': electronic parts '568': insurance '569': ai in food '570': apparel '571': anti fraud management '572': calcul '573': delivery service '574': storage tanks '575': internet '576': softskills '577': beauty & personal care '578': relations médias '579': fitness training '580': personal loans '581': beauty & personal care products '582': fats '583': transparency '584': predictive '585': cooperatives '586': oilfield marketplace '587': metal coating '588': réemploi '589': scheduled delivery '590': hybridization-based analysis '591': civic clubs '592': signature '593': physical security '594': digital learning '595': monitoring '596': chatgpt '597': re-seller '598': farm data and analytics '599': batch 6 '600': govtech '601': animal care '602': installation '603': public opinion '604': equity research platforms '605': gene editing '606': professional network '607': merchants '608': adventure travel '609': instagram '610': language learning '611': ui design '612': deep tech '613': quotidien '614': renewable energy tech '615': metal work '616': timing & synchronisation '617': robot '618': data storage '619': community '620': chaine '621': soudure numérique '622': startup crowdfunding '623': wealth management it '624': '@industrie4.0' '625': internet first news publishers '626': government relations '627': online sports platform '628': ophthalmology '629': online games '630': investment services '631': surveying '632': auto parts '633': visual analytics '634': neocourtage '635': case & matter management '636': government operations and administrative tools '637': furniture '638': formation commer '639': all images '640': farm mapping '641': hosted pages '642': cardiovascular '643': energy, utilities & waste treatment '644': mind & body wellness '645': testing & optimization '646': 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'902': agriservices '903': erp crm '904': wearable technology '905': aux expertises complémentaires ayant la volonté de partager. 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'954': neurodegenerative '955': employability '956': opportunistic '957': big data analytics '958': jamstack '959': pos payment terminal '960': large langage models '961': motor and generator '962': formateur '963': bocconi university '964': vol '965': gros electromenager '966': recruiting '967': corporate prepaid cards '968': consumer insurance management '969': other medical data '970': freight '971': contrat '972': conseils immobiliers '973': personalized recommendation '974': children '975': telephony & wireless '976': website & application security '977': digital content '978': conversational user interface '979': salle de bains '980': centre de formation '981': business & finance '982': plastique '983': credit builder loans '984': brain game '985': fraud detection '986': wall covering '987': marques '988': audio devices '989': son '990': villeurbanne '991': light gauge '992': ott streaming platforms '993': test de compétences '994': third party risk '995': tech support '996': 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'1035': experiential '1036': events tech '1037': maté '1038': boisson sans alcool '1039': investissement '1040': internet first platforms '1041': sms & push notifications '1042': moving & storage '1043': pet supplies e commerce '1044': watches '1045': process & collaborative applications '1046': browser extensions '1047': city administration '1048': property development '1049': free float '1050': idp '1051': service centers '1052': openstack platforms '1053': algorithmic '1054': '@nutrition' '1055': teleproduction '1056': réseau social '1057': bogue '1058': detective '1059': histoire interactive '1060': account-linked '1061': cytokine receptors '1062': real estate investment '1063': video analysis '1064': plan de masse '1065': home care e-commerce '1066': holiday packages '1067': blood & organ banks '1068': texas in houston '1069': lotissements '1070': generator '1071': personalfinancemanagement '1072': building maintenance '1073': pénibilité '1074': checks '1075': suivipédagogique '1076': homeservice '1077': solar energy '1078': electrical distribution '1079': courrier '1080': innovation digitale '1081': for professionals '1082': sw components '1083': multi industries '1084': fabrication à la demande '1085': production '1086': team cohesion '1087': e-commerce enablers '1088': genetic counseling '1089': health care services '1090': energies propres '1091': building energy management '1092': student solutions '1093': power transmission '1094': ar vr cms '1095': genome engineering '1096': veterinary '1097': skill assessment '1098': materials tech '1099': broker '1100': in-memory '1101': ménage '1102': enseignant '1103': broderie '1104': public company data '1105': activity based '1106': tools '1107': homedesign '1108': manufacturing '1109': automated '1110': maketing automation '1111': storytelling '1112': construction & real estate '1113': sécurité systèmes information '1114': carte de visite '1115': plomberie '1116': network operators '1117': metasender '1118': accounting enablers '1119': advertising '1120': medical diagnostics '1121': consulting & professional services '1122': sound based '1123': technology platforms '1124': personal health management '1125': '@immobilier' '1126': architecture intérieure '1127': enterprise '1128': meditation apps '1129': cash flow management '1130': management information systems '1131': private search engine '1132': agricultural equipment '1133': creation de site '1134': natural language processing '1135': staffing & recruiting '1136': tour operator '1137': emailing '1138': techstars portfolio '1139': travel and hospitality '1140': personal wellness services '1141': visite virtuelle 3d '1142': internal '1143': permis de conduire '1144': talent management '1145': billing management '1146': graphite '1147': oncology '1148': agents '1149': warnerbros '1150': embedded systems '1151': craft & sewing '1152': nonmetallic mineral manufacturing '1153': law enforcement '1154': chinese '1155': baby health monitoring '1156': développement d'offre '1157': tech first rental agency '1158': commission-based marketplace '1159': crop tech '1160': hardware '1161': agences immobilières '1162': remote monitoring '1163': apparel brands '1164': education it '1165': hotel it '1166': flexoffice '1167': material provider '1168': collateral management '1169': survey builders '1170': tradedepot '1171': smb suite '1172': enchère immobilière '1173': hospitality '1174': novel foods '1175': billing '1176': musical groups '1177': media buying & planning tools (adtech) '1178': performance commerciale '1179': music creation '1180': cargomanagement '1181': analytics platforms '1182': automotive parts & accessories '1183': mobilemarketing '1184': chiropractors '1185': consumer and sme loans '1186': programming '1187': sector specific '1188': courses '1189': coffrefortnumérique '1190': protective equipment '1191': noncitrus fruit '1192': nanotechnology '1193': locksmiths '1194': nontraditional '1195': addiction '1196': modelisation '1197': triplanner '1198': cricket '1199': professional music creation '1200': integration and orchestration middleware '1201': enterprise search '1202': abonnement vin '1203': iim bangalore '1204': charging solutions '1205': '@logiciel' '1206': frontier tech '1207': microcredit '1208': ui/ux '1209': fitness & wellness tech '1210': lowinterest '1211': délivrabilité '1212': disposable plastics '1213': database & file management software '1214': pegase moto '1215': building management '1216': leadership '1217': holding companies '1218': bop '1219': 3d printing in fashion '1220': rent guarantee '1221': blood banks '1222': '@agriculture' '1223': storage management '1224': home & kitchen '1225': interior design '1226': course à pied '1227': brokers '1228': financement des créances '1229': health & personal care '1230': financialinclusion '1231': immunohistochemistry '1232': client management '1233': online business '1234': ai in life sciences '1235': apparel & footwear '1236': passenger '1237': trading platform '1238': cybersecurité '1239': ad exchanges '1240': masques '1241': space travel '1242': distributed energy resources '1243': travaux publics '1244': commande publique '1245': vocational education '1246': animal production '1247': contact management '1248': sales enablement '1249': mail delivery '1250': employee benefits '1251': counseling '1252': relationships '1253': influence '1254': fintech for all '1255': charitable organizations & foundations '1256': vision solutions '1257': battery design software '1258': k-12 '1259': delivery '1260': homeland security '1261': food & staples retailing '1262': recrutement '1263': single-family '1264': security and trading data '1265': database administration '1266': agence analytics '1267': trade finance '1268': neobanque '1269': research & innovation '1270': bi verticals '1271': network management '1272': used car '1273': business payments solutions '1274': gps '1275': digital pathology '1276': seg '1277': social media '1278': employee management '1279': multiple medical images '1280': student loans '1281': influenceurs '1282': food processing '1283': biometrics system '1284': tech for digital publishers '1285': gestion du temps '1286': film & tv production '1287': habitat '1288': soin '1289': internet first real estate brokers '1290': permis libre '1291': skincare '1292': waste '1293': '@qvt' '1294': physicians clinics '1295': trauma care '1296': lung function testing '1297': university solutions '1298': on demand commercial space marketplaces '1299': employee advocacy '1300': cinema '1301': pergola '1302': épicerie fine '1303': networking '1304': agriculture and forestry data '1305': vehicule luxe '1306': cleanbeauty '1307': btp '1308': proxy '1309': online travel '1310': industry verticals '1311': marketing événementiel '1312': repas '1313': supply chain procurement '1314': mom & baby care '1315': paperboard '1316': '@solaire' '1317': hotel '1318': regulatory affairs '1319': mass payouts '1320': crm & srm (customer & sales management) '1321': biomaterials '1322': subscription service '1323': livestock '1324': mobile experience technology '1325': deepweb '1326': payment fraud detection '1327': influencer marketplace '1328': online payments '1329': marchés publics '1330': drive-to-store '1331': scanning probe microscope '1332': storage '1333': worker's compensation '1334': distributor '1335': blockchain in energy '1336': alternative proteins '1337': '@roadtrip' '1338': hobby goods '1339': curated '1340': business vpn '1341': managed security service '1342': php '1343': indoor '1344': napkins '1345': creation '1346': quality assurance '1347': expérience immersive '1348': university of pennsylvania '1349': transportation '1350': goods '1351': contracting services '1352': portfolio tracker & analysis '1353': tours and sightseeing '1354': test & measurement equipment '1355': supply chain analytics '1356': delivery management '1357': integrated '1358': film, games '1359': activity based learning '1360': medicine '1361': directory '1362': school '1363': employer '1364': boxmensuelle '1365': legal professionals '1366': assurance '1367': precision medicine '1368': architecture, engineering & design '1369': beachfront properties '1370': network attached storage '1371': biotech '1372': export '1373': thés '1374': connectivity '1375': customers financials services '1376': secondary market '1377': media '1378': nonalcoholic beverage '1379': medication '1380': videos streaming '1381': cosmetics '1382': travel arrangement '1383': revenue ops '1384': automobile '1385': epargne '1386': network '1387': vidéosurveillance '1388': erm '1389': highway construction '1390': enterprise ticketing platforms '1391': développement commercial '1392': debit card '1393': mobile technologies '1394': relance '1395': audit de conformité '1396': spirits '1397': tourisme voyage '1398': hôtellerie '1399': social media advertising '1400': realiteaugmentee '1401': cyberattaques '1402': taxi paris75 '1403': postproduction '1404': qvct '1405': research software '1406': tele-travail '1407': gastronomie '1408': conversational bots '1409': interview '1410': pet suppliers '1411': convenience stores, gas stations & liquor stores '1412': smart kitchen appliances '1413': customerservice '1414': technical support '1415': eployment data verification '1416': inspiration cadeau '1417': fire prevention '1418': generation z '1419': expenses aggregation '1420': kotlin '1421': art gallery '1422': shopping cart solutions '1423': senior care '1424': religious organizations '1425': relay '1426': cfo services '1427': monétisation '1428': television '1429': esanté '1430': exchange '1431': funéraire '1432': collection agency '1433': jeux de piste '1434': insurance policy solutions '1435': full stack investment platform '1436': immobilier neuf '1437': nlp platforms '1438': media planning '1439': location de parking '1440': applicant tracking system '1441': supply chain execution '1442': media production and management '1443': enterprise collaboration solutions '1444': care '1445': aménagement '1446': bordeaux '1447': '@madeinfrance' '1448': mobile game developers '1449': peer-to-peer '1450': grocery & staples '1451': steam '1452': hrtech edtech '1453': diabetes '1454': internet software & services '1455': gestiondecrise '1456': health diagnostics '1457': plug in '1458': google '1459': zoning analysis '1460': découverte '1461': repricing '1462': recurring billing '1463': donation '1464': maintenance '1465': content - software components '1466': remediation services '1467': applications web '1468': realestate '1469': chemical engineering '1470': artisanal products '1471': patient aids '1472': prototyping '1473': création de contenu '1474': civil engineering construction '1475': electronics '1476': blockchain '1477': cable & satellite '1478': sales development '1479': rpa '1480': telemedecine '1481': examen en ligne '1482': assurance-crédit '1483': health care equipment & supplies '1484': onboarding kyc and aml '1485': task management tools '1486': commerce and shopping '1487': technologie de soudage '1488': expense tracking '1489': nourriture '1490': reseller '1491': trucking, transportation '1492': platform & services '1493': high tech '1494': casheconomy '1495': cognitif '1496': business formation '1497': amusement parks '1498': hypercustomisation '1499': economy '1500': ai in recruiting '1501': id document verification '1502': stage '1503': vulnerability assessment '1504': divertissement '1505': testing platform '1506': technology platform '1507': daf externalisé '1508': pooling '1509': hax '1510': emballage souple innovant '1511': energie '1512': listings '1513': repair services '1514': casual wear '1515': financial forecasting '1516': pollution '1517': finance and accounting '1518': manufacturing process analytics '1519': temperature-controlled services '1520': urban planning '1521': multiboxs.com '1522': phytothérapie '1523': together est une communauté d’entrepreneurs '1524': sales enablement tools '1525': econcierge '1526': printing services '1527': post acute care '1528': social platforms & communication '1529': outdoor advertising '1530': personal products '1531': expert immobilier '1532': video on demand '1533': auto-école '1534': louermaborne.fr '1535': k-12 edtech '1536': billetterie '1537': pc & console games '1538': socialmedia '1539': american football '1540': warehouse '1541': climate tech '1542': input '1543': tobacco '1544': co-browsing '1545': bain '1546': workspace '1547': luminaires '1548': on-demand glucose monitoring '1549': server '1550': trusts '1551': barcelone '1552': network firewalls '1553': hospital information systems '1554': cyberbullying '1555': support client '1556': laboratory services '1557': test and measurement '1558': cosmetics, beauty supply & personal care products '1559': risk & compliance '1560': corporate cards '1561': it architecture '1562': batch 10 '1563': caméra '1564': enregistrement video '1565': industrial drones '1566': listing services '1567': travailleurs indépendants '1568': décoration d'intérieur '1569': cse '1570': techinfab '1571': food & prime mantry '1572': equipementsportif '1573': publicité google '1574': social guide '1575': chemicals '1576': motivation '1577': racetracks '1578': crypto '1579': rénovation '1580': iso27001 '1581': réfrigération '1582': gas sensors '1583': emploi & entreprise '1584': lms '1585': cohort 6.0 '1586': biometrics '1587': equity research '1588': communications equipment '1589': campaign optimization '1590': cars '1591': football americain '1592': outdoors '1593': management '1594': offgrid '1595': petrochemicals '1596': genetique '1597': pass french tech '1598': batch 2 '1599': speech-to-text '1600': body vitals based '1601': application performance management '1602': structural metal '1603': enhanced automation '1604': millennials '1605': archiving service '1606': education '1607': summaries '1608': lawyer '1609': child care '1610': development platforms '1611': deal management '1612': transformation numérique '1613': internet first point of sale financing '1614': masterclasse '1615': ondemand insurance '1616': expertise comptable '1617': iot in healthcare '1618': cristaux '1619': transportation data '1620': brex for international startups '1621': video content management system '1622': masschallenge batches '1623': ux design '1624': subscription '1625': ml libraries '1626': consumerlending '1627': meilleurs sites '1628': circuit de proximité '1629': modelling '1630': event promotion '1631': content creation '1632': personal care '1633': veterans affairs '1634': marketing research '1635': exposition '1636': photography studio '1637': ingénierie acoustique '1638': community-based '1639': carte postale '1640': rencontres business '1641': ship building '1642': hypertension '1643': humanitarian '1644': scheduled freight '1645': financial trading '1646': voyageaffaire '1647': loan comparison platforms '1648': mice '1649': multi-function '1650': health & drug screening '1651': tactile feedback '1652': ticket booking platforms '1653': payement platforms '1654': diy platforms '1655': couverture '1656': distillery '1657': process mining '1658': golf '1659': infogérance '1660': operating system '1661': cv '1662': satellite communication '1663': beautycare '1664': b2cfocus '1665': lowcarbon '1666': estate agent '1667': display medium '1668': barber shops '1669': synthetic rubber '1670': decarbonation '1671': corporate '1672': littérature jeunesse '1673': ui/ux design '1674': restaurant services '1675': ffp2 '1676': gaming '1677': '@serre' '1678': train '1679': food stalls '1680': menswear '1681': vehicles '1682': medecin '1683': '@startup' '1684': microprocessors '1685': consigne '1686': services professionnels '1687': crowdsourced security testing '1688': rocket internet portfolio '1689': internet first men's clothing brands '1690': impact environnemental '1691': 'devices: consumers & industry' '1692': multi-analyte systems '1693': facilities management '1694': internet first health insurers '1695': medical laboratories & imaging centers '1696': doctolib '1697': lowcode '1698': esg data '1699': audiobooks '1700': private equity '1701': agentsimmobilier '1702': occasion '1703': badgeuse '1704': mayonnaise '1705': streetmarketing '1706': biofuels '1707': modélisation 3d '1708': vacation camps '1709': public figures '1710': public assistance '1711': paiement '1712': ad retargeting '1713': lumber, wood production & timber operations '1714': machinelearning '1715': nonferrous metal '1716': wire & cable '1717': site emploi '1718': doctor appointment booking '1719': financial product aggregators '1720': internet first brand '1721': hosiery '1722': batch 11 '1723': piece goods '1724': moteur de recherche immobilier '1725': lead management '1726': workforce management '1727': document automation '1728': hotel channel management software '1729': organic products '1730': source code management '1731': lpwa '1732': '@agroforestry' '1733': venue '1734': optics '1735': etf '1736': cheveux '1737': ibpms '1738': organisation & sharing '1739': e-commerce solutions '1740': exchanges '1741': ehr platforms '1742': financial communication '1743': mattress '1744': vertical freelancer marketplace '1745': nutraceuticals '1746': conseil en image '1747': data aggregation & analytics '1748': gaming news '1749': green buildings '1750': franchising '1751': cinemas '1752': clinical data management '1753': web 3 '1754': blockchain in healthcare '1755': mushroom '1756': rayonnements '1757': mobile apps '1758': hardware development '1759': byod security '1760': iot cellular connectivity '1761': horticulture '1762': b2b payments '1763': engineering software '1764': usedcars '1765': motobikes '1766': product design '1767': cornell tech program '1768': health and wellness '1769': content marketing agencies '1770': safety management '1771': it solutions '1772': industrial '1773': insuretech '1774': image recognition '1775': food & beverage industry '1776': iit guwahati '1777': online travel agencies '1778': digitalisation des acomptes sur salaire '1779': store '1780': supply chain solutions '1781': agricultural production '1782': field sales software '1783': végétarien '1784': construction durable '1785': mesures d'opinion '1786': building security management '1787': financial planning & analysis '1788': puériculture '1789': customer data platform '1790': animation and character design '1791': farm produce '1792': personal '1793': studio vidéo '1794': energy management solutions '1795': feature management '1796': floral arrangements '1797': pneumatics and compressors '1798': foulard '1799': agriculture drones '1800': 3dprinting & reverse engineering sw '1801': textiles, apparel & luxury goods '1802': processautomation '1803': service de livraison '1804': software platform '1805': micropaiement '1806': rural '1807': screening devices '1808': chargeback '1809': '@tezos' '1810': corporate learning management systems '1811': '@sportech' '1812': insurance it '1813': integrative bioinformatics platforms '1814': shipping & fulfillment '1815': fruit food '1816': souvenirs '1817': solarsystem '1818': theater '1819': incubateur '1820': product feedback '1821': music education '1822': energy monitoring system '1823': elderly care '1824': metal can '1825': legal assistance '1826': card solutions '1827': mortgage '1828': start-up factory '1829': industry based '1830': product design & development '1831': paas '1832': unified email '1833': modules '1834': car & truck rental '1835': vitamin '1836': designer productivity tools '1837': feedback '1838': heath '1839': occupant detection '1840': social enterprise '1841': veterinary care '1842': weddings '1843': films '1844': micro electro mechanical systems '1845': feedback analytics '1846': incendie '1847': pv '1848': local '1849': petites annonces '1850': publipostage '1851': pharmaceutical '1852': écoconception '1853': beauté mode maison '1854': business intelligence (traditional olap) '1855': ar management & invoicing '1856': ly '1857': cell-based therapies '1858': low code development platforms '1859': roasted nuts '1860': metal engraving '1861': accélérateur '1862': incuballiance '1863': facilitation '1864': hôtel '1865': homemanagement '1866': instruction '1867': audio '1868': mobiles & accessories '1869': cargo tracking '1870': savings & investing '1871': firearms '1872': performance '1873': tiers de confiance '1874': gestion locative '1875': savings platforms '1876': internet of things '1877': warehousing '1878': tribal nations '1879': y combinator batches '1880': animauxdecompagnie '1881': guide culturel '1882': privacy policy '1883': oem '1884': extermination '1885': salesforce '1886': sexual wellness '1887': metaverse '1888': security deposit replacement '1889': suividechantier '1890': cameras & accessories '1891': smartcity '1892': site web immobilier '1893': prevention and intervention '1894': phonebot '1895': machinery manufacturing '1896': fleurs '1897': industry solutions '1898': membership group '1899': chauffage '1900': automated content generation '1901': thermal products '1902': ameublement '1903': martech '1904': campagnes publicitaires '1905': legal practice management '1906': content '1907': '@sûreté' '1908': communications infrastructure '1909': generation equipment '1910': utility '1911': docteur '1912': compute fabric platforms '1913': key value store '1914': signature électronique '1915': enterprise data management '1916': vegetable '1917': mobile gaming '1918': flash devices '1919': ad '1920': industrial conglomerates '1921': vanadium '1922': book tech '1923': software delivery '1924': education general '1925': fast casual dining '1926': food discovery and ordering '1927': home renovation '1928': wellcare '1929': language translation '1930': randonnée '1931': wired telecommunications '1932': energy storage '1933': distributed '1934': employee scheduling '1935': organizations general '1936': consumerbehavior '1937': coupons '1938': materials testing '1939': '2017' '1940': pharmacie '1941': telecommunications '1942': genomics '1943': virtual care '1944': marketing automation '1945': vision intelligente '1946': nut '1947': solidarité collective '1948': aml & ctf '1949': cigarettes '1950': code development '1951': developer tools '1952': building technology '1953': commerce '1954': achat groupé '1955': coffret '1956': digital logic ics '1957': health programs '1958': corporate & business '1959': hydrocarbons '1960': eau usées '1961': bi suite '1962': smartindustry '1963': gateways '1964': data discovery and visualization '1965': prestashop '1966': check out '1967': hobbies '1968': safran '1969': utilities '1970': bioplastics '1971': electricite '1972': home sharing '1973': greeting card '1974': water usage and leak detection '1975': beauty services '1976': adhesives '1977': tech-enabled services '1978': university of california (berkeley) '1979': crowdfunding platforms '1980': readers '1981': networking software '1982': alternative medicine '1983': pos payments '1984': conseil digital '1985': application sw '1986': prepared food '1987': plug in charging '1988': pcm '1989': digitalpayment '1990': photographers '1991': landing pages '1992': cashtogood '1993': tech enabled freight forwarder '1994': peace advocacy '1995': electricity production '1996': farm management software '1997': industrial automation '1998': corporate identity '1999': wireless '2000': '@banqueprivée' '2001': salaire '2002': education services '2003': communication devices '2004': meat substitute '2005': expensesmanagement '2006': désinfection '2007': création boutique en ligne '2008': motels '2009': stress and anxiety management '2010': poc insights '2011': educational resources '2012': site de recontre '2013': réalité mixte '2014': healthcare & fitness '2015': countertop '2016': money transfer '2017': web design '2018': conduite sans permis '2019': intelligence artificiel '2020': primaire '2021': university of chicago '2022': business financial services '2023': holding companies & conglomerates '2024': industrials '2025': credit '2026': carrière '2027': collaborative albums '2028': agence web3 '2029': b2b remittance '2030': semiconductors '2031': '@location' '2032': cohort 1.0 '2033': book summarization platforms '2034': ticket '2035': insurance as a service '2036': answering services '2037': machine vision '2038': tiktok '2039': médico-social '2040': design sprint '2041': renewable and alternative energy '2042': artiste '2043': node js '2044': web hosting '2045': blockchain in financial services '2046': threat management '2047': workflow management '2048': bienmanger '2049': air '2050': récupération de données '2051': integrated grc software '2052': enterprise resources management applications ( erp & grc) '2053': autonomous vehicles '2054': grocery retailers '2055': storytellers '2056': sécurité informatique '2057': tea '2058': telecoms '2059': tech solutions '2060': expenses management '2061': cartographic '2062': rédaction '2063': o '2064': electricien '2065': pets '2066': défis '2067': human resources '2068': maritime tech '2069': seasoning '2070': bijou '2071': payment card '2072': hec paris '2073': edge computing platforms '2074': digital entertainment '2075': sim less fota '2076': aide à la décision '2077': authoring '2078': docker ecosystem '2079': loan brokers '2080': other energy '2081': in-vitro '2082': chatsolution '2083': funerals '2084': contract lifecycle management '2085': outdoor and casual apparel '2086': financement participatif '2087': java '2088': sponsorship '2089': anti-nuisible '2090': adhesive '2091': annuaire '2092': cadeau '2093': local shopping '2094': account based marketing '2095': extermination service '2096': fidelité '2097': cause '2098': ddos mitigation '2099': machine tools '2100': blogging platforms '2101': escrow '2102': intangible assets '2103': female founders '2104': binding '2105': manufacturing services marketplaces '2106': comparateur d'assurances '2107': user location apps '2108': books '2109': petservices '2110': emploi '2111': petmarket '2112': mmo games '2113': k-12 schools '2114': marketing technology (martech) as a category addresses growing needs that heads of marketing have to link investments to revenue. '2115': carte de fidélité '2116': online stores '2117': livre '2118': regulatedmarkets '2119': workforce recruitment '2120': crm for smbs '2121': influencer marketing '2122': babytech '2123': wallet management '2124': robots '2125': small business loans '2126': patient management '2127': media & entertainment '2128': credit assessment '2129': online event ticketing '2130': wealth data aggregation '2131': attendee engagement '2132': ambulatory services '2133': hybrid cloud '2134': homework help '2135': bière '2136': marine transportation '2137': performing arts '2138': digitization '2139': veille stratégique '2140': casino '2141': cohort 10 '2142': gift registry '2143': clinical chemistry '2144': massachusetts institute of technology (mit) '2145': '@construction' '2146': curation '2147': search engines '2148': content moderation '2149': equity '2150': content creation tools '2151': msp + saas '2152': proprietary '2153': ride sharing '2154': marketing surveys '2155': dropshipping '2156': subscriptions bills '2157': recreational activities '2158': public relations '2159': mobile app studios '2160': '@inclusion' '2161': speech analytics '2162': api connectivity service '2163': service à la personne '2164': account linked '2165': summer '2166': affairespubliques '2167': crm & marketing '2168': arttech '2169': food discovery & ordering '2170': micro-delivery grocery service '2171': security testing '2172': algebraic modeling '2173': commercial machinery '2174': enabler '2175': paper bag '2176': dairy products '2177': ferry '2178': retirement '2179': software product development '2180': piano '2181': shoes & jewelry '2182': events '2183': iot development boards & kits '2184': asset management '2185': softwaresolution '2186': custom software & it services '2187': performers '2188': cultural & informational centers '2189': online forums '2190': fresque du climat '2191': sustainable packaging '2192': notice '2193': evenement '2194': text solutions '2195': innerwear '2196': medical imaging '2197': photographic services '2198': industrial machinery & equipment '2199': funeral '2200': harvard university '2201': gestion de parc informatique '2202': data preparation '2203': campaigns '2204': estate '2205': small molecule '2206': psychometric '2207': bon plan '2208': team and event management '2209': truck '2210': création environnement '2211': job posting '2212': route optimization '2213': couvreur '2214': mental health '2215': materials '2216': b2c fashion e-commerce '2217': dental & maxillo-facial '2218': revenuebasedfinancing '2219': sports goods '2220': wallet transfer '2221': avion '2222': '@hardware' '2223': telephone '2224': glass products '2225': mobile application management (mam) '2226': rail it '2227': augmented reality '2228': science '2229': approvisionnement '2230': dairy '2231': database technology '2232': lessive '2233': développeurs '2234': virtual assistants '2235': miel '2236': recreational vehicles '2237': virtual tours '2238': vegan '2239': diesel '2240': market strategy '2241': autonomous farming '2242': receptors '2243': home centers '2244': content - data '2245': used '2246': wheat '2247': hotels, restaurants & leisure '2248': advanced manufacturing '2249': sequencing analysis '2250': préparateurs '2251': bienêtre '2252': inventory '2253': outdoor power equipment '2254': vue '2255': hvac '2256': in game '2257': developer tool '2258': software testing '2259': satellites '2260': direct mail '2261': cloud gaming '2262': data and monitoring solutions '2263': operator transit planning '2264': agence marketing '2265': underwriting & risk management '2266': omni-commerce '2267': pet '2268': free to play '2269': cadeaux vin '2270': legal services '2271': jewellery '2272': design conceptualization '2273': volleyball '2274': drone security '2275': dental care '2276': wifi '2277': customermanagement '2278': creative agency '2279': rv parks '2280': taxi '2281': industrial process monitoring '2282': dentists '2283': tracking and monitoring '2284': investment data '2285': atm '2286': integrated platforms '2287': gamers tools '2288': debt collections '2289': sans engagement '2290': freight rate '2291': lettre de motivation '2292': customer success management '2293': cut and sew '2294': supply chain visibility '2295': partnership '2296': foreign trade '2297': 4 coupons & deals '2298': defi '2299': conférences '2300': belt conveyor systems '2301': iot in food '2302': autonomous vehicles lidar '2303': opendata '2304': baskets '2305': agence webmarketing '2306': monitoring services '2307': metallurgie '2308': langue '2309': sculpture '2310': financialproducts '2311': conseil d administration '2312': bornes de recharge '2313': airlines '2314': amour '2315': bi & search '2316': fulfillment '2317': santé auditive '2318': merchantsservices '2319': analytical processing '2320': collaboration '2321': logistics & supply chain '2322': interactive learning '2323': oil gas and energy field services '2324': digital twin '2325': téléconsultation '2326': autonomous vehicles mapping '2327': p2p '2328': '@paiement' '2329': multiple data sources '2330': plumbing services '2331': b2bloans '2332': data & analytics '2333': deathtech '2334': acne '2335': white-label ticketing '2336': asset monitoring & tracking '2337': competence '2338': incubation '2339': charpente '2340': stock videos '2341': multi-industry '2342': legapass '2343': challenge '2344': video sharing '2345': video surveillance '2346': lyon '2347': marketingcampaign '2348': competitions '2349': a/b testing '2350': core banking '2351': nanomedicine '2352': rketing '2353': carpentry '2354': marine shipping & transportation '2355': information '2356': earphones & headphones '2357': workforce '2358': online information search '2359': retail investor '2360': news '2361': bookkeeping '2362': devices and instruments '2363': metal valve manufacturing '2364': admin solutions '2365': ecommerce (platform) '2366': savingplatform '2367': medecins '2368': integration '2369': virtual office '2370': teambuilding '2371': identités numériques '2372': museums and historical sites '2373': achat en ligne '2374': biometric authentication '2375': foodmarketplace '2376': biodegradable polymers '2377': mobile security '2378': cryptocurrency trading '2379': writing & editing '2380': inorganic chemical '2381': pollen '2382': iot sensors '2383': call centers & business centers '2384': genomics services '2385': industrialisation '2386': radio networks '2387': event space '2388': beacon '2389': property rentals '2390': land prospecting '2391': toulouse '2392': consumer intelligence '2393': career guidance '2394': voicebot '2395': diversité et inclusion '2396': graduate jobs '2397': financial support '2398': jobs '2399': loyalty '2400': correctional institutions '2401': nail salons '2402': ranking & comparator '2403': deepfake detection '2404': social platform '2405': advertising management, advertising '2406': industry specific applications '2407': foire virtuelle '2408': cross border '2409': '@fidélité' '2410': bim and 3d modelling '2411': consumer focused '2412': sigfox '2413': robotics in manufacturing '2414': it education '2415': '@autonomie' '2416': tribunews '2417': surgical instruments '2418': staking '2419': indoor positioning '2420': data as a service '2421': drug retailers '2422': interractive data '2423': virtual cards '2424': entrepreneuriat féminin '2425': social shopping '2426': manual '2427': btoc '2428': logistictech '2429': administrative workflow '2430': analytics & reporting '2431': bloodtests '2432': api integration '2433': local business '2434': presentation tools '2435': licensing '2436': recreational vehicle '2437': ride-hailing '2438': creditaccess '2439': trade life cycle management '2440': market research '2441': hacker news '2442': refineries '2443': plateformes digitales '2444': skin health '2445': mid & back office solutions '2446': climatechange '2447': '@sport' '2448': hand, power & lawn-care tools '2449': robot lawyers '2450': road & rail '2451': neurodegenerative diseases drugs '2452': mooc '2453': finance & accounting tech '2454': industrial internet of things '2455': managementsoftware '2456': industrial distribution '2457': term life insurance '2458': terrazzo '2459': intelligence prescriptive '2460': accelerators & incubators '2461': help desk '2462': socialplatform '2463': bpo '2464': mobilepayments '2465': office manager '2466': fruit '2467': data collection & internet portals '2468': safety '2469': textile '2470': anti phishing software '2471': scholarship programs '2472': handtools '2473': textbook '2474': actuators '2475': retailing '2476': test utilisateur '2477': baby health '2478': laser treatment '2479': energy, utilities & waste treatment general '2480': rental management '2481': personal health records '2482': natural '2483': motion picture '2484': markettools '2485': green building '2486': sleep tech wearables '2487': database '2488': réparation téléphone '2489': data governance '2490': agriculture and farming '2491': formatique '2492': microinsurance '2493': implantable '2494': autocad '2495': postal service '2496': virtual san '2497': consulting et intégration ssi '2498': system management software '2499': individuals '2500': charcuterie '2501': extension chrome '2502': creation de site web '2503': financial transactions '2504': formations '2505': physical sciences '2506': dry '2507': air force '2508': réseau '2509': supercomputers '2510': art '2511': secondary education '2512': youtube '2513': processors '2514': blanchisserie '2515': ultimate frisbee '2516': managed data security '2517': car washes '2518': salle de réunion '2519': application lifecycle management '2520': groupe '2521': hotelmanagement '2522': vidéo interactive '2523': wholesaler '2524': activité physique '2525': translation management systems '2526': configure price quote '2527': gestion du budget '2528': visibility & compliance '2529': smb '2530': waste heat recovery '2531': proprietary drones '2532': winery '2533': home furnishing products '2534': pipeline '2535': pharma '2536': physical '2537': amazon '2538': hotel management '2539': bicycle '2540': behavioral science '2541': '@smartgrid' '2542': publication directory '2543': returns '2544': internet of things infrastructure '2545': railroad '2546': gestion entreprise '2547': web experience management '2548': process & collaborative applications (ecm '2549': commercial goods '2550': biofuel '2551': viager '2552': confidentialité '2553': accessories '2554': incubators '2555': semiconductor '2556': data engineering '2557': big data in cybersecurity '2558': montres '2559': private social networking '2560': autonome '2561': distribution and wholesalers '2562': leather '2563': forme '2564': vente '2565': smb-focused '2566': scan '2567': psychothérapie '2568': life sciences tools & services '2569': pain '2570': elder care '2571': vitamins, supplements & health stores '2572': vidéo publicitaire '2573': immersion '2574': services '2575': employeebenfits. '2576': winetech '2577': colorants '2578': vélo '2579': on-demand economy '2580': cold emails '2581': produits locaux '2582': machine intelligence systems '2583': marketing apis & middleware '2584': engagement based rewards '2585': online '2586': image based '2587': call analytics '2588': cuisinier '2589': smoking '2590': jeux vidéo '2591': diasporas '2592': investment tools and platforms '2593': heavy machinery '2594': communitymanager '2595': ballon solaire '2596': solutions professionnelles '2597': metavers '2598': look '2599': landscaping '2600': animal training '2601': communication strategy '2602': equity crowdfunding '2603': last mile '2604': information collection & delivery '2605': vibrotactile '2606': télécoms '2607': inventory management solution '2608': disease agnostic '2609': jobboard '2610': employee engagement '2611': investmentmanagement '2612': order management systems '2613': rental services '2614': commentaires '2615': lunettes '2616': p2p second hand cars marketplace '2617': restauration rapide '2618': mobile food '2619': childrens clothing '2620': mam '2621': intelligence & governance '2622': watches & jewelry '2623': localisation - l10n tech '2624': produits du quotidien '2625': '@jeu' '2626': e-distributor '2627': operationalexcellence '2628': sports & outdoors '2629': planning & analytics '2630': paint '2631': produit '2632': data solutions '2633': welding equipment '2634': forex '2635': software engineering '2636': insulation '2637': multi subject '2638': automotive services '2639': pompe à chaleur '2640': home healthcare '2641': hr services '2642': brand marketing '2643': furnace '2644': financial, legal & hr software '2645': gestionnaire '2646': aquaculture '2647': '@enfants' '2648': payment security '2649': piam '2650': studio '2651': apero '2652': marine vessel tracking data '2653': tuitionfees '2654': physical fitness '2655': appliances '2656': cognitive '2657': investing '2658': onlineservices '2659': emploi startup '2660': nondurable goods '2661': na '2662': iwms '2663': banking & mortgages '2664': core banking platforms '2665': off-road vehicles '2666': minerals & mining '2667': juices '2668': '@déco' '2669': home improvement services '2670': deep learning '2671': connected vehicles '2672': emergency medicine '2673': music artist it '2674': bike app '2675': asset tracking '2676': real time marketing '2677': generalist '2678': smartcities '2679': visual content '2680': mechanical design '2681': dbaas '2682': diving '2683': charging point locator '2684': marketingtech '2685': swimming pool '2686': car '2687': regtech '2688': advocacy group '2689': card issuance '2690': informatique / it '2691': nosql database '2692': online financial databases '2693': business operations '2694': bricolage '2695': academics '2696': frozen fruit '2697': charter school '2698': private label products '2699': mobilepayment '2700': chambre '2701': paymentservices '2702': caregivers '2703': multi industry '2704': virtual reality '2705': safety & security '2706': space vehicles '2707': family planning '2708': accompagnement aux porteurs de projet '2709': medical testing & clinical laboratories '2710': assistance services '2711': dentaire '2712': cee '2713': personalized audio profile apps '2714': school it '2715': inventaire '2716': rfid '2717': usage-based '2718': restaurant it '2719': university '2720': couteaux '2721': generic '2722': lending & brokerage '2723': sustainability '2724': thermoelectric '2725': bioinformatics and computational biology suites '2726': commercial vehicles '2727': anti-corruption '2728': consultation '2729': community and lifestyle '2730': online brokers '2731': healthcare monitoring '2732': '@weekend' '2733': cosmetic surgery '2734': mobilité durable '2735': augmented shopping '2736': grocery retail '2737': private mail centers '2738': enabling technologies '2739': womencare '2740': asset-light '2741': cell or tissue-based '2742': smart building '2743': green consumer goods '2744': issuer processors '2745': garage aggregator '2746': core network '2747': inboundcommunication '2748': end to end services '2749': food and beverage '2750': aerospace '2751': business lifecycle management '2752': carnet de voyage '2753': protein technology '2754': advice '2755': site internet '2756': skylight '2757': communautés '2758': ats '2759': object recognition '2760': '@carnetdevoyage' '2761': automotive insurance '2762': refrigeration '2763': déconnexion '2764': software defined storage '2765': api management platforms '2766': second hand vehicles '2767': batch x '2768': professional translation services '2769': back office solutions '2770': predictive analytics '2771': water & water treatment '2772': industrial engineering '2773': archiving '2774': gamified platform '2775': lastmiledelivery '2776': environmental health and safety compliance '2777': peer to peer '2778': bijou connecté '2779': book publishing '2780': communication hardware '2781': drh '2782': legal '2783': fintech & legaltech '2784': e-bikes '2785': histopathology '2786': zoos '2787': cloud based '2788': power line inspection '2789': identity management '2790': healthcare '2791': exterior lighting '2792': airbnb '2793': food preparation '2794': corrections facilities '2795': tourism '2796': food service '2797': vending and concessions '2798': confiance '2799': 5g enablers '2800': computer equipment & peripherals '2801': tech for teachers '2802': truck stops '2803': sns '2804': robotics '2805': support '2806': service industry '2807': parking '2808': cold supply chain '2809': early education '2810': facture électronique '2811': rewards & cashback '2812': bénévolat '2813': music from body movement '2814': leadership development '2815': graphql '2816': hospital operations '2817': blog chat '2818': scientific data & cataologs '2819': loan '2820': learning '2821': video games '2822': africatech '2823': ad tech '2824': bikes '2825': mining '2826': special needs '2827': aspects mentaux '2828': '@blockchain' '2829': retail '2830': turkey '2831': deposits '2832': search '2833': base de connaissance '2834': élection législative '2835': church '2836': augmented reality (hw) '2837': recherche operationnelle '2838': shoes '2839': intranet '2840': farm analytics '2841': services à la personne & aux tpe '2842': enterprise security '2843': genomeediting '2844': imprimerie '2845': nutrition services '2846': benefits card '2847': catalogue '2848': ar display enablers '2849': flexible work '2850': bed-and-breakfast '2851': iron '2852': university of cambridge '2853': factoring '2854': access management '2855': cost optimization for cloud services '2856': internet first restaurant '2857': se faire des amis '2858': famille & société '2859': listing and recommendation '2860': 3d '2861': consumer 3d printing '2862': conseil et vente de matériel '2863': maison connectée '2864': ucaas '2865': optimization solvers '2866': doctors '2867': specialized '2868': non-wearable '2869': erp software '2870': hosting services '2871': partager '2872': art, culture '2873': event venues '2874': video creation '2875': carindustry '2876': pc and console game developers '2877': industry '2878': b2bmarketplace '2879': vibration '2880': éco-responsable '2881': arts & crafts '2882': procure-to-pay '2883': gaming tech '2884': healthcare general '2885': b2bgiftshop '2886': sustainable materials '2887': food & accessories '2888': audio/visual '2889': prix '2890': productivity software '2891': e-commerce solutions, e-commerce & marketplaces '2892': scientific & engineering applications '2893': '@ecommerce' '2894': cleaner '2895': table tennis '2896': jeux de cartes '2897': freelancers '2898': bookmarking '2899': trucks '2900': design collaboration '2901': audit digital '2902': nucleic acid analysis '2903': previsions électorales '2904': tcf '2905': individual investor '2906': energy consumption analytics '2907': crossborder teachers '2908': new materials & packaging '2909': '@deathtech' '2910': 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propriété intellectuelle '3544': factures '3545': field support '3546': templates '3547': trucking, moving & storage '3548': b2bpayments '3549': saas management platform '3550': barber shops & beauty salons '3551': drones '3552': tms '3553': energies renouvelables '3554': direct home buying '3555': switzerland '3556': e-signature '3557': poster '3558': diagnostics '3559': qualité de l'eau '3560': wearable dependent platforms '3561': national university of singapore (nus) '3562': faites du sport '3563': cours en ligne '3564': flowers '3565': voiture sans permis '3566': prévention '3567': 3d printing services '3568': mfa '3569': marque personnelle '3570': self ordering '3571': unified communications '3572': cash and liquidity management '3573': administrative consulting '3574': madagascar '3575': anti phishing '3576': taxi parisien '3577': big data infrastructure '3578': semi closed '3579': text-based '3580': laverie '3581': 3d models '3582': data protection '3583': terminal de paiement '3584': 3d printing in consumer goods '3585': asset intelligence '3586': liège '3587': agence seo '3588': accounting suite '3589': visual design, multimedia design '3590': casb '3591': tech4good '3592': pegasus '3593': clearinghouse '3594': .net development '3595': œuvres d’art '3596': document security and compliance '3597': reduction '3598': ecologie '3599': supply chain management applications (scm) '3600': greenit '3601': invisible orthodontics '3602': foodsupplement '3603': inspection services '3604': social selling '3605': social learning '3606': carbone '3607': '@socialimpact' '3608': horlogerie '3609': content - intelligence '3610': professional organizations '3611': marketplaces - mapping germany '3612': digital oilfield '3613': antivirus '3614': retail returns management '3615': climatisation '3616': stock footage '3617': cms '3618': digital health - mapping germany '3619': health benefits '3620': marketing intelligence '3621': cuisine familiale '3622': traduction juridique '3623': browser-based '3624': multi channel networks (mcn) '3625': academic programs '3626': ai in education '3627': image analysis '3628': cabinet de conseil '3629': borne de recharge électrique '3630': customer identity '3631': app-based '3632': diversified diagnostics '3633': solution de paiement '3634': medical '3635': design marketplaces '3636': property buying '3637': human resources software '3638': workflow '3639': business supplies '3640': creation de siteweb '3641': drilling '3642': vidéo '3643': building energy efficiency '3644': restaurant participatif '3645': college admissions counseling '3646': expense approval '3647': champignon '3648': private label '3649': religious '3650': corporate citizenship '3651': riskmanagement '3652': workplace '3653': angel investment '3654': '@bébés' '3655': mobilemoney '3656': computer programming '3657': garden '3658': claimsprocessing '3659': ancillary software '3660': idée de sortie '3661': diagnosis '3662': cold chain monitoring '3663': bilan de compétences '3664': other rental stores (furniture, a/v, construction & industrial equipment) '3665': wallpaper '3666': learning & development '3667': briquet '3668': capital management '3669': famille '3670': dine out '3671': active '3672': museums & art galleries '3673': location-based discovery '3674': painting '3675': promoteur immobilier '3676': risk monitoring in financial services '3677': spend management '3678': online bill payments '3679': agenda des salons '3680': dentiste '3681': asset based lending '3682': electrical '3683': artisans '3684': marketing culinaire '3685': phishing protection '3686': family focused '3687': hygiène alimentaire '3688': staffing solutions '3689': marketing participatif '3690': geschäft '3691': réputation '3692': medical centers '3693': surf '3694': refining '3695': digital