Datasets:
davanstrien
/
summary-test

Dataset card Data Studio Files
xet
Community
1
datasetId
stringlengths
2
117
card
stringlengths
19
1.01M
scan
--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - bsd multilinguality: - monolingual pretty_name: SCAN size_categories: - 10K<n<100K source_datasets: - original task_categories: - text2text-generation task_ids: [] paperswithcode_id: scan tags: - multi-turn dataset_info: - config_name: simple features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3217770 num_examples: 16728 - name: test num_bytes: 799912 num_examples: 4182 download_size: 4080388 dataset_size: 4017682 - config_name: addprim_jump features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2535625 num_examples: 14670 - name: test num_bytes: 1508445 num_examples: 7706 download_size: 4111174 dataset_size: 4044070 - config_name: addprim_turn_left features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3908891 num_examples: 21890 - name: test num_bytes: 170063 num_examples: 1208 download_size: 4148216 dataset_size: 4078954 - config_name: filler_num0 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2513034 num_examples: 15225 - name: test num_bytes: 330087 num_examples: 1173 download_size: 2892291 dataset_size: 2843121 - config_name: filler_num1 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2802865 num_examples: 16290 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3185317 dataset_size: 3132952 - config_name: filler_num2 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3106220 num_examples: 17391 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3491975 dataset_size: 3436307 - config_name: filler_num3 features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3412704 num_examples: 18528 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3801870 dataset_size: 3742791 - config_name: length features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2672464 num_examples: 16990 - name: test num_bytes: 1345218 num_examples: 3920 download_size: 4080388 dataset_size: 4017682 - config_name: template_around_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2513034 num_examples: 15225 - name: test num_bytes: 1229757 num_examples: 4476 download_size: 3801870 dataset_size: 3742791 - config_name: template_jump_around_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3412704 num_examples: 18528 - name: test num_bytes: 330087 num_examples: 1173 download_size: 3801870 dataset_size: 3742791 - config_name: template_opposite_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 2944398 num_examples: 15225 - name: test num_bytes: 857943 num_examples: 4476 download_size: 3861420 dataset_size: 3802341 - config_name: template_right features: - name: commands dtype: string - name: actions dtype: string splits: - name: train num_bytes: 3127623 num_examples: 15225 - name: test num_bytes: 716403 num_examples: 4476 download_size: 3903105 dataset_size: 3844026 config_names: - addprim_jump - addprim_turn_left - filler_num0 - filler_num1 - filler_num2 - filler_num3 - length - simple - template_around_right - template_jump_around_right - template_opposite_right - template_right --- # Dataset Card for "scan" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/brendenlake/SCAN](https://github.com/brendenlake/SCAN) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 224.18 MB - **Size of the generated dataset:** 44.53 MB - **Total amount of disk used:** 268.71 MB ### Dataset Summary SCAN tasks with various splits. SCAN is a set of simple language-driven navigation tasks for studying compositional learning and zero-shot generalization. See https://github.com/brendenlake/SCAN for a description of the splits. Example usage: data = datasets.load_dataset('scan/length') ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### addprim_jump - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 4.05 MB - **Total amount of disk used:** 22.73 MB An example of 'train' looks as follows. ``` ``` #### addprim_turn_left - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 4.09 MB - **Total amount of disk used:** 22.76 MB An example of 'train' looks as follows. ``` ``` #### filler_num0 - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 2.85 MB - **Total amount of disk used:** 21.53 MB An example of 'train' looks as follows. ``` ``` #### filler_num1 - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 3.14 MB - **Total amount of disk used:** 21.82 MB An example of 'train' looks as follows. ``` ``` #### filler_num2 - **Size of downloaded dataset files:** 18.69 MB - **Size of the generated dataset:** 3.44 MB - **Total amount of disk used:** 22.12 MB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### addprim_jump - `commands`: a `string` feature. - `actions`: a `string` feature. #### addprim_turn_left - `commands`: a `string` feature. - `actions`: a `string` feature. #### filler_num0 - `commands`: a `string` feature. - `actions`: a `string` feature. #### filler_num1 - `commands`: a `string` feature. - `actions`: a `string` feature. #### filler_num2 - `commands`: a `string` feature. - `actions`: a `string` feature. ### Data Splits | name |train|test| |-----------------|----:|---:| |addprim_jump |14670|7706| |addprim_turn_left|21890|1208| |filler_num0 |15225|1173| |filler_num1 |16290|1173| |filler_num2 |17391|1173| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Lake2018GeneralizationWS, title={Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks}, author={Brenden M. Lake and Marco Baroni}, booktitle={ICML}, year={2018}, url={https://arxiv.org/pdf/1711.00350.pdf}, } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
Joragasy/CultureNuc_ft
--- license: mit ---
wybxc/books
--- license: odc-by task_categories: - text-generation language: - zh size_categories: - 100K<n<1M --- 从小说以及其他来源提取的单/多轮对话语料。
proculation/mytestds2
--- dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: is_impossible dtype: bool - name: answers struct: - name: answer_start dtype: int32 - name: text dtype: string splits: - name: train num_bytes: 1092344 num_examples: 721 download_size: 147635 dataset_size: 1092344 configs: - config_name: default data_files: - split: train path: data/train-* ---
gauss314/opciones
--- license: apache-2.0 task_categories: - tabular-classification - tabular-regression tags: - Merval - options pretty_name: Merval historical options data, for deep learning and machine learning tests --- # Downloading this Options Dataset This document will guide you through the steps to download the Merval options dataset from Hugging Face Datasets. To start, you'll need to install Hugging Face's `datasets` library if you haven't done so already. You can do this using the following pip command: ```python !pip install datasets ``` Here's the Python code to load the Merval equity dataset from Hugging Face Datasets and convert it into a pandas DataFrame: ```python from datasets import load_dataset import pandas as pd id = "gauss314/opciones" data = load_dataset(id) df = pd.DataFrame(data['train'][:]) ```
dolo650/alpaca_1k
--- license: apache-2.0 ---
EleutherAI/cifarnet
--- dataset_info: features: - name: img dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck splits: - name: train num_bytes: 1560708615.0 num_examples: 190000 - name: test num_bytes: 82238790.0 num_examples: 10000 download_size: 1642628895 dataset_size: 1642947405.0 --- CIFARNet contains 200K images sampled from ImageNet-21K (Winter 2019 release), resized to 64x64, using coarse-grained labels that roughly match those of CIFAR-10. The exact ImageNet synsets used were: ``` { "n02691156": 0, # airplane "n02958343": 1, # automobile "n01503061": 2, # bird "n02121620": 3, # cat "n02430045": 4, # deer "n02083346": 5, # dog "n01639765": 6, # frog "n02374451": 7, # horse "n04194289": 8, # ship "n04490091": 9, # truck } ``` The classes are balanced, and the dataset is pre-split into a training set of 190K images and a validation set of 10K images.