billboards '3696': isolation '3697': content management & demo tools '3698': fintech & enterprise software '3699': p2p energy sharing and trading '3700': environmental sustainability '3701': bookkeeping software '3702': two-factor authentification '3703': electronic design automation (eda) '3704': design génératif '3705': diversified financial services '3706': satellite based '3707': marvinrecruiter '3708': product usage analytics '3709': management tools '3710': blockchain infrastructure '3711': transfert d'argent '3712': ess '3713': working capital loans '3714': cleaning services '3715': '@emballage' '3716': facilities management & commercial cleaning '3717': internal applications '3718': coach virtuel '3719': listing and reviews '3720': fitness center '3721': b2bfinancing '3722': loans '3723': b2bfinancialservices '3724': health & sport apps '3725': debt repair '3726': auto repair '3727': job '3728': traçabilté '3729': recrutement programmatique '3730': anti-drone '3731': customer service '3732': tech accessories '3733': multi asset '3734': pet tech '3735': online health information '3736': customer experience '3737': iot middleware '3738': quality and life-cycle tools '3739': energytech '3740': funeral homes & funeral related services '3741': merchant '3742': consumer transit applications '3743': cancer awareness '3744': e-filing '3745': internet first restaurants '3746': bureau virtuel '3747': ai-driven '3748': task management '3749': échange '3750': b2bcredit '3751': gestion it '3752': beauty e commerce '3753': m&a '3754': parts '3755': moderation de contenu '3756': metal manufacturing '3757': device management '3758': product management '3759': programme de fidélisation '3760': social news '3761': vulnérabilités '3762': progression '3763': lead generation platforms '3764': lead '3765': distilleries '3766': quote to cash '3767': retailtech '3768': asset managers '3769': estimateur immobilier '3770': corporate travel and expense cards '3771': orange '3772': zone de chalandise '3773': boats & submarines '3774': plant-based products '3775': nonprofit '3776': life insurance '3777': credit cards & transaction processing '3778': filament- based '3779': electric housewares '3780': credit bureaus '3781': crude petroleum '3782': retail & hospitality '3783': visioconférence '3784': employee surveys '3785': hrms for enterprises '3786': trading companies & distributors '3787': banque en ligne '3788': business process optimisation '3789': '@insurtech' '3790': translational research '3791': specialty hospitals '3792': cities, towns & municipalities general '3793': '@recruitment' '3794': building material '3795': petite enfance '3796': recommendation '3797': légende de la musique '3798': customer communication management '3799': sur-mesure '3800': clinical resources '3801': london business school (lbs) '3802': cloud management '3803': loyalty enablers '3804': home services '3805': fabric '3806': coffee maker '3807': financial contracts '3808': data and analytics '3809': la réunion '3810': cardiovascular diseases '3811': adult clothing '3812': building '3813': eat at home '3814': financials, diversified financial services '3815': graine '3816': nanoenabled products '3817': insurance distribution platforms '3818': ar vr in retail '3819': reputation '3820': engineering infrastructure '3821': microservices '3822': behavioral health '3823': membership pass '3824': website personalization '3825': chasse aux trésors '3826': predictive machine maintenance '3827': aidant '3828': customer service chatbots '3829': service '3830': marine services '3831': cloud computing '3832': audio guide '3833': nutraceutical '3834': sex tech '3835': facturation '3836': webcam '3837': traveltech '3838': '@onboarding' '3839': indoor farming '3840': women's health '3841': voice analytics '3842': mobiletransfers '3843': sales '3844': payment as a service '3845': electrical appliance '3846': data center automation '3847': semiconductors & semiconductor equipment '3848': '@sécurité' '3849': open source '3850': devops '3851': groves '3852': drugs '3853': spices '3854': digital assets '3855': video creation tools '3856': iot in manufacturing '3857': speech recognition '3858': energy efficiency tech '3859': matcha '3860': telecom operators '3861': waterriskmanagement '3862': skin '3863': cyber '3864': 1 hardware related sw '3865': water tech '3866': cad software '3867': ad analytics suite '3868': customer data platforms '3869': overlays '3870': site w '3871': vertical '3872': expenses sharing '3873': cross channel '3874': video editors '3875': building services '3876': customer data management '3877': marketing digital '3878': conversion rate optimization '3879': insurtech ? '3880': supply chain management (scm) software '3881': pulmonology '3882': vr & ar '3883': féminisme '3884': a b testing '3885': jewelry & watches '3886': marketing d'influence '3887': restaurants '3888': obstetrician-gynecologist '3889': business efficiency '3890': click-to-call '3891': energy intelligence '3892': tennis '3893': chez l'habitant '3894': smart city '3895': bfsi '3896': parents '3897': colleges & universities '3898': credit scoring '3899': production analysis '3900': affiche '3901': iit bombay '3902': oil & gas '3903': general '3904': customer identity access management (ciam) '3905': music production & services '3906': hand, power and lawn-care tools '3907': '@impression' '3908': museums '3909': push notification '3910': ingénierie pédagogique '3911': gas sensing '3912': suppliers '3913': bcg '3914': ride hailing '3915': plastic products '3916': intelligence collective '3917': embroidery '3918': process improvement '3919': multi-mode communication '3920': cameras '3921': écologie '3922': software release management '3923': paralegal '3924': application wrapping '3925': entertainment '3926': internet first innerwear brands '3927': glucose '3928': '@marketplace' '3929': plagiarism detection '3930': cables & wires '3931': clientmanagement '3932': community health '3933': création '3934': enterprise video editors '3935': tattoo '3936': gestion d'entreprise '3937': manufacturing tech '3938': upscale '3939': ed tech '3940': '@hrteach' '3941': entreprises '3942': user feedback tools '3943': social recruiting '3944': cacao '3945': specialized displays '3946': commercial '3947': artist crowdfunding '3948': coupons and deals '3949': pricing strategies '3950': travel agencies & services '3951': droit '3952': distributed commerce '3953': housekeeping service '3954': public order '3955': services généraux '3956': procurement management '3957': enterprise applications '3958': multi-sourced data '3959': '@tourisme' '3960': livraison entre particuliers '3961': '@gaming' '3962': echapter '3963': unmanned retail '3964': microfinance '3965': specialty retail '3966': organisation '3967': biochemical tests '3968': daily needs '3969': jeunes entrepreneurs '3970': mode enfant '3971': talent acquisition '3972': dressing '3973': stress management '3974': loan life cycle management '3975': offshoring '3976': ar vr in healthcare '3977': referral '3978': functional beverages '3979': asa '3980': datascience '3981': iot '3982': worker safety '3983': code development . '3984': developer productivity tools '3985': technical services '3986': nonresidential '3987': buildings '3988': movies & tv '3989': enterprise information '3990': data encryption '3991': detergent '3992': parenting '3993': tech innovation '3994': patch management '3995': sports betting '3996': platforms '3997': hedge fund '3998': cpa services '3999': medical devices '4000': '@superapp' '4001': foodwaste '4002': biologique '4003': content publishing '4004': oracle '4005': renewable electricity '4006': invoicing '4007': evidence-based medicine '4008': micro jobs '4009': urology '4010': papas '4011': campagnes électorales '4012': orchestration '4013': digital banking '4014': base de données b2b '4015': conservation '4016': sportech '4017': cloud access security broker '4018': hr '4019': brewing '4020': feed '4021': replay '4022': aggregates, concrete & cement '4023': roadtrip '4024': risc '4025': universities '4026': outsourcing '4027': security services '4028': industrial equipment '4029': email '4030': version control '4031': '@référencement naturel' '4032': running '4033': bpifrance '4034': ip management software '4035': launcher '4036': offline '4037': printing '4038': mobile app enablers '4039': insurance software suite '4040': video security '4041': weapons '4042': electronic components '4043': expatrié '4044': '@climatechange' '4045': distribution networks '4046': foodsupplly '4047': tv production '4048': community based shopping platforms '4049': annuaire des salons '4050': b2c grocery delivery '4051': telecom infrastructure tech '4052': maps '4053': beauté '4054': diététiciens '4055': banque '4056': maritime '4057': b2b software '4058': consommation responsable '4059': vinification '4060': software development '4061': reputation management '4062': paramétrage automatique '4063': speech and voice recognition '4064': residential solar monitoring '4065': banks '4066': brand protection software '4067': detox digitale '4068': human resources information system (hris) '4069': banking services '4070': tirage au sort '4071': physical stores '4072': brocante '4073': qehs compliance '4074': water '4075': aerial '4076': multidisciplinary '4077': courtier immobilier '4078': inventory based '4079': marketing mobile '4080': industrials & manufacturing '4081': clothing and apparel '4082': fitness & dance facilities '4083': souvenir '4084': conventional fabrics '4085': savoie '4086': garantie '4087': food products '4088': travel & leisure '4089': sharing economy '4090': circuit court '4091': journaliste '4092': dinner theaters '4093': storage device '4094': usage based insurance '4095': sondage '4096': restaurant '4097': supplychain '4098': linux '4099': 3d printing in retail '4100': messaging and telecommunications '4101': testing '4102': amusement '4103': financialservices '4104': screencast '4105': grinding wheels '4106': containers '4107': building equipment '4108': transition écologique '4109': advice and advisory services '4110': fantasy sports '4111': road transport tech '4112': on cloud '4113': parcel '4114': discount '4115': cornell university '4116': cable networks '4117': currency '4118': contract research '4119': crisis management '4120': energy management system '4121': b2c marketplace '4122': franchize '4123': location de véhicules utilitaires '4124': mergers and acquisitions '4125': dataviz '4126': fresh fruit '4127': videos) '4128': advertising campaigns '4129': sauvegarde '4130': food & beverage products '4131': financial apps '4132': électromobilité '4133': university of toronto '4134': content syndication '4135': strategic planning '4136': resources '4137': professional schools '4138': advanced analytics '4139': childcare providers '4140': web conferencing '4141': ach payments '4142': commande groupée '4143': aso '4144': property management tech '4145': food ingredients '4146': legal forms '4147': factorymanagement '4148': care facilities '4149': retail investor relations '4150': moment marketing '4151': rv '4152': parentalité '4153': 3d stereo vision '4154': digitalmedicalmanagement '4155': proposal management '4156': data quality '4157': entertainment & recreation '4158': own '4159': amazon services '4160': internet radio '4161': ecrutement '4162': calendrier éditorial '4163': retail store '4164': fraud detection in financial services '4165': art work '4166': gpec '4167': surface analysis tool '4168': audit et conseil '4169': healthcare chatbots '4170': excavating '4171': '@pertedepoids' '4172': brand management '4173': collaborative '4174': chemicals, petrochemicals, glass & gases '4175': workforce performance management '4176': ambulance services '4177': perle '4178': accessoires de mode '4179': iit delhi '4180': service digital '4181': multi disciplinary '4182': financial accounting '4183': underwriting '4184': medical purposes '4185': clinical operations '4186': location saisonnière '4187': consumer lending '4188': advanced materials '4189': batteries '4190': wharton business school '4191': wiring supplies '4192': commercial real estate tech '4193': '@poster' '4194': '@résidencesecondaire' '4195': cats and dogs '4196': lims '4197': boitier '4198': mapping '4199': relocation '4200': signage '4201': roku '4202': marketing de proximité '4203': insider threat detection '4204': brands '4205': power supplies '4206': bicycles '4207': veneer '4208': rental property '4209': medical devices & equipment '4210': plant based protein '4211': dried '4212': learning management system '4213': dairy production '4214': after sales service '4215': managed network security '4216': coffee '4217': internetprovider '4218': data privacy '4219': webv visibilité en ligne '4220': display '4221': diversified consumer services '4222': internet service providers, website hosting & internet-related services '4223': sanitation '4224': recruitment '4225': social media marketing '4226': hub social '4227': mobilier '4228': business '4229': '@tech4good' '4230': coque '4231': bus '4232': deepfake '4233': shipping broker '4234': ebooks '4235': wellness '4236': blood pressure monitoring '4237': infrastructure '4238': tech enabled service '4239': electronic health record '4240': carnegie mellon university '4241': paiement mobile '4242': réalité augmentée '4243': autonomous systems enablers '4244': customer analytics '4245': hostels '4246': issuer processor '4247': beauty '4248': prototypage '4249': gift card '4250': abonnement box '4251': purchase financing '4252': bol '4253': productivity '4254': fitness & nutrition '4255': corporate learning '4256': organicfertilizers '4257': aggregation apis '4258': bourse '4259': 3d printers '4260': motors '4261': maroc '4262': homeless shelter '4263': ammunition '4264': healthcare analytics '4265': power grid '4266': recrutement / rh '4267': développement d'entreprise '4268': osint '4269': property management suite '4270': office products '4271': référencement '4272': private market data '4273': collectibles '4274': plugins '4275': direct marketing '4276': prevention '4277': expert '4278': defiscalisation '4279': cafe '4280': renewable energy '4281': collectivité '4282': office products retail & distribution '4283': networks '4284': cbd '4285': mental health & rehabilitation facilities '4286': electronic equipment, instruments & components '4287': batch 3 '4288': eim suite '4289': workforce analytics '4290': produits variés '4291': stockage en ligne '4292': crowdfunding '4293': vertical search '4294': physiological '4295': boat '4296': continuous deployment '4297': environnemental '4298': global distribution systems '4299': demenager '4300': collections '4301': farming '4302': candidate assistance '4303': alan '4304': multi-home services '4305': bizops tracking '4306': animal grooming '4307': shopping '4308': call tracking '4309': telemarketing '4310': accounts receivable automation '4311': location tracking '4312': for payment institutions '4313': brand & retail '4314': soutien collaboratif '4315': paris sportifs '4316': photo editing '4317': healthcare booking platforms '4318': terrasse '4319': linkedin '4320': ecommerce_dtc '4321': production audiovisuelle '4322': it advisory '4323': assurance-vie '4324': second hand cars ecommerce '4325': driving schools '4326': lab management '4327': mixité '4328': evaporated '4329': frozen dessert '4330': dietary supplements '4331': système information '4332': iti '4333': computer vision '4334': tech for film & tv '4335': plastic, packaging & containers '4336': in store retail tech '4337': cold-pressed '4338': coffret cadeau '4339': streaming management '4340': reselling '4341': autonomie '4342': solutions digitales '4343': service aux particuliers '4344': carpooling '4345': architectural manufacturing '4346': religious studies '4347': data visualization '4348': gov '4349': erp pour esn '4350': '@assurance-emprunteur' '4351': chemistry '4352': '@fidelité' '4353': celebrity '4354': spice '4355': fait main '4356': design and visualization '4357': insurance services '4358': regulatory intelligence '4359': internet first jewellery brands '4360': alzheimer disease '4361': financial fraud '4362': équitation '4363': outdoor gear '4364': avocats '4365': design tools '4366': traduction médicale '4367': industry agnostic '4368': conseil sécurité '4369': social networks '4370': sports & fitness '4371': financial exchanges '4372': chouchou foulard '4373': trial compliance & quality management '4374': coursier '4375': real estate it '4376': push-to-card '4377': placement agencies '4378': defense '4379': beta '4380': mhealth '4381': communication systems '4382': flowers, gifts & specialty stores '4383': hotel accommodations '4384': tidal '4385': other cytokine receptor targets '4386': textiles '4387': leather goods '4388': studios '4389': search engine & portal '4390': organizer tools '4391': receipt management '4392': showroom '4393': planification des ressources '4394': db migration '4395': impact '4396': business support system (bss) '4397': academic research '4398': soil testing '4399': application performance monitoring for developers '4400': surveyplatform '4401': femtech '4402': payroll loans '4403': vidéos à la demande '4404': intellectual property '4405': social crm '4406': livestream '4407': smart transactions '4408': insurancecomparison '4409': food & drink '4410': civic engagement '4411': stationnement '4412': gifts '4413': generic libraries '4414': state '4415': peer-assisted '4416': sandpaper '4417': legislative bodies '4418': personalassetmanagement '4419': shipbuilding '4420': business management and planning '4421': produits gourmands '4422': security products & services '4423': oil and gas '4424': electric lamp '4425': concussion management '4426': two-wheelers '4427': real estate and construction '4428': offres '4429': database system '4430': chiens '4431': peanut butter '4432': e-commerce '4433': grantmaking '4434': cloud '4435': precision agriculture '4436': bouquets '4437': dfs '4438': sports apps '4439': credit bureau '4440': repair & maintenance '4441': mise en relation '4442': fitness & training '4443': produits français '4444': data labeling '4445': paris en ligne '4446': shuttle service '4447': webos '4448': infographie '4449': medical care '4450': souvenir sportif '4451': property appraisal '4452': restauration en entreprise '4453': expert-led '4454': project management tools '4455': restauration '4456': domain registrar '4457': boarding houses '4458': people tech '4459': human resources & staffing '4460': robotic process automation '4461': time-based '4462': hospitals & clinics '4463': gift '4464': back office services '4465': big data '4466': calculateur '4467': procurement '4468': online car rental '4469': capital market it '4470': video & dvd rental '4471': risk and compliance management '4472': sous-loueur '4473': wound '4474': superfood '4475': erp esn '4476': accident claims '4477': no-code '4478': daf externe '4479': payment gateway '4480': campaign management '4481': background checks '4482': multimedia '4483': messanger '4484': maitred'oeuvre '4485': game publishers '4486': budget optimization '4487': multi sector '4488': big data infrastructures '4489': aiops '4490': books and graphic novels '4491': digital manufacturing & operation management '4492': chronic care '4493': gasoline stations '4494': valet '4495': rooibos '4496': small businesses '4497': governance '4498': consumer applications '4499': gamification based '4500': menus '4501': content discovery '4502': mechanical services '4503': marques écoresponsables '4504': hotellerie '4505': upstream '4506': decorating '4507': robo advisors '4508': ssii '4509': business focused '4510': tva '4511': sequence analysis '4512': sales engagement '4513': pc games '4514': safety audits '4515': multi-channel '4516': live shopping '4517': internet first insurers '4518': service à domicile '4519': personalized coaching '4520': content delivery network (cdn) '4521': human computer interaction '4522': génération de leads '4523': construction materials '4524': vacation rentals '4525': fruits et légumes '4526': markeplace '4527': online resellers '4528': graphism '4529': business performance management '4530': b2b ecommerce '4531': content management '4532': avis client '4533': endpoint security '4534': product adoption '4535': telematics based fleet management '4536': online auto parts and accessories '4537': pregnancy '4538': home loans '4539': alternative investments and assets '4540': employee wellness '4541': relational '4542': rh '4543': telco '4544': '@proptech' '4545': bulk stations '4546': asset recovery '4547': darknet '4548': plastics '4549': industry agnostic applications '4550': bateau '4551': risk analysis '4552': cabinets '4553': iot devices '4554': heavy equipment '4555': professionnel de sante '4556': communication interne '4557': assuretech '4558': outils b2b '4559': gas '4560': carte '4561': business travel '4562': smart homes '4563': beauty products '4564': corn '4565': on demand '4566': '@ia' '4567': score '4568': bnpl '4569': display manufacturer '4570': prix immobilier '4571': business services general '4572': security & surveillance technology '4573': secure communication '4574': virtual '4575': grain '4576': cardiometabolic diseases '4577': drug discovery '4578': vacances '4579': retailer '4580': création site web '4581': sleep inducing devices '4582': banking service providers '4583': tranfer '4584': box mensuelles '4585': questionnaire '4586': wood chips '4587': blendedlearning '4588': software installation '4589': elecom '4590': camping '4591': medications '4592': maroquinerie '4593': theatre '4594': electric vehicle charging '4595': other travel '4596': montagne '4597': aggregators '4598': stanford gsb '4599': backend '4600': 3d displays '4601': gym '4602': storage & system management software '4603': data preparation platforms '4604': live streaming '4605': baby '4606': scheduling '4607': healthcare software '4608': soccer '4609': e-book '4610': handicap '4611': gypsum '4612': business process services '4613': e-coaching '4614': love '4615': aerial image processing '4616': climatchange '4617': gasoline '4618': landfill '4619': intelligent systems '4620': customer metrics '4621': affiliation '4622': performance and monitoring '4623': '@maisondecampagne' '4624': évaluation '4625': sto '4626': plan '4627': audiotape manufacturing '4628': esg '4629': art contemporain '4630': software libraries '4631': brosse a dent '4632': made to order '4633': histoire '4634': 1 fintech '4635': rendez-vous '4636': pointage '4637': customer-facing '4638': b2blending '4639': cuisine '4640': conferences '4641': nft '4642': accounts receivables '4643': plumbing '4644': durable goods '4645': open banking '4646': collection '4647': comparateur '4648': other education applications '4649': operational processing '4650': accompagnement '4651': business lending '4652': kick scooters '4653': it operations '4654': content marketing '4655': industrial internet '4656': remote work tools '4657': staking crypto '4658': food '4659': email marketing '4660': music lessons '4661': relation investisseurs individuels '4662': smart mobility '4663': travel and tourism '4664': online learning '4665': survey tools '4666': telcos '4667': real estate finance '4668': cab ride-hailing '4669': diversity and inclusion '4670': appel d'offres '4671': jeu mobile '4672': computational drug design '4673': pcr '4674': b2b marketing & services '4675': teenagers '4676': railway '4677': pollution control '4678': reconnaissance de monuments '4679': cohort 2.0 '4680': séminaire '4681': alcoholic '4682': supply chain management '4683': page '4684': mastering '4685': communication '4686': entretien '4687': contract creation '4688': elementary '4689': cobot '4690': menu planning '4691': city '4692': online translation services '4693': beauty tech '4694': fruit & vegetable '4695': fmcg '4696': recettes '4697': risk & compliance management '4698': financialadvisory '4699': services aux entreprises '4700': metals & mining '4701': voyage immobile '4702': study implementation '4703': photography '4704': developer platform '4705': étudiant '4706': weight & health management '4707': tracker gps '4708': vidéo 3d '4709': offline classes '4710': salon booking '4711': wearables for diabetes monitoring '4712': glazing '4713': ai frameworks '4714': location analytics '4715': turbine '4716': paymenttransaction '4717': freight service '4718': algo trading '4719': money transfer and remittances '4720': internet first auto insurers '4721': ar/vr '4722': crowdsourcing '4723': auctions '4724': full-stack system '4725': foodtech '4726': job boards '4727': accountability '4728': fidélisation '4729': mode '4730': buckles '4731': multi sender '4732': ged '4733': '@tryon' '4734': visual aids for blindness '4735': écran géant led '4736': engine equipment '4737': social ads '4738': photographie '4739': reconnaissance de revenu '4740': scm software '4741': application platforms '4742': other '4743': iam '4744': online grocery '4745': fintech (starting to look at devtools '4746': student resources '4747': arts & culture '4748': postioning '4749': 'education: online training' '4750': ecommerce technologies '4751': regie '4752': emerging markets '4753': embedded analytics '4754': assistance vocale '4755': quantum hardware '4756': bespoke design & manufacturing '4757': plastics and rubber manufacturing '4758': second hand goods '4759': tele-consultation '4760': maintenance & repair '4761': grease '4762': cadres dirigeants '4763': locataire '4764': shared '4765': component materials '4766': industry vertical '4767': modeling '4768': mobileinsurance '4769': lending-as-a-service '4770': inventory optimization '4771': image processing & mapping '4772': banking as a service '4773': venture capital funds '4774': service consommateur '4775': specialty '4776': financial risk '4777': immatériel '4778': roscas '4779': image and video analytics '4780': montre connectée '4781': demo tools '4782': electric vehicle '4783': autonomous '4784': check in '4785': dec 19 '4786': online pharmacies '4787': tax preparation '4788': digital ooh '4789': decentralised marketplace '4790': adtech '4791': care planning '4792': messaging marketing '4793': diagnostic stratégique '4794': print matching services '4795': paper goods '4796': human '4797': '@web3' '4798': anti fraud '4799': fortified juice '4800': vacation rental management '4801': simple '4802': music label '4803': outdoor structures & accessories '4804': huissier de justice '4805': eventsmanagement '4806': account opening and onboarding '4807': hr & staffing '4808': '@seo' '4809': élection présidentielle '4810': public transport '4811': '@remote' '4812': building materials '4813': dpu '4814': home energy monitoring '4815': content - models '4816': simulation '4817': collaboration suite '4818': miniwarehouses '4819': distribution & performance analytics '4820': pet wearables '4821': santé sécurité au travail '4822': future fifty '4823': agent sécurité '4824': financing services '4825': nanophotonics '4826': tool that enables engaging '4827': relance et suivi client '4828': customer identity verification '4829': freelancer marketplace '4830': energy storage tech '4831': bijoux '4832': publicité '4833': lending '4834': employee perks '4835': meeting management '4836': ai infrastructure '4837': mathématiques '4838': continued learning for professionals '4839': disability services '4840': '@eau' '4841': statistique '4842': document '4843': product research '4844': kids '4845': trade '4846': cohort 8.0 '4847': fashiontech '4848': ssl '4849': référencement local '4850': social club '4851': softwood '4852': internet first prepaid card issuers '4853': lgbt '4854': garden supplies '4855': '@csr' '4856': it services '4857': multi-brand '4858': online gaming '4859': accounting solutions '4860': social impact '4861': data privacy software '4862': dressage '4863': securing payments '4864': growth stage '4865': lawn '4866': water purification '4867': decarbonization '4868': patent analytics & management '4869': edutainment '4870': data management '4871': mom '4872': videos '4873': candy '4874': secondaire '4875': e-learning, e-learning '4876': ides and frameworks '4877': éco responsable '4878': fraud '4879': échange d'argent '4880': multi genre '4881': sauces pickles and condiments '4882': p2p marketplace '4883': allumette permanente '4884': allergies '4885': music tech '4886': real estate tech '4887': plant based '4888': adult '4889': enceinte '4890': boulangerie '4891': enterprise networking '4892': broadcasting '4893': search engine optimization '4894': iotplatform '4895': building automation system '4896': lingerie '4897': mindmapping '4898': chatgroup '4899': childcare '4900': data-sharing '4901': aerospace manufacturing '4902': dépannage '4903': smart bands '4904': medical supplies '4905': aftermarket service '4906': hedgefunds '4907': build tools '4908': adgm '4909': imagerie '4910': transport tech '4911': autonomous checkout solutions '4912': language '4913': power conversion & protection equipment '4914': multimedia, games & graphics software '4915': massage '4916': generation y '4917': inc 5000 '4918': data analytics '4919': service robots '4920': decor '4921': video advertising '4922': cours '4923': men '4924': drop shipping '4925': cracker '4926': paristech entrepreneurs '4927': étude de marché '4928': cotransportage '4929': government contracting '4930': video on demand (vod) '4931': paper mills '4932': online courses '4933': controle '4934': talent '4935': data visualisation '4936': solar energy tech '4937': mentorat '4938': smart fitting solutions '4939': asset backed loans '4940': sight '4941': data anonymization '4942': marketing resource management '4943': blueprint '4944': small and medium businesses '4945': gambling & casinos '4946': citoyen '4947': electric kick scooters '4948': developer tools (outil pour développeurs) '4949': private company data '4950': colis '4951': automated underwater vehicles '4952': vehicular traffic '4953': m2m '4954': construction contractors & services '4955': smart cities '4956': immersive marketing '4957': online tutoring '4958': residential '4959': radlad '4960': fitness and diet apps '4961': meals '4962': threat intelligence '4963': iot apps '4964': bitcoin '4965': 3d printing in healthcare '4966': '@plateforme' '4967': e-government '4968': health & wellness '4969': mobile communication '4970': claims processing software '4971': architectural '4972': hotels '4973': retailers '4974': activity trackers '4975': dsi '4976': talentmanagement '4977': distribution platform '4978': multi-channel sellers '4979': sales sw '4980': fun '4981': medtech '4982': '@menus' '4983': éclairage '4984': '@agrinea' '4985': audit '4986': machine learning platforms '4987': tokens '4988': elevators '4989': techprofile '4990': bien-être financier '4991': laser based '4992': ott subscription sharing '4993': social advocacy '4994': investigative services '4995': electrical installations '4996': mcrm '4997': drug '4998': box '4999': desktop '5000': aviation '5001': citrus '5002': big data in financial services '5003': proximity marketing '5004': data integration '5005': freelancing '5006': préservation du patrimoine culturel '5007': vat '5008': acoustics '5009': supplier collaboration '5010': datamanagement '5011': monitoring devices '5012': care coordination '5013': particulier '5014': design model '5015': habillement '5016': water transportation '5017': digital guest directory '5018': sextech '5019': home health care '5020': vehicle subscription '5021': through bank partnerships '5022': booking platforms '5023': music concerts '5024': dance '5025': marketing intelligence. '5026': operations and workflow '5027': investment '5028': military '5029': immobilier '5030': blockchain application development platforms '5031': rehabilitation '5032': solar '5033': data-driven marketing '5034': application security '5035': design services '5036': record production '5037': travel accommodations '5038': personalisation '5039': sneakers '5040': missions '5041': piano learning '5042': gig employer it '5043': internet-first platforms '5044': accounting services '5045': alternative lending '5046': internet first credit card issuers '5047': inner wear & night wear '5048': intelligence numérique '5049': storage engines '5050': dégustation de vin '5051': web & client portals '5052': classifieds '5053': music creation it '5054': house call '5055': review & recommendation '5056': point of sale '5057': france '5058': pdf tools '5059': water treatment '5060': managed home rentals '5061': smart contracts '5062': dataquality '5063': abonnements '5064': kpi monitoring '5065': facilitated consulting networks '5066': franchise '5067': feedback platforms '5068': cisco '5069': hoses '5070': cargo delivery '5071': b2cfinancing '5072': fast turnaround '5073': startup studio '5074': internet first lenders '5075': sea '5076': eyewear e-commerce '5077': usage-based insurance '5078': complaints resolution '5079': spm '5080': crypto monnaie '5081': search engines & internet portals '5082': residential real estate tech '5083': visite '5084': sportoccasion '5085': marketing analytics '5086': electric vehicles '5087': garden equipment '5088': groupe de musique '5089': industry 4.0 '5090': wind energy '5091': content & media '5092': sustainability & compliance '5093': mytribunews '5094': objet '5095': '@cloud' '5096': hydroalcoolique '5097': cheval '5098': agence web guadeloupe '5099': marketing, sales & marketing '5100': loyalty programs '5101': innovant '5102': quantum computing '5103': social gaming '5104': virtualization '5105': e-commerce logistics '5106': steel wire '5107': customer loyalty program '5108': machine learning components '5109': janitorial service '5110': integrated risk management '5111': ussd '5112': relamping '5113': mining technology '5114': presentations '5115': guide privé '5116': dev tools '5117': lifestyle brands '5118': intellectual property (ip) management '5119': e-réputation '5120': product search '5121': environmental applications '5122': bioinformatics '5123': agent '5124': web content management system '5125': bagage '5126': investor relationships management '5127': voiture '5128': pilotage de trésorerie '5129': smb payments '5130': developer platforms '5131': immoblier '5132': valise '5133': for businesses & fis '5134': salon virtuel '5135': elearning '5136': station based '5137': midjourney '5138': table de capitalisation '5139': aggregator '5140': internet connectivity '5141': sponsorships '5142': process control '5143': cement and concrete '5144': consumer