tyzhu/find_marker_both_sent_train_400_eval_40_last_permute
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 5973002.880726015 num_examples: 4188 - name: validation num_bytes: 220570 num_examples: 200 download_size: 1111218 dataset_size: 6193572.880726015 --- # Dataset Card for "find_marker_both_sent_train_400_eval_40_last_permute" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
korean-corpus/korquad_v1.0_namu_candidates_256
--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question sequence: string - name: answers list: - name: answer_start sequence: int64 - name: id dtype: string - name: origin_answer_start sequence: int64 - name: origin_text sequence: string - name: text sequence: string - name: similar_context sequence: string splits: - name: train num_bytes: 46928632 num_examples: 9606 - name: validation num_bytes: 4662215 num_examples: 960 download_size: 27292916 dataset_size: 51590847 --- # Dataset Card for "korquad_v1.0_namu_candidates_256" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Adeptschneider/swahili_common_voice
--- license: apache-2.0 ---
autoevaluate/autoeval-staging-eval-project-be45ecbd-7284772
--- type: predictions tags: - autotrain - evaluation datasets: - cnn_dailymail eval_info: task: summarization model: patrickvonplaten/bert2bert_cnn_daily_mail dataset_name: cnn_dailymail dataset_config: 3.0.0 dataset_split: test col_mapping: text: article target: highlights --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: patrickvonplaten/bert2bert_cnn_daily_mail * Dataset: cnn_dailymail To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
AdaptLLM/law-tasks
--- configs: - config_name: SCOTUS data_files: - split: test path: scotus/test.json - config_name: CaseHOLD data_files: - split: test path: case_hold/test.json - config_name: UNFAIR_ToS data_files: - split: test path: unfair_tos/test.json task_categories: - text-classification - question-answering - zero-shot-classification language: - en tags: - legal --- # Domain Adaptation of Large Language Models This repo contains the **evaluation datasets** for our **ICLR 2024** paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**. ### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗 **************************** **Updates** **************************** * 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets * 2024/1/16: 🎉 Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!🎉 * 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B. * 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B. * 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B. ## Domain-Specific LLaMA-1 ### LLaMA-1-7B In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are: <p align='center'> <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> </p> ### LLaMA-1-13B Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B). ## Domain-Specific LLaMA-2-Chat Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat) For example, to chat with the law-chat model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AdaptLLM/law-chat") tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/law-chat") # Put your input here: user_input = '''Question: Which of the following is false about ex post facto laws? Options: - They make criminal an act that was innocent when committed. - They prescribe greater punishment for an act than was prescribed when it was done. - They increase the evidence required to convict a person than when the act was done. - They alter criminal offenses or punishment in a substantially prejudicial manner for the purpose of punishing a person for some past activity. Please provide your choice first and then provide explanations if possible.''' # Apply the prompt template and system prompt of LLaMA-2-Chat demo for chat models (NOTE: NO prompt template is required for base models!) our_system_prompt = "\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n" # Please do NOT change this prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{user_input} [/INST]" # # NOTE: # # If you want to apply your own system prompt, please integrate it into the instruction part following our system prompt like this: # your_system_prompt = "Please, answer this question faithfully." # prompt = f"<s>[INST] <<SYS>>{our_system_prompt}<</SYS>>\n\n{your_system_prompt}\n{user_input} [/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device) outputs = model.generate(input_ids=inputs, max_length=4096)[0] answer_start = int(inputs.shape[-1]) pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}') ``` ## Domain-Specific Tasks ### Pre-templatized/Formatted Testing Splits To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks). **Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models. ### Raw Datasets We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: - [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt) - [RCT](https://huggingface.co/datasets/AdaptLLM/RCT) - [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) - [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA) - [Headline](https://huggingface.co/datasets/AdaptLLM/Headline) - [NER](https://huggingface.co/datasets/AdaptLLM/NER) - [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code: ```python from datasets import load_dataset # MQP: dataset = load_dataset('medical_questions_pairs') # PubmedQA: dataset = load_dataset('bigbio/pubmed_qa') # USMLE: dataset=load_dataset('GBaker/MedQA-USMLE-4-options') # SCOTUS dataset = load_dataset("lex_glue", 'scotus') # CaseHOLD dataset = load_dataset("lex_glue", 'case_hold') # UNFAIR-ToS dataset = load_dataset("lex_glue", 'unfair_tos') ``` ## Citation If you find our work helpful, please cite us: ```bibtex @inproceedings{ cheng2024adapting, title={Adapting Large Language Models via Reading Comprehension}, author={Daixuan Cheng and Shaohan Huang and Furu Wei}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=y886UXPEZ0} } ```
AdapterOcean/med_alpaca_standardized_cluster_47_alpaca
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 11417125 num_examples: 8087 download_size: 6032990 dataset_size: 11417125 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_47_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shermansiu/sgd_dst
--- license: cc-by-sa-4.0 tags: - natural-language-processing - dialogue-state-tracking language: - en pretty_name: Schema-Guided Dialogue dataset - Dialogue State Tracking configs: - config_name: sgd-sdt data_files: - split: train path: "sdt/sgd_sdt_v0_train.tsv" - split: dev path: "sdt/sgd_sdt_v0_dev.tsv" - split: test path: "sdt/sgd_sdt_v0_test.tsv" - config_name: sgd-d3st data_files: - split: train path: "d3st/sgd_d3st_v0_train.tsv" - split: dev path: "d3st/sgd_d3st_v0_dev.tsv" - split: test path: "d3st/sgd_d3st_v0_test.tsv" dataset_info: - config_name: sgd-sdt features: - name: prompt dtype: string - name: target dtype: string - name: dialogue_id dtype: string - name: turn_id dtype: string - name: frame_id dtype: string splits: - name: train num_bytes: 269690581 num_examples: 175780 - name: dev num_bytes: 38376392 num_examples: 26077 - name: test num_bytes: 72134551 num_examples: 46116 - config_name: sgd-d3st features: - name: prompt dtype: string - name: target dtype: string - name: dialogue_id dtype: string - name: turn_id dtype: string - name: frame_id dtype: string splits: - name: train num_bytes: 226090375 num_examples: 175780 - name: dev num_bytes: 32170435 num_examples: 26077 - name: test num_bytes: 59997238 num_examples: 46116 --- # Schema-Guided Dialogue dataset - Dialogue State Tracking This dataset contains the Schema-Guided Dialogue Dataset, formatted according to the prompt formats from the following two dialogue state tracking papers: - Description-Driven Dialogue State Tracking (D3ST) ([Zhao et al., 2022](https://arxiv.org/abs/2201.08904)) - Show, Don't Tell (SDT) ([Gupta et al., 2022](https://arxiv.org/abs/2204.04327)) Data processing code: https://github.com/google-research/task-oriented-dialogue Original dataset: https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
indiehackers/Telugu_InstructData
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 82739156 num_examples: 33350 download_size: 32498948 dataset_size: 82739156 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - text-generation language: - te size_categories: - 10K<n<100K --- This dataset is a translated version of three original datasets, namely [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots), [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/dolly-15k), and a subset of Telugu from [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset)..
open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0
--- pretty_name: Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EmbeddedLLM/Mistral-7B-Merge-14-v0](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0)\ \ 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_EmbeddedLLM__Mistral-7B-Merge-14-v0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-18T09:26:04.672254](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0/blob/main/results_2023-12-18T09-26-04.672254.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\": 0.6495541508127465,\n\ \ \"acc_stderr\": 0.03195905121079232,\n \"acc_norm\": 0.6503791395121084,\n\ \ \"acc_norm_stderr\": 0.03260613037981816,\n \"mc1\": 0.41003671970624234,\n\ \ \"mc1_stderr\": 0.017217844717449325,\n \"mc2\": 0.5858188205527958,\n\ \ \"mc2_stderr\": 0.015472440902902757\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131169,\n\ \ \"acc_norm\": 0.658703071672355,\n \"acc_norm_stderr\": 0.013855831287497728\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6736705835490938,\n\ \ \"acc_stderr\": 0.004679111783653906,\n \"acc_norm\": 0.8552081258713403,\n\ \ \"acc_norm_stderr\": 0.0035117170854519824\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\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n\ \ \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.04960449637488583,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.04960449637488583\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-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\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\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\": 0.5175438596491229,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\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\"\ : 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997695,\n \"\ acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997695\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8064516129032258,\n\ \ \"acc_stderr\": 0.022475258525536057,\n \"acc_norm\": 0.8064516129032258,\n\ \ \"acc_norm_stderr\": 0.022475258525536057\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.02399150050031304,\n \ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.02399150050031304\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113115,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113115\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\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\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233483,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233483\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\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\": 0.03989139859531771,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.02370309952525817,\n\ \ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.02370309952525817\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.38100558659217876,\n\ \ \"acc_stderr\": 0.016242028834053616,\n \"acc_norm\": 0.38100558659217876,\n\ \ \"acc_norm_stderr\": 0.016242028834053616\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n\ \ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \ \ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4654498044328553,\n\ \ \"acc_stderr\": 0.012739711554045706,\n \"acc_norm\": 0.4654498044328553,\n\ \ \"acc_norm_stderr\": 0.012739711554045706\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \ \ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\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\": 0.7061224489795919,\n \"acc_stderr\": 0.02916273841024977,\n\ \ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.02916273841024977\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41003671970624234,\n\ \ \"mc1_stderr\": 0.017217844717449325,\n \"mc2\": 0.5858188205527958,\n\ \ \"mc2_stderr\": 0.015472440902902757\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626918\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6671721000758151,\n \ \ \"acc_stderr\": 0.012979892496598281\n }\n}\n```" repo_url: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0 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_12_18T09_26_04.672254 path: - '**/details_harness|arc:challenge|25_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-18T09-26-04.672254.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|gsm8k|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hellaswag|10_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-18T09-26-04.672254.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-management|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T09-26-04.672254.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|truthfulqa:mc|0_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-18T09-26-04.672254.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_18T09_26_04.672254 path: - '**/details_harness|winogrande|5_2023-12-18T09-26-04.672254.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-18T09-26-04.672254.parquet' - config_name: results data_files: - split: 2023_12_18T09_26_04.672254 path: - results_2023-12-18T09-26-04.672254.parquet - split: latest path: - results_2023-12-18T09-26-04.672254.parquet --- # Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [EmbeddedLLM/Mistral-7B-Merge-14-v0](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0) 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_EmbeddedLLM__Mistral-7B-Merge-14-v0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-18T09:26:04.672254](https://huggingface.co/datasets/open-llm-leaderboard/details_EmbeddedLLM__Mistral-7B-Merge-14-v0/blob/main/results_2023-12-18T09-26-04.672254.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": 0.6495541508127465, "acc_stderr": 0.03195905121079232, "acc_norm": 0.6503791395121084, "acc_norm_stderr": 0.03260613037981816, "mc1": 0.41003671970624234, "mc1_stderr": 0.017217844717449325, "mc2": 0.5858188205527958, "mc2_stderr": 0.015472440902902757 }, "harness|arc:challenge|25": { "acc": 0.6271331058020477, "acc_stderr": 0.014131176760131169, "acc_norm": 0.658703071672355, "acc_norm_stderr": 0.013855831287497728 }, "harness|hellaswag|10": { "acc": 0.6736705835490938, "acc_stderr": 0.004679111783653906, "acc_norm": 0.8552081258713403, "acc_norm_stderr": 0.0035117170854519824 }, "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": 0.6518518518518519, "acc_stderr": 0.041153246103369526, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.041153246103369526 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7171052631578947, "acc_stderr": 0.03665349695640767, "acc_norm": 0.7171052631578947, "acc_norm_stderr": 0.03665349695640767 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.04960449637488583, "acc_norm": 0.58, "acc_norm_stderr": 0.04960449637488583 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "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": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "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": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "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": 0.4021164021164021, "acc_stderr": 0.025253032554997695, "acc_norm": 0.4021164021164021, "acc_norm_stderr": 0.025253032554997695 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.04878317312145632, "acc_norm": 0.38, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8064516129032258, "acc_stderr": 0.022475258525536057, "acc_norm": 0.8064516129032258, "acc_norm_stderr": 0.022475258525536057 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009181, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009181 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.02399150050031304, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.02399150050031304 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.02889774874113115, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.02889774874113115 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "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": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.025530100460233483, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.025530100460233483 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596914, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596914 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8240740740740741, "acc_stderr": 0.036809181416738807, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.036809181416738807 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "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": 0.03989139859531771, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.03989139859531771 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8339719029374202, "acc_stderr": 0.0133064782430663, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.0133064782430663 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7369942196531792, "acc_stderr": 0.02370309952525817, "acc_norm": 0.7369942196531792, "acc_norm_stderr": 0.02370309952525817 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.38100558659217876, "acc_stderr": 0.016242028834053616, "acc_norm": 0.38100558659217876, "acc_norm_stderr": 0.016242028834053616 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7009646302250804, "acc_stderr": 0.02600330111788514, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.02600330111788514 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7438271604938271, "acc_stderr": 0.024288533637726095, "acc_norm": 0.7438271604938271, "acc_norm_stderr": 0.024288533637726095 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.475177304964539, "acc_stderr": 0.029790719243829727, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.029790719243829727 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4654498044328553, "acc_stderr": 0.012739711554045706, "acc_norm": 0.4654498044328553, "acc_norm_stderr": 0.012739711554045706 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7022058823529411, "acc_stderr": 0.02777829870154544, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.02777829870154544 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6683006535947712, "acc_stderr": 0.01904748523936038, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.01904748523936038 }, "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": 0.7061224489795919, "acc_stderr": 0.02916273841024977, "acc_norm": 0.7061224489795919, "acc_norm_stderr": 0.02916273841024977 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578337, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578337 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.41003671970624234, "mc1_stderr": 0.017217844717449325, "mc2": 0.5858188205527958, "mc2_stderr": 0.015472440902902757 }, "harness|winogrande|5": { "acc": 0.7963693764798737, "acc_stderr": 0.011317798781626918 }, "harness|gsm8k|5": { "acc": 0.6671721000758151, "acc_stderr": 0.012979892496598281 } } ``` ## 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]
Mohamad-Jaallouk/SnowTest
--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: test num_bytes: 8657666.0 num_examples: 36 download_size: 8585450 dataset_size: 8657666.0 configs: - config_name: default data_files: - split: test path: data/test-* ---
Tngarg/Codemix_tamil_english_train
--- dataset_info: features: - name: tweet dtype: string - name: sentiment dtype: string splits: - name: train num_bytes: 1934489.5931346258 num_examples: 25840 download_size: 1135012 dataset_size: 1934489.5931346258 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Codemix_tamil_english_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-high_school_psychology
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: fewshot_context_neg dtype: string splits: - name: dev num_bytes: 5820 num_examples: 5 - name: test num_bytes: 2627471 num_examples: 545 download_size: 247537 dataset_size: 2633291 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-high_school_psychology" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nxsbr/schmidt
--- license: openrail ---
SilentAntagonist/dalle3-reddit-cogvlm-captions
--- license: cc-by-nc-4.0 --- # DALL-E 3 text-image pairs ## Description This dataset contains DALL-E images posted on Reddit (mixed with other AI-generated images from Midjourney and Stable Diffusion).
Nerfgun3/star_style
--- language: - en license: creativeml-openrail-m thumbnail: "https://huggingface.co/datasets/Nerfgun3/star_style/resolve/main/star_showcase.png" tags: - stable-diffusion - text-to-image - image-to-image inference: false --- # Star Style Embedding / Textual Inversion <img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/star_style/resolve/main/star_showcase.png"/> ## Usage To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt: ```"star_style"``` Personally, I would recommend to use my embeddings with a strength of 0.8, like ```"(star_style:0.8)"``` This embedding can be used for characters aswell! Just use it with a strength of 0.6 or less! I hope you enjoy the embedding. If you have any questions, you can ask me anything via Discord: "Nerfgun3#7508" ## License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
open-llm-leaderboard/details_DreadPoor__ToppyLake-7B-slerp