hardware '5145': performance testing '5146': hds '5147': comics '5148': temporary shelters '5149': condensed '5150': p2p power trading '5151': gamification '5152': content management systems '5153': continuing education chains '5154': ml '5155': console games '5156': facebook '5157': structured data management '5158': jean '5159': '@maintenance' '5160': industrie '5161': sleep management '5162': procure to pay '5163': wholesale '5164': site vitrine '5165': urban transit '5166': hiring services '5167': valves '5168': fooddelivery '5169': b2b second hand cars marketplaces '5170': ders management '5171': carrentalplatform '5172': code quality '5173': food truck '5174': information and communications technology (ict) '5175': security and alarm systems '5176': facility management '5177': biscuits salés '5178': juices and dilutables '5179': privacy '5180': sewage '5181': internet first apparel brands '5182': sme loans '5183': user authentication '5184': chambers of commerce '5185': microsoft '5186': stratégie électorale '5187': reits '5188': integrated video analytics '5189': digitalisation '5190': sharing '5191': encyclopédie '5192': patio '5193': social - patient to expert '5194': publishers '5195': ocean '5196': courriel '5197': dé-métallation '5198': ai-based '5199': record, video & book stores '5200': photographic & optical equipment '5201': tape recorders '5202': crowdtiming '5203': backup '5204': norms & legal '5205': reporting '5206': full range '5207': toit '5208': newspapers & news services '5209': floor covering '5210': home monitoring '5211': visite virtuelle 360 '5212': creative arts '5213': insurance underwriting and risk management '5214': zero emissions '5215': metal merchant '5216': wallet '5217': integrated circuits '5218': daytime '5219': marketplace for farmers '5220': deeptech '5221': valorisation '5222': pc cloud '5223': clothing '5224': passenger service systems '5225': air quality and filtration '5226': services informatiques '5227': fdj '5228': vote numérique '5229': blood pressure '5230': identity theft '5231': travel agency '5232': pilotage rse '5233': out of home '5234': box par abonnement '5235': mmobilie '5236': travel '5237': societegeneral '5238': hebdomadaire '5239': silverware '5240': comparator '5241': instant messengers '5242': healthcare products '5243': geodetic '5244': radio stations '5245': magazine '5246': administrative services '5247': optical communication '5248': general purpose '5249': gestion de contenu '5250': heavy industries '5251': chirurgie esthetique '5252': p2p wallet remittance '5253': corporate travel and expense management '5254': pet care '5255': computer aided facility management '5256': mamans '5257': foulard cheveux '5258': water monitoring '5259': hair care '5260': tablette '5261': antibody engineering '5262': revenue management '5263': application development platforms '5264': wealth management '5265': googlestreetview '5266': food and beverages '5267': in-flight entertainment '5268': electric power '5269': wine and spirits '5270': talent agencies '5271': consumer electronics e-commerce '5272': microsoft 365 '5273': music venues '5274': removable '5275': hydraulics '5276': autopartage '5277': therapeutic devices '5278': behavioral '5279': alcohol '5280': clienteling '5281': home based '5282': cloud data services '5283': barber '5284': analysis '5285': music stores '5286': transparence '5287': agenda '5288': cardiac '5289': women's cancer '5290': cross platform '5291': rbf '5292': location '5293': b2g '5294': organic food '5295': glass & clay '5296': psychologie '5297': utility trailer '5298': bigdata '5299': hospitalmanagement '5300': mckinsey '5301': coaching '5302': '@goponic' '5303': déplacements '5304': account receivables '5305': organized labor '5306': resilience '5307': audio accessories '5308': clean energy '5309': signature en masse '5310': learning management systems '5311': pathology '5312': office machinery '5313': alimentation saine et durable / agriculture raisonnée '5314': podcasting tools '5315': hyperaccumulator '5316': research services '5317': investir '5318': construction collaboration '5319': forum '5320': pay-per-collection '5321': headless content management system '5322': externalisation informatique '5323': crowdfunding immobilier '5324': smblending '5325': mailing list '5326': travel & tourism '5327': commercial art '5328': aircraft '5329': creative communication '5330': seafood '5331': accounting & fp&a '5332': regulatory reporting '5333': experienced professionals '5334': real estate '5335': logiciel '5336': participatif '5337': surveillance '5338': uber '5339': electric lighting '5340': rdpress '5341': ai '5342': générateur '5343': pressing '5344': automotive industry '5345': bien être '5346': outils de gestion '5347': mutuelle '5348': private market investing '5349': algorithm '5350': soft skills '5351': patient treatment '5352': it infrastructure management '5353': rail, bus & taxi '5354': ceramic '5355': cervical cancer '5356': 3 travel '5357': traditional '5358': testing types '5359': montessori '5360': memorabilia '5361': real '5362': backup & recovery '5363': nickel '5364': petit électroménager '5365': backup power '5366': transcription '5367': exhibits '5368': roaming '5369': enterprise social networking '5370': grc solutions '5371': hair care and styling products '5372': digital '5373': farm monitoring and management '5374': growth hacking '5375': ethyl alcohol '5376': nosql '5377': industrial design '5378': web analytics '5379': supply chain & logistics '5380': fertility '5381': sw infrastructure '5382': home improvement & hardware retail '5383': datacenter '5384': information technology '5385': outpatient care '5386': employeebenefit '5387': content - multimedia '5388': medical data & billing '5389': transport '5390': scientific '5391': clinical labs it '5392': communication financière '5393': livraison express '5394': personalized learning '5395': claims adjusting '5396': coliving '5397': gamified '5398': bien être au travail '5399': jeunesse '5400': tarification '5401': optimisation pour les moteurs de recherche '5402': prévision des ventes '5403': moteur de recherche '5404': athletes '5405': agence digitale '5406': online loan comparison platforms '5407': board meetings '5408': kinésithérapie '5409': portrait '5410': remote collaboration '5411': paper '5412': cyber risk & vulnerability assessment '5413': information sensible '5414': school solutions '5415': environmental innovation '5416': investment platforms '5417': professional community '5418': bi & analytics '5419': explosives '5420': it rese '5421': reproductive health '5422': clinical care '5423': stratégie digitale '5424': vente directe '5425': recommandation '5426': oilseed '5427': focus group '5428': expertise '5429': publishing '5430': outsourced '5431': connectivité '5432': scientific applications '5433': vente à réméré '5434': app or video based training '5435': lidar '5436': configure price quote software '5437': commercial lending '5438': video streaming '5439': hatcheries '5440': classified '5441': interactivité '5442': functional skills '5443': blue collar '5444': threat detection '5445': credit analysis '5446': online trading platforms '5447': chatbot '5448': appointment booking '5449': privacy and security '5450': sustainability management '5451': compensation carbone '5452': omnichannel solutions '5453': hifi '5454': tech enabled feedback service '5455': metier tech '5456': athletics '5457': industrial services '5458': microlearning '5459': applications '5460': consulates '5461': real estate marketing software '5462': animal feed '5463': workplace safety management '5464': justice '5465': cem '5466': independent property '5467': general medical '5468': amputees '5469': venturelab portfolio '5470': cabinet de recrutement tech '5471': private funds it '5472': digital diagnostics platforms '5473': cloud security '5474': formation '5475': perception '5476': pharmacy '5477': online flashcards '5478': intervenant scolaire '5479': alumni '5480': energy '5481': alm '5482': fluid control '5483': marketplaces '5484': ia '5485': cahier de vacances '5486': merchantservices '5487': seo '5488': application software '5489': pool & spa monitoring '5490': decision support '5491': electric scooter manufacturers '5492': navigation systems '5493': touchless '5494': international trade '5495': casino en ligne '5496': food manufacturing '5497': piscine '5498': wedding tech '5499': materials recovery '5500': hub '5501': smb accounting '5502': wireless communications '5503': market research tools '5504': all images - ai '5505': trottinette électrique '5506': applications mobiles '5507': consumer credit '5508': technology hardware, storage & peripherals '5509': germany - enterprise saas '5510': laboratory instrument '5511': handmade '5512': q&a '5513': face & expression recognition '5514': game development '5515': humanitaire '5516': crowd-sourced collection '5517': enterprise storage '5518': household '5519': location vêtements '5520': jeu '5521': energy access '5522': santé mental '5523': oem and supplier management '5524': cross-channel marketing '5525': radiothérapie '5526': quantum '5527': person tracking technology '5528': health centers '5529': iot infrastructure '5530': life '5531': ai in auto '5532': medical device '5533': données en magasin '5534': '@crypto' '5535': energy harvesting '5536': localisation '5537': legal analytics '5538': commercial bakeries '5539': visual communication '5540': application development tools '5541': home decor '5542': luster pigments '5543': video-sharing '5544': site e-commerce '5545': ambulance '5546': lecture automatique de documents '5547': online car rentals '5548': multi account card '5549': management hybride '5550': accounting & accounting services '5551': outils pour entrepreneurs et créateurs '5552': assistante virtuelle '5553': fota '5554': pc and console gaming '5555': illustrations '5556': technology enablers '5557': time metrology '5558': emballage '5559': process simulation '5560': owner focused '5561': remoteworking '5562': '@social' '5563': blockchain in retail '5564': meuble sur mesure '5565': car subscription service '5566': particulier à particulier '5567': family services '5568': assurance auto '5569': eye care '5570': data visualization & dashboarding (discovery) '5571': place de marché '5572': electric utilities '5573': mariage '5574': catering '5575': agencement '5576': affichage dynamique '5577': discovery platform '5578': douane '5579': inns '5580': green business '5581': webbrowser '5582': patients experts '5583': sme '5584': geocaching '5585': self-care '5586': archeology '5587': cryptocurrency remittance '5588': banking channels '5589': research & development '5590': femalefounder '5591': impact - mapping germany '5592': presse écrite '5593': covid-19 '5594': oncology - ai '5595': content creation & management '5596': architectural planning '5597': personal services '5598': rse '5599': equity administration '5600': lending services '5601': smart '5602': women's '5603': basketball '5604': surgical hospitals '5605': septic tank '5606': modeling language '5607': '@santé' '5608': phishing awareness '5609': performance reporting & accountability '5610': second hand '5611': bio '5612': dating '5613': digital wallets '5614': risk management '5615': bar '5616': haptic technology '5617': courts '5618': reserver taxi paris 75 '5619': isp '5620': field force automation '5621': hr management, hrtech '5622': '@algorithme' '5623': economie '5624': e-commerce payment solutions '5625': musées '5626': fakesmtp '5627': tournoi '5628': financial databases '5629': cross-border payments '5630': naturel '5631': judiciary '5632': consumer payments '5633': baas '5634': next40 '5635': emergency care '5636': e-learning '5637': navigation and mapping '5638': office management '5639': femme '5640': cybersecurity '5641': support services '5642': credit builder '5643': consumer software '5644': commercial real estate '5645': serrurerie '5646': irm '5647': network attached storage (nas) '5648': health & safety '5649': field bean '5650': hotels & resorts '5651': ocean or air '5652': software + hardware '5653': racing sports '5654': public social networks '5655': jewelry, watches & luxury goods '5656': medicalservices '5657': image & video analytics '5658': e-commerce sellers '5659': soil analysis '5660': teaching '5661': financialmanagement '5662': search engine optimisati '5663': plastic bottles '5664': etc..) '5665': btob '5666': design software '5667': b2b farm produce e-commerce '5668': streaming analytics '5669': corporate charged cards '5670': alternative investment platforms '5671': recettes de famille '5672': neuroimaging '5673': potager '5674': applicant testing platforms '5675': 8 others '5676': soil preparation '5677': machine learning and natural language processing. '5678': '@toulouse' '5679': genetics '5680': consumer services '5681': personalbranding '5682': impact investments '5683': radium '5684': credit & payments cards '5685': professional networking '5686': on-site '5687': insights '5688': digital signage '5689': converter '5690': logiciel de caisse '5691': cpv '5692': penetration testing '5693': kyc '5694': afrique '5695': consignment '5696': unified communication '5697': temples '5698': motor vehicles '5699': politique de confidentialite '5700': bio-eclairage '5701': intelligent processing unit '5702': extension '5703': chemicals & related products '5704': financial education '5705': courtage assurance '5706': supplier management '5707': lithium ion '5708': shop '5709': bus pooling '5710': smartbuilding '5711': energy exploration '5712': scooters '5713': data science platforms '5714': remboursement '5715': medicines '5716': vertical job boards '5717': '@fintech' '5718': sporting products '5719': geographic information systems '5720': lead generation '5721': marinas '5722': manager '5723': surgical navigation systems '5724': training '5725': omnichannel '5726': internet first hair care brands '5727': self-employment '5728': ecommerce '5729': podcasts '5730': diagnostic software '5731': healthy meals '5732': personal trainers '5733': device '5734': marchand de biens '5735': marketplace '5736': bank holding '5737': esn '5738': electronics repair '5739': fashioneditorial '5740': travel trailer '5741': journalism '5742': cooking '5743': art de vivre '5744': trade agents '5745': digitalization '5746': entertainers '5747': other industries '5748': biologie '5749': open source databases '5750': credit risk '5751': humanities '5752': vin '5753': baseball '5754': electricity, oil & gas '5755': contracts management '5756': local payments infrastructure '5757': headless cms '5758': travail '5759': trading & investment '5760': marine technology '5761': automotive maintenance '5762': tbd '5763': courtier '5764': creditscoring '5765': pourboire '5766': '@pub' '5767': cash-based '5768': digital immobilier '5769': film production '5770': livres '5771': public safety '5772': group buying '5773': genomics for drug discovery '5774': art du prompt '5775': industrie4.0 '5776': live music tech '5777': clé '5778': security software '5779': saisie terrain '5780': commerce enabled '5781': information services '5782': autonomous vehicles technologies '5783': stock photo platforms '5784': energy management '5785': day spas '5786': money transmission '5787': qr code '5788': vps '5789': advertising & marketing '5790': fintech opps - financial operation management platform '5791': abonnement couches '5792': container '5793': erphospital '5794': jewelry & watch retail '5795': spring and wire '5796': martinique '5797': wired '5798': investment tools '5799': storage services '5800': flexible '5801': oil '5802': '@maman' '5803': mobile edge computing (mec) '5804': process automation '5805': oil & gas tech '5806': pigment '5807': business services '5808': dépannage à domicile '5809': compliance & certifications '5810': financial & accounting '5811': peau '5812': industry focused '5813': optimisation du bfr '5814': heavy equipment rental '5815': tech enabled services '5816': post-production '5817': femmes '5818': groceries '5819': colleges '5820': éthique '5821': b2c e-commerce '5822': container management '5823': investisseur. '5824': telco & networks '5825': pprentissage '5826': women '5827': '@retail' '5828': content and publishing '5829': data mining '5830': home care products '5831': enterprise resources management applications '5832': télémédecine '5833': '@deux-roues' '5834': therapies '5835': pos payment terminals '5836': loi de finance 2020 '5837': couriers '5838': '@carboncapture' '5839': secondhand '5840': fundraising '5841': conventional vcs '5842': data paas '5843': click-to-video '5844': consumer and audience intelligence '5845': internet first life insurers '5846': agroalimentaire '5847': investment management '5848': intelligence émotionnelle '5849': os '5850': audiotapes '5851': marketing relationnel '5852': pricing optimization '5853': analytics and reporting '5854': care planning & management '5855': b2b2c '5856': véhicules connectés '5857': engagement '5858': development & analytics platforms '5859': connecter '5860': lesson planning '5861': website builder '5862': pay as you go '5863': nightlife '5864': cabinet de recrutement digital '5865': crash reporting '5866': photonics '5867': home management solutions '5868': flight '5869': online photos '5870': internet first insurance platforms '5871': pallet '5872': dormitories '5873': data security '5874': off-site '5875': blue collar job boards '5876': adventure activities '5877': hr technology '5878': novelty '5879': leads formation cpf '5880': véhicule hybride rechargeable '5881': industries '5882': pet supplies '5883': dom-tom '5884': iot hardware '5885': onboarding '5886': semantic search '5887': beverages '5888': mass transit '5889': chat '5890': logo maker '5891': copyright '5892': transit systems '5893': metering management '5894': men & women '5895': jeux éducatifs '5896': voice user interfaces '5897': révolution alimentaire '5898': newsql '5899': fossil fuels '5900': digital identity '5901': music composition '5902': health tech '5903': application mobile '5904': genitourinary '5905': radio '5906': marketing management '5907': '@bienêtreanimal' '5908': for agents & landlords '5909': business ethics '5910': content distribution '5911': reconditionné '5912': property & casualty '5913': communications '5914': 3d technology '5915': commander '5916': transmission '5917': organizations '5918': male fertility '5919': socialcommerce '5920': receivables management '5921': screen-based '5922': atelier '5923': army '5924': cycling '5925': hologramme '5926': score de santé '5927': learning resources '5928': metals and alloys '5929': code review '5930': tile '5931': project stardust '5932': crop farming '5933': réunions '5934': '@puericulture' '5935': data '5936': card linked '5937': waste treatment, environmental services & recycling '5938': specialized consumer services '5939': freelance tech professionals marketplace '5940': electricians '5941': accompagnement pme '5942': travel & ticketing '5943': wearables '5944': hospitals & physicians clinics '5945': art & luxury '5946': online personal shopper '5947': mobilitymarketplace '5948': home and garden '5949': surfing '5950': app based payment '5951': blog '5952': horizontal suite '5953': logiciel erp '5954': cheminformatics '5955': room booking '5956': consumer discretionary '5957': motor vehicle parts '5958': ims '5959': joaillerie '5960': hi-tech libraries '5961': solid waste '5962': consumer finance '5963': insurance comparison platforms '5964': food and beverage distribution '5965': tokenisation '5966': smart products '5967': solution réseau '5968': virtuel '5969': waste management '5970': porte-manteaux '5971': 5 flash & private sales '5972': propriétaire '5973': cad '5974': machinery '5975': hog '5976': after school programs '5977': travel & hospitality '5978': advanced tech '5979': institutional furniture '5980': marketing optimization '5981': fabrication en ligne '5982': dyslexie '5983': software '5984': sporting & recreational equipment retail '5985': maintien à domicile '5986': freights data '5987': vital signs '5988': charter schools '5989': stanford university '5990': marketing data platforms '5991': industrial paper supplies '5992': alcoholic beverage e-commerce '5993': civil engineering and natural resources applications '5994': interactive ads '5995': p2p remittance '5996': smart grid '5997': iast '5998': économiecirculaire '5999': poc devices '6000': mobile ad network '6001': luxury '6002': motor vehicle '6003': assurance santé '6004': maas '6005': financial investment '6006': mairie '6007': passenger car '6008': public safety management '6009': motor vehicle dealers '6010': digital native banks '6011': banking tech '6012': nature conservation '6013': metal container '6014': collecte de fonds '6015': logo en ligne '6016': gambling '6017': illectronisme '6018': cognitive automation '6019': itsm '6020': financialplatform '6021': hacking '6022': continuous integration '6023': 6 subscription commerce '6024': découpe et gravure laser '6025': gratuite '6026': recommendations '6027': hybride '6028': accounting '6029': immersive learning '6030': e-mail marketing '6031': kyc solutions '6032': neurology '6033': hcm '6034': voyage '6035': maternity care '6036': smbs '6037': productivity tools '6038': parrainage '6039': remittance '6040': car sharing '6041': ondemand marketplace '6042': néo-agence '6043': fromages '6044': navigation '6045': '@robotique' '6046': operating system (os) '6047': it monitoring '6048': shipping '6049': eolien '6050': apparel & accessories retail '6051': feature flags '6052': virtual workforce '6053': partage '6054': radio access network (ran) '6055': relations presse '6056': analytics, advanced analytics '6057': tech '6058': itom '6059': hydroelectric '6060': diabetes mellitus '6061': filtration systems '6062': film '6063': nature parks '6064': web search '6065': alternative à l'achat '6066': ai in agriculture '6067': finance / fintech '6068': drug stores & pharmacies '6069': customer success platform '6070': assisted '6071': hrtech '6072': airlines, airports 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discovery & visualization '7083': gestiontechniques '7084': sports tech '7085': pet training '7086': advanced metering infrastructure '7087': rencontre '7088': recurring payments '7089': online video management '7090': fertility monitoring '7091': jewelry '7092': home improvements '7093': customer service and engagement '7094': wallbox '7095': art génératif '7096': consumerloans '7097': '@bol' '7098': online platform '7099': traduction littéraire '7100': cashlesspayment '7101': tax management '7102': hotel management, hotels '7103': complementary timing '7104': code management '7105': sound recording '7106': movie theaters '7107': paymentsolutions '7108': gesture recognition '7109': vetements '7110': sales and marketing '7111': '@vêtements' '7112': contract analytics & due dilligence '7113': game developers '7114': artificial intelligence and machine learning '7115': ehealth '7116': luggae & travel gear '7117': account reconciliation '7118': content & collaboration software '7119': '@assurance' '7120': medical lab assistant software '7121': law firms & legal services '7122': social sciences '7123': partage de documents '7124': adtech (media buying & planning tools) '7125': reservation and ordering '7126': ai in insurance '7127': eyecare '7128': produits ménagers '7129': '@ecologie' '7130': video campaigns '7131': gestion administrative '7132': coo '7133': mpp '7134': automotive service & collision repair '7135': kitchen '7136': lending as a service '7137': freemium '7138': maman '7139': source to pay software '7140': society '7141': games '7142': product recommendation '7143': relecture '7144': test email '7145': partage de la valeur '7146': smart classroom solutions '7147': it compliance '7148': non-profit & charitable organizations '7149': telecommunication equipment '7150': automotive parts '7151': wix '7152': mode ethique '7153': networking equipment '7154': dématérialisation '7155': apps & mobile '7156': computer vision platform '7157': capteur '7158': independent power producer '7159': directeur financier externalisé '7160': actualités '7161': packaging '7162': contract '7163': for students '7164': contests '7165': webdesign '7166': local community '7167': defense & security '7168': newsql database '7169': elderly '7170': country clubs '7171': remote '7172': jobbing '7173': outdoor '7174': prud'hommes '7175': '@animaux' '7176': téléexpertise '7177': ap '7178': multilingual '7179': digital tokens '7180': slack '7181': sleep products '7182': collaborative photo sharing '7183': sauce '7184': interactive media '7185': employer insurance '7186': traceability '7187': smart utilities '7188': phone '7189': fishery '7190': shipping & logistics '7191': commodity '7192': data center '7193': process rh '7194': agence web '7195': energies intelligentes '7196': airdrop '7197': cross-channel '7198': product '7199': biomarkers '7200': '@bitcoin' '7201': rps '7202': gestion de parc '7203': daily deals '7204': uberisation '7205': business intelligence '7206': selfie '7207': audio accessibility '7208': computer '7209': platform '7210': click-to-chat '7211': pre-k edtech '7212': wineries & breweries '7213': ad targeting '7214': propulsion '7215': kidney dialysis '7216': ticket sales '7217': solution métier '7218': news & media '7219': vente interactive '7220': commercial construction '7221': savewater '7222': sem '7223': bike '7224': organic '7225': media (text '7226': iit kanpur '7227': growth '7228': translation '7229': logo '7230': optometrists '7231': miscellaneous building materials (flooring, cabinets, etc.) '7232': motion design '7233': space exploration '7234': cashback '7235': mobilebanking '7236': audiovisual services '7237': organs and blood '7238': provider '7239': desktop video games '7240': presentation '7241': nigeria '7242': management d'office '7243': distribution / retail '7244': internet first term loans for businesses '7245': organizers '7246': job board '7247': white label solutions '7248': ticket de caisse '7249': iot applications '7250': alternative data providers '7251': staffing '7252': teleconsultation '7253': point of sale financing '7254': '@cashback' '7255': sport en entreprise '7256': advertising platforms '7257': field sales productivity '7258': flour '7259': grocery & groumet food '7260': paper & forest products '7261': prepaid cards '7262': cash advance '7263': content delivery network '7264': enchère '7265': in game virtual assets '7266': assessment '7267': multicategory '7268': photocopying '7269': evenementiel & communication '7270': urgences '7271': text analytics '7272': pharmaceuticals '7273': digitalbanking '7274': convention '7275': wms '7276': sensors '7277': research applications '7278': food recipe analytics '7279': sales force automation '7280': audit management system '7281': hmo '7282': drycleaning '7283': recipe box '7284': truckmanagement '7285': radar '7286': laser '7287': call center '7288': light bulbs '7289': ctms '7290': household durables '7291': '@jeuxvideo' '7292': legal software '7293': commercial property '7294': industrial 3d printers '7295': beet sugar '7296': airport '7297': fish '7298': tourisme urbain '7299': class action '7300': wood processing '7301': réservation '7302': ugc '7303': practice management '7304': online retail '7305': '@abonnement' '7306': funds '7307': amr '7308': sexual education '7309': hébergement web '7310': therapeutics '7311': l'oréal '7312': frais professionnels '7313': relation investisseurs '7314': fragrance '7315': bilan carbone '7316': professional services '7317': 0 content '7318': data processing '7319': apps '7320': construction management '7321': document preparation '7322': couple '7323': '@data' '7324': bsa '7325': group savings '7326': leglatech '7327': nanotools '7328': traitement automatique du langage '7329': biotech r&d '7330': reconditionnement '7331': hardwa '7332': low-code '7333': numérique au service de l'environnement '7334': metal ore '7335': market '7336': swipe '7337': conservation patrimoine culturel '7338': brickandmortar '7339': digital lending '7340': commercial services & supplies '7341': biophysical and physiological sensors '7342': online freight forwarders '7343': economie circulaire '7344': financial benefits '7345': same day delivery '7346': ios development '7347': content creators '7348': operations intelligence '7349': art & crafts '7350': sms '7351': investment tech '7352': enterprise resource planning (erp) software '7353': pricing engine '7354': stocks '7355': to be shared with other vcs '7356': fan engagement platforms '7357': dépense '7358': coworking '7359': fluid milk '7360': illimité '7361': ethereum '7362': environnement '7363': grid management '7364': ott video '7365': gasket '7366': home appliance repair '7367': adas '7368': harvard business school '7369': cyberespionnage '7370': personalised wedding apps '7371': access tokens '7372': air bags '7373': limousine service '7374': coffee machine '7375': feeding '7376': ar vr '7377': discussion forum '7378': metal fabrication '7379': satellite '7380': energy usage monitoring '7381': gui based query '7382': visitor management '7383': online-to-offline '7384': premium financing '7385': search engine '7386': electronic health record (ehr) '7387': '@rh' '7388': foodservice '7389': other health care '7390': camping-car '7391': lawn & garden '7392': e-santé '7393': facility management tech '7394': farmers market '7395': bfr '7396': zéro déchet '7397': logistique '7398': electric kick scooter rentals '7399': review '7400': simulation & programming '7401': historical studies '7402': entreprise '7403': abonnement '7404': media & internet general '7405': constat '7406': baked goods '7407': pest control '7408': navire autonome '7409': estimation immobilière '7410': sanitary paper '7411': twitter '7412': iot in automotive '7413': ebook '7414': monetization '7415': flocage '7416': sports management tech '7417': energy efficiency '7418': user feedback '7419': études de marché '7420': excellent service '7421': bivouac '7422': vehicle telematics '7423': natural resources '7424': recherche '7425': accompagnement rse '7426': arts de la table '7427': offre commerciale '7428': track & trace '7429': portal '7430': 3d scanning '7431': casual '7432': home automation '7433': chemical products '7434': national security '7435': career planning '7436': fashion tech '7437': sql '7438': independent music '7439': pc & console gaming '7440': reservation management '7441': food delivery '7442': visual development '7443': solution saas '7444': béton ciré '7445': medical models and surgical guides '7446': family '7447': '@food' '7448': custom '7449': nutraceutique '7450': voip '7451': graphisme '7452': remodelers '7453': checkout '7454': industry applications '7455': banking '7456': alternance '7457': astuces '7458': contract integration '7459': accommodation '7460': consumer robotics '7461': subscription commerce '7462': nettoyage '7463': construction '7464': citizen developers '7465': local businesses '7466': trading strategy '7467': sensor fusion '7468': maison '7469': commercial energy monitoring '7470': scooter '7471': brand protection & anti-counterfeit '7472': navigation and guidance '7473': métiers de bouche '7474': computer science '7475': demand side platform '7476': meeting collaboration '7477': brevet '7478': musical instruments '7479': workshop management '7480': disease agnostic ai '7481': safes '7482': '@bébé' '7483': startup funding '7484': theaters '7485': biopharma outsourcing '7486': industrial grade '7487': association '7488': téléphonie '7489': open data '7490': wireless telecommunication services '7491': intelligence économique '7492': personnalisée '7493': communauté sportive '7494': pulp & paper '7495': training & workshops '7496': animals & livestock '7497': essential mobile apps '7498': carinsurance '7499': glue '7500': industrial inspections & monitoring '7501': product aggregators '7502': lifelong learning '7503': marine it '7504': legal design '7505': digital marketing '7506': support téléphonique '7507': professional development solutions '7508': auto insurance '7509': coding '7510': bank '7511': tourisme '7512': therapy '7513': mainstream '7514': cheminformatics for drug discovery '7515': real estate and property management '7516': esport '7517': streaming '7518': tech enablers '7519': rental '7520': pain and anxiety management '7521': auto dealership '7522': economies '7523': hommes '7524': ipaas '7525': peluche '7526': nano-based '7527': fcg '7528': chefs '7529': société civile immobilière '7530': réalité virtuelle '7531': fintech batch 2 '7532': cosmétique '7533': network incident response '7534': simulation solutions '7535': artisanat d'art '7536': photographer '7537': learning platforms '7538': internet first footwear brands '7539': enterprise video '7540': fiber '7541': chronic pain '7542': political organization '7543': fleet '7544': entreprise nettoyage '7545': autonomous retail '7546': radiology '7547': réponses '7548': location based services '7549': smes '7550': telecom '7551': student activities '7552': aéronautique, spatial '7553': technologie '7554': grocery '7555': prompt '7556': speech '7557': b2bservices '7558': smart speakers '7559': smart cars '7560': image processing & 3d mapping '7561': building materials e-commerce '7562': jardin '7563': cdn '7564': no code tools '7565': communication apps '7566': pelvic fitness '7567': plateforme '7568': activity booking platforms '7569': wine '7570': fashion & beauty '7571': media & internet '7572': booking '7573': energy, utilities & waste '7574': ios '7575': chatbots '7576': b2c ecommerce '7577': agrégateur annonces immobilières '7578': traduction technique '7579': corporate governance '7580': retail technology '7581': gestion des compétences '7582': routers '7583': network security '7584': charter services '7585': used merchandise '7586': virtual world '7587': computer storage equipment '7588': interaction analytics '7589': human ressources '7590': it management '7591': product prototyping platforms '7592': personalized '7593': devops, software development '7594': médiation '7595': bill savings '7596': trend '7597': guide '7598': reading apps '7599': import/export '7600': teenage '7601': temporary help '7602': mailbox rental '7603': video '7604': video editors for enterprises '7605': maternité '7606': live '7607': promoters '7608': ml as a service '7609': precision health '7610': customer support & engagement '7611': developer apis '7612': enterprise functions splits: - name: train num_bytes: 2599 num_examples: 10 download_size: 349999 dataset_size: 2599 --- # Dataset Card for "ds_cb_test_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aaditya/databricks-dolly-15k-Hinglish-Codemix