--- pretty_name: Evaluation run of DreadPoor/ToppyLake-7B-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [DreadPoor/ToppyLake-7B-slerp](https://huggingface.co/DreadPoor/ToppyLake-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_DreadPoor__ToppyLake-7B-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-13T17:37:50.313114](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__ToppyLake-7B-slerp/blob/main/results_2024-02-13T17-37-50.313114.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\": 0.6527643322107648,\n\ \ \"acc_stderr\": 0.03209219499038095,\n \"acc_norm\": 0.65299648074574,\n\ \ \"acc_norm_stderr\": 0.032754010922687565,\n \"mc1\": 0.4700122399020808,\n\ \ \"mc1_stderr\": 0.01747199209169754,\n \"mc2\": 0.6253804109646736,\n\ \ \"mc2_stderr\": 0.01533242283561269\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6706484641638225,\n \"acc_stderr\": 0.013734057652635474,\n\ \ \"acc_norm\": 0.6919795221843004,\n \"acc_norm_stderr\": 0.013491429517292038\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6954789882493527,\n\ \ \"acc_stderr\": 0.004592637369905785,\n \"acc_norm\": 0.8698466440948018,\n\ \ \"acc_norm_stderr\": 0.0033578442491239546\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\ \ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.050161355804659205\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\": 0.6589595375722543,\n\ \ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\ \ \"acc_norm_stderr\": 0.036146654241808254\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\":\ \ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\ \ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944433,\n \"\ acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944433\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\ : 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726854,\n \"\ acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726854\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\": 0.69,\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\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6846153846153846,\n \"acc_stderr\": 0.02355964698318994,\n \ \ \"acc_norm\": 0.6846153846153846,\n \"acc_norm_stderr\": 0.02355964698318994\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\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\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\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553332,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553332\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.0263616516683891,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.0263616516683891\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037182\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.02158649400128137,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.02158649400128137\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8352490421455939,\n\ \ \"acc_stderr\": 0.013265346261323788,\n \"acc_norm\": 0.8352490421455939,\n\ \ \"acc_norm_stderr\": 0.013265346261323788\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500097,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500097\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3888268156424581,\n\ \ \"acc_stderr\": 0.016303899530796123,\n \"acc_norm\": 0.3888268156424581,\n\ \ \"acc_norm_stderr\": 0.016303899530796123\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.02555316999182652,\n\ \ \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.02555316999182652\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.023993501709042107,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.023993501709042107\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4602346805736636,\n\ \ \"acc_stderr\": 0.01272978538659856,\n \"acc_norm\": 0.4602346805736636,\n\ \ \"acc_norm_stderr\": 0.01272978538659856\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616914,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616914\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4700122399020808,\n\ \ \"mc1_stderr\": 0.01747199209169754,\n \"mc2\": 0.6253804109646736,\n\ \ \"mc2_stderr\": 0.01533242283561269\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8279400157853196,\n \"acc_stderr\": 0.010607731615247012\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6595905989385898,\n \ \ \"acc_stderr\": 0.013052097103299102\n }\n}\n```" repo_url: https://huggingface.co/DreadPoor/ToppyLake-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_02_13T17_37_50.313114 path: - '**/details_harness|arc:challenge|25_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-13T17-37-50.313114.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|gsm8k|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hellaswag|10_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-13T17-37-50.313114.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-management|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T17-37-50.313114.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|truthfulqa:mc|0_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-13T17-37-50.313114.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_13T17_37_50.313114 path: - '**/details_harness|winogrande|5_2024-02-13T17-37-50.313114.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-13T17-37-50.313114.parquet' - config_name: results data_files: - split: 2024_02_13T17_37_50.313114 path: - results_2024-02-13T17-37-50.313114.parquet - split: latest path: - results_2024-02-13T17-37-50.313114.parquet --- # Dataset Card for Evaluation run of DreadPoor/ToppyLake-7B-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [DreadPoor/ToppyLake-7B-slerp](https://huggingface.co/DreadPoor/ToppyLake-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_DreadPoor__ToppyLake-7B-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-13T17:37:50.313114](https://huggingface.co/datasets/open-llm-leaderboard/details_DreadPoor__ToppyLake-7B-slerp/blob/main/results_2024-02-13T17-37-50.313114.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": 0.6527643322107648, "acc_stderr": 0.03209219499038095, "acc_norm": 0.65299648074574, "acc_norm_stderr": 0.032754010922687565, "mc1": 0.4700122399020808, "mc1_stderr": 0.01747199209169754, "mc2": 0.6253804109646736, "mc2_stderr": 0.01533242283561269 }, "harness|arc:challenge|25": { "acc": 0.6706484641638225, "acc_stderr": 0.013734057652635474, "acc_norm": 0.6919795221843004, "acc_norm_stderr": 0.013491429517292038 }, "harness|hellaswag|10": { "acc": 0.6954789882493527, "acc_stderr": 0.004592637369905785, "acc_norm": 0.8698466440948018, "acc_norm_stderr": 0.0033578442491239546 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6592592592592592, "acc_stderr": 0.040943762699967926, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.040943762699967926 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6973684210526315, "acc_stderr": 0.03738520676119669, "acc_norm": 0.6973684210526315, "acc_norm_stderr": 0.03738520676119669 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544067, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544067 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "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": 0.6589595375722543, "acc_stderr": 0.036146654241808254, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.036146654241808254 }, "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": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.03240038086792747, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.03240038086792747 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5175438596491229, "acc_stderr": 0.04700708033551038, "acc_norm": 0.5175438596491229, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555497, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555497 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944433, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944433 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.02328766512726854, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726854 }, "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": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328972, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328972 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6846153846153846, "acc_stderr": 0.02355964698318994, "acc_norm": 0.6846153846153846, "acc_norm_stderr": 0.02355964698318994 }, "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": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "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": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5138888888888888, "acc_stderr": 0.03408655867977749, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.025524722324553332, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.025524722324553332 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7932489451476793, "acc_stderr": 0.0263616516683891, "acc_norm": 0.7932489451476793, "acc_norm_stderr": 0.0263616516683891 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.03498149385462472, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.03498149385462472 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037182, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037182 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.02158649400128137, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.02158649400128137 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8352490421455939, "acc_stderr": 0.013265346261323788, "acc_norm": 0.8352490421455939, "acc_norm_stderr": 0.013265346261323788 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500097, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500097 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3888268156424581, "acc_stderr": 0.016303899530796123, "acc_norm": 0.3888268156424581, "acc_norm_stderr": 0.016303899530796123 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7254901960784313, "acc_stderr": 0.02555316999182652, "acc_norm": 0.7254901960784313, "acc_norm_stderr": 0.02555316999182652 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.025583062489984813, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.025583062489984813 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7530864197530864, "acc_stderr": 0.023993501709042107, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.023993501709042107 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4858156028368794, "acc_stderr": 0.02981549448368206, "acc_norm": 0.4858156028368794, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4602346805736636, "acc_stderr": 0.01272978538659856, "acc_norm": 0.4602346805736636, "acc_norm_stderr": 0.01272978538659856 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6764705882352942, "acc_stderr": 0.02841820861940676, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.02841820861940676 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6699346405228758, "acc_stderr": 0.019023726160724553, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.019023726160724553 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616914, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616914 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.4700122399020808, "mc1_stderr": 0.01747199209169754, "mc2": 0.6253804109646736, "mc2_stderr": 0.01533242283561269 }, "harness|winogrande|5": { "acc": 0.8279400157853196, "acc_stderr": 0.010607731615247012 }, "harness|gsm8k|5": { "acc": 0.6595905989385898, "acc_stderr": 0.013052097103299102 } } ``` ## 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]
eduardo654nff/kakashinn
--- license: openrail ---
projecte-aina/openslr-slr69-ca-trimmed-denoised
--- annotations_creators: - no-annotation language_creators: - crowdsourced language: - ca license: - cc-by-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: openslr task_categories: - text-to-speech task_ids: [] pretty_name: openslr-slr69-ca-trimmed-denoised 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: 811311975.4 num_examples: 4240 download_size: 721217811 dataset_size: 811311975.4 --- # Dataset Card for openslr-slr69-ca-denoised This is a post-processed version of the Catalan subset belonging to the [Open Speech and Language Resources (OpenSLR)](https://www.openslr.org/index.html) speech dataset. Specifically the subset [OpenSLR-69](https://www.openslr.org/69). The original HF🤗 SLR-69 dataset is located [here](https://huggingface.co/datasets/openslr/viewer/SLR69). Same license is maintained: [Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by/4.0/). ## Dataset Details ### Dataset Description We processed the data of the Catalan OpenSLR with the following recipe: - **Trimming:** Long silences from the start and the end of clips have been removed. - [py-webrtcvad](https://pypi.org/project/webrtcvad/) -> Python interface to the Voice Activity Detector (VAD) developed by Google for the WebRTC. - **Resampling:** From 48000 Hz to 22050 Hz, which is the most common sampling rate for training TTS models - Resampler from [CoquiTTS](https://github.com/coqui-ai/TTS/tree/dev) framework - **Denoising:** Although base quality of the audios is high, we could remove some background noise and small artifcats thanks to the CleanUNet denoiser developed by NVIDIA. - [CleanUNet](https://github.com/NVIDIA/CleanUNet) - [arXiv](https://arxiv.org/abs/2202.07790) We kept the same number of wave files, also the original anonymized file names and transcriptions. ## Uses The purpose of this dataset is mainly for training text-to-speech and automatic speech recognition models in Catalan. ## 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. --> The dataset consists of a single split, providing audios and transcriptions: ``` DatasetDict({ train: Dataset({ features: ['audio', 'transcription'], num_rows: 4240 }) }) ``` Each data point is structured as: ``` >> data['train'][0]['audio'] {'path': 'caf_09901_01619988267.wav', 'array': array([-3.05175781e-05, -3.05175781e-05, -3.05175781e-05, ..., -6.10351562e-05, -6.10351562e-05, -6.10351562e-05]) 'sampling_rate': 22050} >> data['train'][0]['transcription'] "L'òpera de Sydney es troba a l'entrada de la badia" ``` ### Dataset Splits - ```audio (dict)```: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: ```dataset[0]["audio"]``` the audio file is automatically decoded and resampled to ```dataset.features["audio"].sampling_rate```. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus, it is important to first query the sample index before the "audio" column, i.e. ```dataset[0]["audio"]``` should always be preferred over ```dataset["audio"][0]```. * ```path (str)```: The path to the audio file. * ```array (array)```: Decoded audio array. * ```sampling_rate (int)```: Audio sampling rate. - ```transcription (str)```: The sentence the user was prompted to speak. ## Dataset Creation ### Source Data *SLR69: Crowdsourced high-quality Catalan multi-speaker speech data set* This data set contains transcribed high-quality audio of Catalan sentences recorded by volunteers. The recordings were prepared with the help of Direcció General de Política Lingüística del Departament de Cultura, Generalitat de Catalunya. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains an anonymized FileID and the transcription of audio in the file. The data set has been manually quality checked, but there might still be errors. Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues The original dataset is distributed under Creative Commons Attribution-ShareAlike 4.0 International Public License. See [LICENSE](https://www.openslr.org/resources/69/LICENSE) file and [https://github.com/google/language-resources#license](https://github.com/google/language-resources#license) for license information. #### 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. --> This is a post-processed version of the Catalan [OpenSLR-69](https://www.openslr.org/69) dataset. For more inormation about the original data collection and processing refer to [this paper](https://aclanthology.org/2020.sltu-1.3/). #### Who are the source data producers? Copyright 2018, 2019 Google, Inc. Copyright 2023 Language Technologies Unit (LangTech) at Barcelona Supercomputing Center ### Annotations <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> (N/A) #### 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. --> The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> This dataset is a post-processed version of another previously created dataset. Please, refer to its documentation to know about any possible risks, biases and limitations. ## Citation The original paper where authors detail how OpenSLR-69 was generated: ``` @inproceedings{kjartansson-etal-2020-open, title = {{Open-Source High Quality Speech Datasets for Basque, Catalan and Galician}}, author = {Kjartansson, Oddur and Gutkin, Alexander and Butryna, Alena and Demirsahin, Isin and Rivera, Clara}, booktitle = {Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)}, year = {2020}, pages = {21--27}, month = may, address = {Marseille, France}, publisher = {European Language Resources association (ELRA)}, url = {https://www.aclweb.org/anthology/2020.sltu-1.3}, ISBN = {979-10-95546-35-1}, } ``` **APA:** ## Funding This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). ## Dataset Card Contact langtech@bsc.es
mindchain/Text_Classification_Deutsch_Beispiel
--- license: apache-2.0 language: - de size_categories: - 1K<n<10K ---
ThWu/mmlu_test_val_split
--- dataset_info: features: - name: prompt dtype: string - name: choices struct: - name: gpt-4-1106-preview dtype: string - name: mixtral-8x7b-instruct-v0.1 dtype: string - name: formatted_propmt dtype: string - name: label dtype: string - name: subject dtype: string - name: prompt_id dtype: int64 - name: winner dtype: string splits: - name: val num_bytes: 5321330 num_examples: 1531 - name: test num_bytes: 48566928 num_examples: 14042 download_size: 11932844 dataset_size: 53888258 configs: - config_name: default data_files: - split: val path: data/val-* - split: test path: data/test-* ---
Vextwix/Yes
--- license: creativeml-openrail-m ---
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r4-q_k_v_o
--- pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o)\ \ 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_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r4-q_k_v_o\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-28T03:32:51.454817](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r4-q_k_v_o/blob/main/results_2023-10-28T03-32-51.454817.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\": 0.37258808724832215,\n\ \ \"em_stderr\": 0.004951428522573584,\n \"f1\": 0.41863255033557134,\n\ \ \"f1_stderr\": 0.004838761301543826,\n \"acc\": 0.4445987937813739,\n\ \ \"acc_stderr\": 0.010466651540029098\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.37258808724832215,\n \"em_stderr\": 0.004951428522573584,\n\ \ \"f1\": 0.41863255033557134,\n \"f1_stderr\": 0.004838761301543826\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12282031842304776,\n \ \ \"acc_stderr\": 0.009041108602874675\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7663772691397001,\n \"acc_stderr\": 0.011892194477183524\n\ \ }\n}\n```" repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o 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_10_10T09_48_52.263585 path: - '**/details_harness|arc:challenge|25_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-10T09-48-52.263585.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_28T03_32_51.454817 path: - '**/details_harness|drop|3_2023-10-28T03-32-51.454817.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-28T03-32-51.454817.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_28T03_32_51.454817 path: - '**/details_harness|gsm8k|5_2023-10-28T03-32-51.454817.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-28T03-32-51.454817.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hellaswag|10_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-48-52.263585.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-48-52.263585.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_10T09_48_52.263585 path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T09-48-52.263585.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-10T09-48-52.263585.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_28T03_32_51.454817 path: - '**/details_harness|winogrande|5_2023-10-28T03-32-51.454817.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-28T03-32-51.454817.parquet' - config_name: results data_files: - split: 2023_10_10T09_48_52.263585 path: - results_2023-10-10T09-48-52.263585.parquet - split: 2023_10_28T03_32_51.454817 path: - results_2023-10-28T03-32-51.454817.parquet - split: latest path: - results_2023-10-28T03-32-51.454817.parquet --- # Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o - **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 [CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE5_4w-r4-q_k_v_o) 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_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r4-q_k_v_o", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T03:32:51.454817](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE5_4w-r4-q_k_v_o/blob/main/results_2023-10-28T03-32-51.454817.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": 0.37258808724832215, "em_stderr": 0.004951428522573584, "f1": 0.41863255033557134, "f1_stderr": 0.004838761301543826, "acc": 0.4445987937813739, "acc_stderr": 0.010466651540029098 }, "harness|drop|3": { "em": 0.37258808724832215, "em_stderr": 0.004951428522573584, "f1": 0.41863255033557134, "f1_stderr": 0.004838761301543826 }, "harness|gsm8k|5": { "acc": 0.12282031842304776, "acc_stderr": 0.009041108602874675 }, "harness|winogrande|5": { "acc": 0.7663772691397001, "acc_stderr": 0.011892194477183524 } } ``` ### 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]
SageLiao/guanaco-llama2-1k
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1654448 num_examples: 1000 download_size: 0 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)
version-control/ds-lib-version-3
--- dataset_info: features: - name: repo_name dtype: string - name: version list: - name: pyproject.toml struct: - name: matplotlib dtype: string - name: numpy dtype: string - name: pandas dtype: string - name: scikit-learn dtype: string - name: scipy dtype: string - name: tensorflow dtype: string - name: torch dtype: string - name: requirements.txt struct: - name: matplotlib dtype: string - name: numpy dtype: string - name: pandas dtype: string - name: scikit-learn dtype: string - name: scipy dtype: string - name: tensorflow dtype: string - name: torch dtype: string - name: setup.py struct: - name: matplotlib dtype: string - name: numpy dtype: string - name: pandas dtype: string - name: scikit-learn dtype: string - name: scipy dtype: string - name: tensorflow dtype: string - name: torch dtype: string - name: hexsha sequence: string splits: - name: train num_bytes: 2506652 num_examples: 10000 download_size: 824580 dataset_size: 2506652 configs: - config_name: default data_files: - split: train path: data/train-* ---
MatsuoDochiai/Roxy
--- license: openrail ---
Amir13/wnut2017-persian