--- dataset_info: features: - name: en_instruction dtype: string - name: en_input dtype: string - name: en_output dtype: string - name: id dtype: string - name: en_category dtype: string - name: codemix_instruction dtype: string - name: codemix_input dtype: string - name: codemix_output dtype: string - name: codemix_category dtype: string splits: - name: train num_bytes: 24382493 num_examples: 15011 download_size: 15777048 dataset_size: 24382493 configs: - config_name: default data_files: - split: train path: data/train-* tags: - hindi - codemix - hinglish --- # Summary `aaditya/databricks-dolly-15k-Hindi` is an open source Hinglish-Codemix version dataset of databricks/databricks-dolly-15k. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Hindi Version: 1.0 Original Dataset repo https://huggingface.co/datasets/databricks/databricks-dolly-15k/edit/main/README.md # Citation ``` @misc {dolly_hinglish, author = { Pal, Ankit }, title = { databricks-dolly-15k-Hinglish-Codemix (Revision ca228c8) }, year = 2024, url = { https://huggingface.co/datasets/aaditya/databricks-dolly-15k-Hinglish-Codemix }, doi = { 10.57967/hf/1677 }, publisher = { Hugging Face } } ```
kyzor/guanaco-llama2-1k
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 966693 dataset_size: 1654448 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CATIE-AQ/universal_dependencies_fr_pud_fr_prompt_pos
--- language: - fr license: cc-by-sa-3.0 size_categories: - 10K<n<100K task_categories: - token-classification tags: - pos - DFP - french prompts annotations_creators: - found language_creators: - found multilinguality: - monolingual source_datasets: - universal_dependencies_fr_pud --- # universal_dependencies_fr_pud_fr_prompt_pos ## Summary **universal_dependencies_fr_pud_fr_prompt_pos** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP). It contains **21,000** rows that can be used for a part-of-speech task. The original data (without prompts) comes from the dataset [universal_dependencies](https://huggingface.co/datasets/universal_dependencies) where only the French pud split has been kept. A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al. ## Prompts used ### List 21 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ``` 'Extraire les classes des mots du texte suivant : '+text, 'Extrais les classes des mots du texte suivant : '+text, 'Extrayez les classes des mots du texte suivant : '+text, 'Isoler les classes des mots du texte suivant : '+text, 'Isole les classes des mots du texte suivant : '+text, 'Isolez les classes des mots du texte suivant : '+text, 'Dégager les classes des mots dans le texte : '+text, 'Dégage les classes des mots dans le texte : '+text, 'Dégagez les classes des mots dans le texte : '+text, 'Générer les classes des mots issues du texte suivant : '+text, 'Génère les classes des mots issues du texte suivant : '+text, 'Générez les classes des mots issues du texte suivant : '+text, 'Trouver les classes des mots du texte : '+text, 'Trouve les classes des mots du texte : '+text, 'Trouvez les classes des mots du texte : '+text, 'Repérer les classes des mots présentes dans le texte suivant : '+text, 'Repère les classes des mots présentes dans le texte suivant : '+text, 'Repérez les classes des mots présentes dans le texte suivant : '+text, 'Indiquer les classes des mots du texte :'+text, 'Indique les classes des mots du texte : '+text, 'Indiquez les classes des mots du texte : '+text ``` ### Features used in the prompts In the prompt list above, `text` and `targets` have been constructed from: ``` fr_pud = load_dataset('universal_dependencies', 'fr_pud') # text fr_pud['test']['tokens'] = list(map(lambda i: ' '.join(fr_pud['test']['tokens'][i]), range(len(fr_pud['test']['tokens'])))) # targets fr_pud['test']['upos'] = list(map(lambda x: x.replace("[","").replace("]","").replace('17','AUX').replace('16','VERB').replace('15','INTJ').replace('14','ADV').replace('13','_').replace('12','X').replace('11','PRON').replace('10','PROPN').replace('9','CCONJ').replace('8','DET').replace('7','PART').replace('6','ADJ').replace('5','SCONJ').replace('4','SYM').replace('3','NUM').replace('2','ADP').replace('1','PUNCT').replace('0','NOUN'), map(str,fr_pud['test']['upos']))) ``` # Splits - `train` with 21,000 samples - no `valid` split - no `test` split # How to use? ``` from datasets import load_dataset dataset = load_dataset("CATIE-AQ/universal_dependencies_fr_pud_fr_prompt_pos") ``` # Citation ## Original data > Contributors: Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Slav Petrov, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, Jana Strnadová, Gauthier Caron, Martin Popel, Daniel Zeman, Marie-Catherine de Marneffe, Bruno Guillaume. ## This Dataset > @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023, author = { {Centre Aquitain des Technologies de l'Information et Electroniques} }, title = { DFP (Revision 1d24c09) }, year = 2023, url = { https://huggingface.co/datasets/CATIE-AQ/DFP }, doi = { 10.57967/hf/1200 }, publisher = { Hugging Face } } ## License CC BY-SA 3.0
GhayasAhmed/indian_ASR_2
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 2668422488.192 num_examples: 16152 download_size: 3009401094 dataset_size: 2668422488.192 --- # Dataset Card for "indian_ASR_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
argilla/zephyr_ultrafeedback-new-3
--- dataset_info: features: - name: input dtype: string - name: generations sequence: string - name: raw_generation_response sequence: string - name: rating sequence: int64 - name: rationale sequence: string - name: raw_labelling_response struct: - name: choices list: - name: finish_reason dtype: string - name: index dtype: int64 - name: message struct: - name: content dtype: string - name: role dtype: string - name: created dtype: int64 - name: id dtype: string - name: model dtype: string - name: object dtype: string - name: usage struct: - name: completion_tokens dtype: int64 - name: prompt_tokens dtype: int64 - name: total_tokens dtype: int64 splits: - name: train num_bytes: 42845 num_examples: 10 download_size: 47621 dataset_size: 42845 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "zephyr_ultrafeedback-new-3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
diabolic6045/flanv2_cot_alpeca
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 433196702 num_examples: 373681 download_size: 207846630 dataset_size: 433196702 configs: - config_name: default data_files: - split: train path: data/train-* ---
Luciya/llama-2-clinc-train
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 10464310 num_examples: 4468 download_size: 986893 dataset_size: 10464310 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "llama-2-clinc-train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kheopss/embedding_dataset
--- dataset_info: features: - name: id dtype: string - name: text dtype: string - name: questions sequence: string splits: - name: shard_0 num_bytes: 4604004 num_examples: 5965 - name: shard_1 num_bytes: 4617546 num_examples: 5965 - name: shard_2 num_bytes: 4616997 num_examples: 5965 - name: shard_3 num_bytes: 4590657 num_examples: 5965 - name: shard_4 num_bytes: 4614216 num_examples: 5965 - name: shard_5 num_bytes: 4626937 num_examples: 5965 - name: shard_6 num_bytes: 4611489 num_examples: 5965 - name: shard_7 num_bytes: 4604575 num_examples: 5965 - name: shard_8 num_bytes: 4623831 num_examples: 5965 - name: shard_9 num_bytes: 4594027 num_examples: 5965 - name: shard_10 num_bytes: 4616296 num_examples: 5965 - name: shard_11 num_bytes: 4606268 num_examples: 5965 - name: shard_12 num_bytes: 4613044 num_examples: 5965 - name: shard_13 num_bytes: 4612459 num_examples: 5965 - name: shard_14 num_bytes: 4611549 num_examples: 5965 - name: shard_15 num_bytes: 4604027 num_examples: 5965 - name: shard_16 num_bytes: 4616186 num_examples: 5965 - name: shard_17 num_bytes: 4607129 num_examples: 5965 - name: shard_18 num_bytes: 4620141 num_examples: 5965 - name: shard_19 num_bytes: 4628969 num_examples: 5965 - name: shard_20 num_bytes: 4608334 num_examples: 5965 - name: shard_21 num_bytes: 4613870 num_examples: 5965 - name: shard_22 num_bytes: 4601899 num_examples: 5965 - name: shard_23 num_bytes: 4598149 num_examples: 5965 - name: shard_24 num_bytes: 4609556 num_examples: 5965 - name: shard_25 num_bytes: 4613084 num_examples: 5965 - name: shard_26 num_bytes: 4592315 num_examples: 5965 - name: shard_27 num_bytes: 4611395 num_examples: 5965 - name: shard_28 num_bytes: 4589260 num_examples: 5965 - name: shard_29 num_bytes: 4605174 num_examples: 5965 - name: shard_30 num_bytes: 4595089 num_examples: 5965 - name: shard_31 num_bytes: 4606021 num_examples: 5965 - name: shard_32 num_bytes: 4604807 num_examples: 5965 - name: shard_33 num_bytes: 4610354 num_examples: 5965 - name: shard_34 num_bytes: 4595199 num_examples: 5965 - name: shard_35 num_bytes: 4610596 num_examples: 5965 - name: shard_36 num_bytes: 4587872 num_examples: 5965 - name: shard_37 num_bytes: 4596316 num_examples: 5965 - name: shard_38 num_bytes: 4598647 num_examples: 5965 - name: shard_39 num_bytes: 4585849 num_examples: 5965 - name: shard_40 num_bytes: 4579994 num_examples: 5964 - name: shard_41 num_bytes: 4592618 num_examples: 5964 - name: shard_42 num_bytes: 4620249 num_examples: 5964 - name: shard_43 num_bytes: 4638306 num_examples: 5964 - name: shard_44 num_bytes: 4587193 num_examples: 5964 - name: shard_45 num_bytes: 4612625 num_examples: 5964 - name: shard_46 num_bytes: 4601391 num_examples: 5964 - name: shard_47 num_bytes: 4607778 num_examples: 5964 - name: shard_48 num_bytes: 4592387 num_examples: 5964 - name: shard_49 num_bytes: 4598091 num_examples: 5964 - name: shard_50 num_bytes: 4606395 num_examples: 5964 - name: shard_51 num_bytes: 4606194 num_examples: 5964 - name: shard_52 num_bytes: 4608819 num_examples: 5964 - name: shard_53 num_bytes: 4631511 num_examples: 5964 - name: shard_54 num_bytes: 4621906 num_examples: 5964 - name: shard_55 num_bytes: 4587456 num_examples: 5964 - name: shard_56 num_bytes: 4638626 num_examples: 5964 - name: shard_57 num_bytes: 4608168 num_examples: 5964 - name: shard_58 num_bytes: 4623010 num_examples: 5964 - name: shard_59 num_bytes: 4626281 num_examples: 5964 - name: shard_60 num_bytes: 4623950 num_examples: 5964 - name: shard_61 num_bytes: 4618001 num_examples: 5964 - name: shard_62 num_bytes: 4628067 num_examples: 5964 - name: shard_63 num_bytes: 4611102 num_examples: 5964 - name: shard_64 num_bytes: 4617741 num_examples: 5964 - name: shard_65 num_bytes: 4629882 num_examples: 5964 - name: shard_66 num_bytes: 4593534 num_examples: 5964 - name: shard_67 num_bytes: 4624838 num_examples: 5964 - name: shard_68 num_bytes: 4620071 num_examples: 5964 - name: shard_69 num_bytes: 4586273 num_examples: 5964 - name: shard_70 num_bytes: 4600295 num_examples: 5964 - name: shard_71 num_bytes: 4600024 num_examples: 5964 - name: shard_72 num_bytes: 4589211 num_examples: 5964 - name: shard_73 num_bytes: 4606781 num_examples: 5964 - name: shard_74 num_bytes: 4599071 num_examples: 5964 - name: shard_75 num_bytes: 4599836 num_examples: 5964 - name: shard_76 num_bytes: 4587882 num_examples: 5964 - name: shard_77 num_bytes: 4598052 num_examples: 5964 - name: shard_78 num_bytes: 4575789 num_examples: 5964 - name: shard_79 num_bytes: 4617427 num_examples: 5964 - name: shard_80 num_bytes: 4599142 num_examples: 5964 - name: shard_81 num_bytes: 4598673 num_examples: 5964 - name: shard_82 num_bytes: 4605022 num_examples: 5964 - name: shard_83 num_bytes: 4592967 num_examples: 5964 - name: shard_84 num_bytes: 4595012 num_examples: 5964 - name: shard_85 num_bytes: 4602650 num_examples: 5964 - name: shard_86 num_bytes: 4589416 num_examples: 5964 - name: shard_87 num_bytes: 4603516 num_examples: 5964 - name: shard_88 num_bytes: 4608421 num_examples: 5964 - name: shard_89 num_bytes: 4595258 num_examples: 5964 - name: shard_90 num_bytes: 4601451 num_examples: 5964 - name: shard_91 num_bytes: 4612052 num_examples: 5964 - name: shard_92 num_bytes: 4611352 num_examples: 5964 - name: shard_93 num_bytes: 4604830 num_examples: 5964 - name: shard_94 num_bytes: 4585045 num_examples: 5964 - name: shard_95 num_bytes: 4619076 num_examples: 5964 - name: shard_96 num_bytes: 4607353 num_examples: 5964 - name: shard_97 num_bytes: 4623002 num_examples: 5964 - name: shard_98 num_bytes: 4593759 num_examples: 5964 - name: shard_99 num_bytes: 4626992 num_examples: 5964 - name: concatenated num_bytes: 460665947 num_examples: 596440 download_size: 455368847 dataset_size: 921331894 configs: - config_name: default data_files: - split: shard_0 path: data/shard_0-* - split: shard_1 path: data/shard_1-* - split: shard_2 path: data/shard_2-* - split: shard_3 path: data/shard_3-* - split: shard_4 path: data/shard_4-* - split: shard_5 path: data/shard_5-* - split: shard_6 path: data/shard_6-* - split: shard_7 path: data/shard_7-* - split: shard_8 path: data/shard_8-* - split: shard_9 path: data/shard_9-* - split: shard_10 path: data/shard_10-* - split: shard_11 path: data/shard_11-* - split: shard_12 path: data/shard_12-* - split: shard_13 path: data/shard_13-* - split: shard_14 path: data/shard_14-* - split: shard_15 path: data/shard_15-* - split: shard_16 path: data/shard_16-* - split: shard_17 path: data/shard_17-* - split: shard_18 path: data/shard_18-* - split: shard_19 path: data/shard_19-* - split: shard_20 path: data/shard_20-* - split: shard_21 path: data/shard_21-* - split: shard_22 path: data/shard_22-* - split: shard_23 path: data/shard_23-* - split: shard_24 path: data/shard_24-* - split: shard_25 path: data/shard_25-* - split: shard_26 path: data/shard_26-* - split: shard_27 path: data/shard_27-* - split: shard_28 path: data/shard_28-* - split: shard_29 path: data/shard_29-* - split: shard_30 path: data/shard_30-* - split: shard_31 path: data/shard_31-* - split: shard_32 path: data/shard_32-* - split: shard_33 path: data/shard_33-* - split: shard_34 path: data/shard_34-* - split: shard_35 path: data/shard_35-* - split: shard_36 path: data/shard_36-* - split: shard_37 path: data/shard_37-* - split: shard_38 path: data/shard_38-* - split: shard_39 path: data/shard_39-* - split: shard_40 path: data/shard_40-* - split: shard_41 path: data/shard_41-* - split: shard_42 path: data/shard_42-* - split: shard_43 path: data/shard_43-* - split: shard_44 path: data/shard_44-* - split: shard_45 path: data/shard_45-* - split: shard_46 path: data/shard_46-* - split: shard_47 path: data/shard_47-* - split: shard_48 path: data/shard_48-* - split: shard_49 path: data/shard_49-* - split: shard_50 path: data/shard_50-* - split: shard_51 path: data/shard_51-* - split: shard_52 path: data/shard_52-* - split: shard_53 path: data/shard_53-* - split: shard_54 path: data/shard_54-* - split: shard_55 path: data/shard_55-* - split: shard_56 path: data/shard_56-* - split: shard_57 path: data/shard_57-* - split: shard_58 path: data/shard_58-* - split: shard_59 path: data/shard_59-* - split: shard_60 path: data/shard_60-* - split: shard_61 path: data/shard_61-* - split: shard_62 path: data/shard_62-* - split: shard_63 path: data/shard_63-* - split: shard_64 path: data/shard_64-* - split: shard_65 path: data/shard_65-* - split: shard_66 path: data/shard_66-* - split: shard_67 path: data/shard_67-* - split: shard_68 path: data/shard_68-* - split: shard_69 path: data/shard_69-* - split: shard_70 path: data/shard_70-* - split: shard_71 path: data/shard_71-* - split: shard_72 path: data/shard_72-* - split: shard_73 path: data/shard_73-* - split: shard_74 path: data/shard_74-* - split: shard_75 path: data/shard_75-* - split: shard_76 path: data/shard_76-* - split: shard_77 path: data/shard_77-* - split: shard_78 path: data/shard_78-* - split: shard_79 path: data/shard_79-* - split: shard_80 path: data/shard_80-* - split: shard_81 path: data/shard_81-* - split: shard_82 path: data/shard_82-* - split: shard_83 path: data/shard_83-* - split: shard_84 path: data/shard_84-* - split: shard_85 path: data/shard_85-* - split: shard_86 path: data/shard_86-* - split: shard_87 path: data/shard_87-* - split: shard_88 path: data/shard_88-* - split: shard_89 path: data/shard_89-* - split: shard_90 path: data/shard_90-* - split: shard_91 path: data/shard_91-* - split: shard_92 path: data/shard_92-* - split: shard_93 path: data/shard_93-* - split: shard_94 path: data/shard_94-* - split: shard_95 path: data/shard_95-* - split: shard_96 path: data/shard_96-* - split: shard_97 path: data/shard_97-* - split: shard_98 path: data/shard_98-* - split: shard_99 path: data/shard_99-* - split: concatenated path: data/concatenated-* ---
Thefoodprocessor/wine_type
--- dataset_info: features: - name: id dtype: int64 - name: recipe dtype: string - name: wine_type dtype: string splits: - name: train num_bytes: 110425899 num_examples: 74465 download_size: 54692163 dataset_size: 110425899 configs: - config_name: default data_files: - split: train path: data/train-* ---
Tsuinzues/moana
--- license: openrail ---
VuongQuoc/english_learn
--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 4602747761.0 num_examples: 77456 download_size: 4600511540 dataset_size: 4602747761.0 --- # Dataset Card for "english_learn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)