--- annotations_creators: - crowdsourced language: - fa language_creators: - machine-generated license: - other multilinguality: - monolingual paperswithcode_id: pretty_name: wnut2017-persian size_categories: - 1K<n<10K tags: - named entity recognition task_categories: - token-classification task_ids: - named-entity-recognition --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### 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 If you used the datasets and models in this repository, please cite it. ```bibtex @misc{https://doi.org/10.48550/arxiv.2302.09611, doi = {10.48550/ARXIV.2302.09611}, url = {https://arxiv.org/abs/2302.09611}, author = {Sartipi, Amir and Fatemi, Afsaneh}, keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English}, publisher = {arXiv}, year = {2023}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ### Contributions [More Information Needed]
sachith-surge/LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2-gpt4
--- dataset_info: features: - name: instruction dtype: string - name: source dtype: string - name: response dtype: string - name: llama2_status dtype: string - name: llama2_rating dtype: string - name: llama2_reason dtype: string - name: gpt4_status dtype: string - name: gpt4_rating dtype: string - name: gpt4_reason dtype: string splits: - name: train num_bytes: 2729018 num_examples: 1505 download_size: 1378351 dataset_size: 2729018 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "LaMini-LM-dataset-TheBloke-h2ogpt-falcon-40b-v2-GGML-eval-llama2-gpt4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_voidful__changpt-bart
--- pretty_name: Evaluation run of voidful/changpt-bart dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [voidful/changpt-bart](https://huggingface.co/voidful/changpt-bart) 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 4 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_voidful__changpt-bart\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T15:06:06.678906](https://huggingface.co/datasets/open-llm-leaderboard/details_voidful__changpt-bart/blob/main/results_2023-12-02T15-06-06.678906.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\": 0.0,\n \"\ acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \ \ \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/voidful/changpt-bart 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_09T19_52_50.972620 path: - '**/details_harness|arc:challenge|25_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|arc:challenge|25_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-09T19:53:13.918423.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T22_50_05.507806 path: - '**/details_harness|drop|3_2023-09-17T22-50-05.507806.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T22-50-05.507806.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T22_50_05.507806 path: - '**/details_harness|gsm8k|5_2023-09-17T22-50-05.507806.parquet' - split: 2023_12_02T15_06_06.678906 path: - '**/details_harness|gsm8k|5_2023-12-02T15-06-06.678906.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-02T15-06-06.678906.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hellaswag|10_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hellaswag|10_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T19:52:50.972620.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T19:53:13.918423.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T19:53:13.918423.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T19_52_50.972620 path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T19:52:50.972620.parquet' - split: 2023_08_09T19_53_13.918423 path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T19:53:13.918423.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T19:53:13.918423.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T22_50_05.507806 path: - '**/details_harness|winogrande|5_2023-09-17T22-50-05.507806.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T22-50-05.507806.parquet' - config_name: results data_files: - split: 2023_08_09T19_52_50.972620 path: - results_2023-08-09T19:52:50.972620.parquet - split: 2023_08_09T19_53_13.918423 path: - results_2023-08-09T19:53:13.918423.parquet - split: 2023_09_17T22_50_05.507806 path: - results_2023-09-17T22-50-05.507806.parquet - split: 2023_12_02T15_06_06.678906 path: - results_2023-12-02T15-06-06.678906.parquet - split: latest path: - results_2023-12-02T15-06-06.678906.parquet --- # Dataset Card for Evaluation run of voidful/changpt-bart ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/voidful/changpt-bart - **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 [voidful/changpt-bart](https://huggingface.co/voidful/changpt-bart) 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 4 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_voidful__changpt-bart", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T15:06:06.678906](https://huggingface.co/datasets/open-llm-leaderboard/details_voidful__changpt-bart/blob/main/results_2023-12-02T15-06-06.678906.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": 0.0, "acc_stderr": 0.0 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### 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]
polinaeterna/new_meta_format
--- dataset_info: - config_name: custom features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 1600 num_examples: 100 - name: random num_bytes: 160 num_examples: 10 download_size: 3650 dataset_size: 1760 - config_name: default features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 1600 num_examples: 100 - name: random num_bytes: 800 num_examples: 50 download_size: 0 dataset_size: 2400 builder_config: - config_name: custom data_files: - split: train pattern: custom/train-* - split: random pattern: custom/random-* - config_name: default data_files: - split: train pattern: data/train-* - split: random pattern: data/random-* --- # Dataset Card for "new_meta_format" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Marchanjo/spider-es
--- license: cc-by-sa-4.0 --- Distributed under the Creative Commons-by-sa-4.0 respecting the ShareAlike of the [Spider Dataset](https://yale-lily.github.io/spider). Code explanations and links for the model's checkpoints and datasets are on Github [mRAT-SQL](https://github.com/C4AI/gap-text2sql) Here is the [Hugging Face collection](https://huggingface.co/collections/Marchanjo/mrat-sql-65a671743bb0e70b416561f6), you can download the model's checkpoints and datasets, but to understand is better to go to Github [mRAT-SQL](https://github.com/C4AI/gap-text2sql). # mRAT-SQL-FIT ## A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attention Marcelo Archanjo Jose, Fabio Gagliardi Cozman Long sequences of text are challenging in the context of transformers, due to quadratic memory increase in the self-attention mechanism. As this issue directly affects the translation from natural language to SQL queries (as techniques usually take as input a concatenated text with the question and the database schema), we present techniques that allow long text sequences to be handled by transformers with up to 512 input tokens. We propose a training process with database schema pruning (removal of tables and columns names that are useless for the query of interest). In addition, we used a multilingual approach with the mT5-large model fine-tuned with a data-augmented Spider dataset in four languages simultaneously: English, Portuguese, Spanish, and French. Our proposed technique used the Spider dataset and increased the exact set match accuracy results from 0.718 to 0.736 in a validation dataset (Dev). Source code, evaluations, and checkpoints are available at: [mRAT-SQL](https://github.com/C4AI/gap-text2sql). [paper published in Springer-Nature - International Journal of Information Technology](https://doi.org/10.1007/s41870-023-01342-3), [here the SharedIt link](https://rdcu.be/dff19). [here the pre-print in arXiv](https://arxiv.org/abs/2306.14256). # mRAT-SQL+GAP ## mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformer Marcelo Archanjo José, Fabio Gagliardi Cozman The translation of natural language questions to SQL queries has attracted growing attention, in particular in connection with transformers and similar language models. A large number of techniques are geared towards the English language; in this work, we thus investigated translation to SQL when input questions are given in the Portuguese language. To do so, we properly adapted state-of-the-art tools and resources. We changed the RAT-SQL+GAP system by relying on a multilingual BART model (we report tests with other language models), and we produced a translated version of the Spider dataset. Our experiments expose interesting phenomena that arise when non-English languages are targeted; in particular, it is better to train with original and translated training datasets together, even if a single target language is desired. This multilingual BART model fine-tuned with a double-size training dataset (English and Portuguese) achieved 83% of the baseline, making inferences for the Portuguese test dataset. This investigation can help other researchers to produce results in Machine Learning in a language different from English. Our multilingual ready version of RAT-SQL+GAP and the data are available, open-sourced as mRAT-SQL+GAP at: [mRAT-SQL](https://github.com/C4AI/gap-text2sql). BRACIS 2021: [paper published in Springer Lecture Notes in Computer Science](https://link.springer.com/chapter/10.1007%2F978-3-030-91699-2_35), [here the pre-print in arXiv](https://arxiv.org/abs/2110.03546). Based on: RAT-SQL+GAP: [Github](https://github.com/awslabs/gap-text2sql). Paper: [AAAI 2021 paper](https://arxiv.org/abs/2012.10309)
arieg/bw_spec_cls_4_15_s_200
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1278' '1': '1417' '2': '1427' '3': '1443' splits: - name: train num_bytes: 42892698.0 num_examples: 800 - name: test num_bytes: 1071895.0 num_examples: 20 download_size: 38128383 dataset_size: 43964593.0 --- # Dataset Card for "bw_spec_cls_4_15_s_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shivanshjayara2991/ner_resume_data
--- license: other ---
rdiazconcha/marketing-synthetic
--- dataset_info: features: - name: product dtype: string - name: description dtype: string - name: marketing_email dtype: string splits: - name: train num_bytes: 20249 num_examples: 10 download_size: 27613 dataset_size: 20249 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "marketing-synthetic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/autotrain-data-french-ner-blank-model
Invalid username or password.
a98zhang/ibm_argument_example
--- pretty_name: example_ibm ---
ChristophSchuhmann/Chess-Selfplay
--- license: apache-2.0 ---
andersonbcdefg/MEDI-NQ-subset_with_margins
--- dataset_info: features: - name: query dtype: string - name: pos dtype: string - name: neg dtype: string - name: source dtype: string - name: qp_sim dtype: float32 - name: qn_sim dtype: float32 - name: pn_sim dtype: float32 - name: margin dtype: float64 splits: - name: train num_bytes: 32571587.635637943 num_examples: 35797 download_size: 30942084 dataset_size: 32571587.635637943 configs: - config_name: default data_files: - split: train path: data/train-* ---
cyrilzhang/TinyStories2-ascii-bpe-1600
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 2453198100 num_examples: 598341 - name: validation num_bytes: 24690200 num_examples: 6022 download_size: 856137162 dataset_size: 2477888300 --- # Dataset Card for "TinyStories2-ascii-bpe-1600" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ChristophSchuhmann/Mega-Fast-KNN-Captioning
--- license: apache-2.0 ---
yjernite/prof_images_blip__Lykon-DreamShaper
--- dataset_info: features: - name: images dtype: image - name: embeddings sequence: float32 splits: - name: bartender num_bytes: 4232353.0 num_examples: 100 - name: facilities_manager num_bytes: 3233702.0 num_examples: 100 - name: accountant num_bytes: 3301253.0 num_examples: 100 - name: graphic_designer num_bytes: 3779936.0 num_examples: 100 - name: financial_manager num_bytes: 3032824.0 num_examples: 100 - name: baker num_bytes: 3760855.0 num_examples: 100 - name: artist num_bytes: 3321552.0 num_examples: 100 - name: author num_bytes: 3841657.0 num_examples: 100 - name: clergy num_bytes: 3326689.0 num_examples: 100 - name: customer_service_representative num_bytes: 3353667.0 num_examples: 100 - name: dental_hygienist num_bytes: 3116590.0 num_examples: 100 - name: electrician num_bytes: 4444433.0 num_examples: 100 - name: head_cook num_bytes: 3711054.0 num_examples: 100 - name: health_technician num_bytes: 3208097.0 num_examples: 100 - name: carpet_installer num_bytes: 4231786.0 num_examples: 100 - name: civil_engineer num_bytes: 3887933.0 num_examples: 100 - name: ceo num_bytes: 2725789.0 num_examples: 100 - name: computer_support_specialist num_bytes: 3768802.0 num_examples: 100 - name: dentist num_bytes: 3051311.0 num_examples: 100 - name: butcher num_bytes: 4473092.0 num_examples: 100 - name: courier num_bytes: 3220269.0 num_examples: 100 - name: computer_programmer num_bytes: 4013303.0 num_examples: 100 - name: correctional_officer num_bytes: 3250295.0 num_examples: 100 - name: executive_assistant num_bytes: 3109178.0 num_examples: 100 - name: designer num_bytes: 3360493.0 num_examples: 100 - name: groundskeeper num_bytes: 3526805.0 num_examples: 100 - name: aerospace_engineer num_bytes: 4889373.0 num_examples: 100 - name: data_entry_keyer num_bytes: 3810901.0 num_examples: 100 - name: event_planner num_bytes: 3416510.0 num_examples: 100 - name: cook num_bytes: 3783118.0 num_examples: 100 - name: hairdresser num_bytes: 3197788.0 num_examples: 100 - name: farmer num_bytes: 4224326.0 num_examples: 100 - name: construction_worker num_bytes: 3595787.0 num_examples: 100 - name: air_conditioning_installer num_bytes: 4078377.0 num_examples: 100 - name: electrical_engineer num_bytes: 5068341.0 num_examples: 100 - name: career_counselor num_bytes: 3402257.0 num_examples: 100 - name: clerk num_bytes: 3603897.0 num_examples: 100 - name: director num_bytes: 3015590.0 num_examples: 100 - name: fast_food_worker num_bytes: 3902204.0 num_examples: 100 - name: cleaner num_bytes: 2822728.0 num_examples: 100 - name: computer_systems_analyst num_bytes: 4211576.0 num_examples: 100 - name: dental_assistant num_bytes: 3135047.0 num_examples: 100 - name: architect num_bytes: 3334524.0 num_examples: 100 - name: drywall_installer num_bytes: 3186332.0 num_examples: 100 - name: childcare_worker num_bytes: 3723729.0 num_examples: 100 - name: file_clerk num_bytes: 4124578.0 num_examples: 100 - name: community_manager num_bytes: 2923881.0 num_examples: 100 - name: carpenter num_bytes: 4186317.0 num_examples: 100 - name: claims_appraiser num_bytes: 3668012.0 num_examples: 100 - name: dispatcher num_bytes: 4311103.0 num_examples: 100 - name: cashier num_bytes: 4015653.0 num_examples: 100 - name: detective num_bytes: 2545399.0 num_examples: 100 - name: financial_advisor num_bytes: 3101141.0 num_examples: 100 - name: engineer num_bytes: 4143278.0 num_examples: 100 - name: dishwasher num_bytes: 4891231.0 num_examples: 100 - name: fitness_instructor num_bytes: 3356902.0 num_examples: 100 - name: credit_counselor num_bytes: 3340328.0 num_examples: 100 - name: doctor num_bytes: 3038762.0 num_examples: 100 - name: compliance_officer num_bytes: 3241075.0 num_examples: 100 - name: aide num_bytes: 3472385.0 num_examples: 100 - name: bus_driver num_bytes: 4379280.0 num_examples: 100 - name: financial_analyst num_bytes: 3730273.0 num_examples: 100 - name: firefighter num_bytes: 4226861.0 num_examples: 100 - name: coach num_bytes: 3364291.0 num_examples: 100 download_size: 243149155 dataset_size: 232746873.0 --- # Dataset Card for "prof_images_blip__Lykon-DreamShaper" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Back-up/chung-khoan-demo-13
--- dataset_info: features: - name: url dtype: string - name: title dtype: string - name: date dtype: string - name: view struct: - name: number_of_response dtype: string - name: number_of_view dtype: string - name: content list: - name: res dtype: string splits: - name: train num_bytes: 124799388 num_examples: 25736 download_size: 44124158 dataset_size: 124799388 configs: - config_name: default data_files: - split: train path: data/train-* ---
amazingvince/sft-max-2.1
--- dataset_info: features: - name: text dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 9385512725 num_examples: 2363460 - name: test num_bytes: 9211485 num_examples: 2366 download_size: 4947814472 dataset_size: 9394724210 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
CyberHarem/warspite_azurlane
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of warspite/ウォースパイト/厌战 (Azur Lane) This is the dataset of warspite/ウォースパイト/厌战 (Azur Lane), containing 222 images and their tags. The core tags of this character are `blonde_hair, long_hair, purple_eyes, bangs, hair_between_eyes, headgear, sidelocks, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 222 | 273.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/warspite_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 222 | 159.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/warspite_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 531 | 339.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/warspite_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 222 | 243.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/warspite_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 531 | 476.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/warspite_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/warspite_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 44 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, white_gloves, long_sleeves, epaulettes, side-tie_panties, black_panties, looking_at_viewer, white_scarf, jacket, holding_sword, simple_background, blush, open_mouth, white_background, smile | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, christmas, looking_at_viewer, santa_costume, santa_hat, solo, white_scarf, earmuffs, white_pantyhose, hair_ears, holding, red_headwear, :d, blush, boots, fur_trim, open_mouth, red_dress, red_footwear, simple_background, white_background, animal_ears, bare_shoulders, bell, closed_mouth, detached_sleeves, gift, mittens, red_gloves, sack | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | white_gloves | long_sleeves | epaulettes | side-tie_panties | black_panties | looking_at_viewer | white_scarf | jacket | holding_sword | simple_background | blush | open_mouth | white_background | smile | christmas | santa_costume | santa_hat | earmuffs | white_pantyhose | hair_ears | holding | red_headwear | :d | boots | fur_trim | red_dress | red_footwear | animal_ears | bare_shoulders | bell | closed_mouth | detached_sleeves | gift | mittens | red_gloves | sack | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:---------------|:-------------|:-------------------|:----------------|:--------------------|:--------------|:---------|:----------------|:--------------------|:--------|:-------------|:-------------------|:--------|:------------|:----------------|:------------|:-----------|:------------------|:------------|:----------|:---------------|:-----|:--------|:-----------|:------------|:---------------|:--------------|:-----------------|:-------|:---------------|:-------------------|:-------|:----------|:-------------|:-------| | 0 | 44 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | | | | X | X | | | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
spycoder/rawruwurawr
--- license: mit ---
CyberHarem/gridley_azurlane
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of gridley/グリッドレイ/格里德利 (Azur Lane) This is the dataset of gridley/グリッドレイ/格里德利 (Azur Lane), containing 12 images and their tags. The core tags of this character are `blonde_hair, blue_eyes, bangs, hair_between_eyes, ahoge, long_hair, bow, hair_ornament, two_side_up, drill_hair, red_bow, animal_ears, deer_ears, ribbon`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 12 | 16.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 12 | 9.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 30 | 20.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 12 | 14.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 30 | 29.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gridley_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/gridley_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | blush, 1girl, bare_shoulders, looking_at_viewer, smile, solo, holding, open_mouth, sleeveless, thighhighs, camera, christmas, red_dress, reindeer_antlers, santa_costume, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | 1girl | bare_shoulders | looking_at_viewer | smile | solo | holding | open_mouth | sleeveless | thighhighs | camera | christmas | red_dress | reindeer_antlers | santa_costume | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------------|:--------------------|:--------|:-------|:----------|:-------------|:-------------|:-------------|:---------|:------------|:------------|:-------------------|:----------------|:-------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
open-llm-leaderboard/details_CalderaAI__13B-Legerdemain-L2
--- pretty_name: Evaluation run of CalderaAI/13B-Legerdemain-L2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CalderaAI/13B-Legerdemain-L2](https://huggingface.co/CalderaAI/13B-Legerdemain-L2)\ \ 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_CalderaAI__13B-Legerdemain-L2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-12T20:33:10.328879](https://huggingface.co/datasets/open-llm-leaderboard/details_CalderaAI__13B-Legerdemain-L2/blob/main/results_2023-10-12T20-33-10.328879.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\": 0.002726510067114094,\n\ \ \"em_stderr\": 0.0005340111700415904,\n \"f1\": 0.06216547818791966,\n\ \ \"f1_stderr\": 0.0013785278979549318,\n \"acc\": 0.4412861505062612,\n\ \ \"acc_stderr\": 0.010705008172209724\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.002726510067114094,\n \"em_stderr\": 0.0005340111700415904,\n\ \ \"f1\": 0.06216547818791966,\n \"f1_stderr\": 0.0013785278979549318\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13040181956027294,\n \ \ \"acc_stderr\": 0.0092756303245541\n },\n \"harness|winogrande|5\":\ \ {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.01213438601986535\n\ \ }\n}\n```" repo_url: https://huggingface.co/CalderaAI/13B-Legerdemain-L2 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_09T11_34_37.986977 path: - '**/details_harness|arc:challenge|25_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-09T11:34:37.986977.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_12T20_33_10.328879 path: - '**/details_harness|drop|3_2023-10-12T20-33-10.328879.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-12T20-33-10.328879.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_12T20_33_10.328879 path: - '**/details_harness|gsm8k|5_2023-10-12T20-33-10.328879.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-12T20-33-10.328879.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hellaswag|10_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:34:37.986977.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T11:34:37.986977.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_09T11_34_37.986977 path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T11:34:37.986977.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-09T11:34:37.986977.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_12T20_33_10.328879 path: - '**/details_harness|winogrande|5_2023-10-12T20-33-10.328879.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-12T20-33-10.328879.parquet' - config_name: results data_files: - split: 2023_08_09T11_34_37.986977 path: - results_2023-08-09T11:34:37.986977.parquet - split: 2023_10_12T20_33_10.328879 path: - results_2023-10-12T20-33-10.328879.parquet - split: latest path: - results_2023-10-12T20-33-10.328879.parquet --- # Dataset Card for Evaluation run of CalderaAI/13B-Legerdemain-L2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CalderaAI/13B-Legerdemain-L2 - **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 [CalderaAI/13B-Legerdemain-L2](https://huggingface.co/CalderaAI/13B-Legerdemain-L2) 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_CalderaAI__13B-Legerdemain-L2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-12T20:33:10.328879](https://huggingface.co/datasets/open-llm-leaderboard/details_CalderaAI__13B-Legerdemain-L2/blob/main/results_2023-10-12T20-33-10.328879.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": 0.002726510067114094, "em_stderr": 0.0005340111700415904, "f1": 0.06216547818791966, "f1_stderr": 0.0013785278979549318, "acc": 0.4412861505062612, "acc_stderr": 0.010705008172209724 }, "harness|drop|3": { "em": 0.002726510067114094, "em_stderr": 0.0005340111700415904, "f1": 0.06216547818791966, "f1_stderr": 0.0013785278979549318 }, "harness|gsm8k|5": { "acc": 0.13040181956027294, "acc_stderr": 0.0092756303245541 }, "harness|winogrande|5": { "acc": 0.7521704814522494, "acc_stderr": 0.01213438601986535 } } ``` ### 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]
zolak/twitter_dataset_78_1713181846
--- 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: 280052 num_examples: 702 download_size: 147591 dataset_size: 280052 configs: - config_name: default data_files: - split: train path: data/train-* ---
MagedSaeed/AskTheTeacherDataset
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: index dtype: string - name: instruction_en dtype: string - name: output_en dtype: string splits: - name: train num_bytes: 649329 num_examples: 918 download_size: 320236 dataset_size: 649329 configs: - config_name: default data_files: - split: train path: data/train-* ---
FudanSELab/CodeGen4Libs
--- license: mit tags: - code-generation pretty_name: CodeGen4Libs Dataset size_categories: - 100K<n<1M --- # Dataset Card for FudanSELab CodeGen4Libs Dataset ## Dataset Description - **Repository:** [GitHub Repository](https://github.com/FudanSELab/codegen4libs) - **Paper:** [CodeGen4Libs: A Two-stage Approach for Library-oriented Code Generation](https://mingwei-liu.github.io/publication/2023-08-18-ase-CodeGen4Libs) ### Dataset Summary This dataset is used in the ASE2023 paper titled ["CodeGen4Libs: A Two-stage Approach for Library-oriented Code Generation"](https://mingwei-liu.github.io/publication/2023-08-18-ase-CodeGen4Libs). ### Languages [More Information Needed] ## Dataset Structure ```python from datasets import load_dataset dataset = load_dataset("FudanSELab/CodeGen4Libs") DatasetDict({ train: Dataset({ features: ['id', 'method', 'clean_method', 'doc', 'comment', 'method_name', 'extra', 'imports_info', 'libraries_info', 'input_str', 'input_ids', 'tokenized_input_str', 'input_token_length', 'labels', 'tokenized_labels_str', 'labels_token_length', 'retrieved_imports_info', 'retrieved_code', 'imports', 'cluster_imports_info', 'libraries', 'attention_mask'], num_rows: 391811 }) validation: Dataset({ features: ['id', 'method', 'clean_method', 'doc', 'comment', 'method_name', 'extra', 'imports_info', 'libraries_info', 'input_str', 'input_ids', 'tokenized_input_str', 'input_token_length', 'labels', 'tokenized_labels_str', 'labels_token_length', 'retrieved_imports_info', 'retrieved_code', 'imports', 'cluster_imports_info', 'libraries', 'attention_mask'], num_rows: 5967 }) test: Dataset({ features: ['id', 'method', 'clean_method', 'doc', 'comment', 'method_name', 'extra', 'imports_info', 'libraries_info', 'input_str', 'input_ids', 'tokenized_input_str', 'input_token_length', 'labels', 'tokenized_labels_str', 'labels_token_length', 'retrieved_imports_info', 'retrieved_code', 'imports', 'cluster_imports_info', 'libraries', 'attention_mask'], num_rows: 6002 }) }) ``` ### Data Fields The specific data fields for each tuple are delineated as follows: - id: the unique identifier for each tuple. - method: the original method-level code for each tuple. - clean_method: the ground-truth method-level code for each task. - doc: the document of method-level code for each tuple. - comment: the natural language description for each tuple. - method_name: the name of the method. - extra: extra information on the code repository to which the method level code belongs. - license: the license of code repository. - path: the path of code repository. - repo_name: the name of code repository. - size: the size of code repository. - imports_info: the import statements for each tuple. - libraries_info: the libraries info for each tuple. - input_str: the design of model input. - input_ids: the ids of tokenized input. - tokenized_input_str: the tokenized input. - input_token_length: the length of the tokenized input. - labels: the ids of tokenized output. - tokenized_labels_str: the tokenized output. - labels_token_length: the length of the the tokenized output. - retrieved_imports_info: the retrieved import statements for each tuple. - retrieved_code: the retrieved method-level code for each tuple. - imports: the imported packages of each import statement. - cluster_imports_info: cluster import information of code. - libraries: libraries used by the code. - attention_mask: attention mask for the input. ### Data Splits The dataset is splited into a training set, a validation set, and a test set, with 391811, 5967, and 6002 data rows respectively. ## Additional Information ### Citation Information ``` @inproceedings{ase2023codegen4libs, author = {Mingwei Liu and Tianyong Yang and Yiling Lou and Xueying Du and Ying Wang and and Xin Peng}, title = {{CodeGen4Libs}: A Two-stage Approach for Library-oriented Code Generation}, booktitle = {38th {IEEE/ACM} International Conference on Automated Software Engineering, {ASE} 2023, Kirchberg, Luxembourg, September 11-15, 2023}, pages = {0--0}, publisher = {{IEEE}}, year = {2023}, } ```
amishshah/imbalanced_4
--- dataset_info: features: - name: title dtype: string - name: label dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 45166669.74 num_examples: 27000 - name: test num_bytes: 5018518.86 num_examples: 3000 download_size: 27086501 dataset_size: 50185188.6 --- # Dataset Card for "imbalanced_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
reyrg/thermal-camera_v2
--- license: cc-by-4.0 dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 766087220.0 num_examples: 546 download_size: 49415770 dataset_size: 766087220.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
ortofasfat/AAAI_Swahili_dataset
--- license: cc task_categories: - question-answering - text-generation language: - sw - en pretty_name: Dolly_Swahili size_categories: - 10K<n<100K --- # README for Swahili Translated Dataset from Toloka ## Dataset Description This dataset is a Dolly 15k translated from English to Swahili, filtered and processed using the Toloka platform. It includes various contexts, responses, and instructions from diverse domains, providing a rich resource for natural language processing tasks, particularly for those focusing on the Swahili language. ### Data Fields - **task_id**: A unique identifier for each task in the dataset. - **INPUT:context_tr**: The context of the task translated into Swahili. - **INPUT:context_src**: The original context of the task in English. - **INPUT:response_tr**: The response or answer to the task, translated into Swahili. - **INPUT:response_src**: The original response or answer to the task in English. - **INPUT:instruction_tr**: The instruction for the task, translated into Swahili. - **INPUT:instruction_src**: The original instruction for the task in English. - **toloka probabilities**: Probabilities indicating the confidence or quality score associated with each task, as evaluated on the Toloka platform. ### Data Format The dataset is provided in CSV format. ## Dataset Creation ### Curation Rationale This dataset was curated to enhance the resources available for Swahili natural language processing. It aims to provide a robust set of data for translation tasks, question-answering systems, and other language modeling tasks in Swahili. ### Source Data #### Initial Data Collection and Normalization The initial data collection was taken from Dolly-15K ### Dataset Curators This dataset was curated by Nazar Beknazarov, leveraging the Toloka platform for translation and filtering. ### Citation Information Please include citation information here, if available.
anugrah1112/test_gpt
--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 32008.0 num_examples: 2 - name: test num_bytes: 16004 num_examples: 1 download_size: 30762 dataset_size: 48012.0 --- # Dataset Card for "test_gpt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_vicgalleorg__TruthfulQwen1.5-4B
--- pretty_name: Evaluation run of vicgalleorg/TruthfulQwen1.5-4B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [vicgalleorg/TruthfulQwen1.5-4B](https://huggingface.co/vicgalleorg/TruthfulQwen1.5-4B)\ \ 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_vicgalleorg__TruthfulQwen1.5-4B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-04T19:39:20.098262](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalleorg__TruthfulQwen1.5-4B/blob/main/results_2024-03-04T19-39-20.098262.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\": 0.5591026780671433,\n\ \ \"acc_stderr\": 0.034055265814276585,\n \"acc_norm\": 0.5614839252312046,\n\ \ \"acc_norm_stderr\": 0.03474577841927621,\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.016403989469907822,\n \"mc2\": 0.5060376733678527,\n\ \ \"mc2_stderr\": 0.014856853869795477\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.439419795221843,\n \"acc_stderr\": 0.014503747823580125,\n\ \ \"acc_norm\": 0.4709897610921502,\n \"acc_norm_stderr\": 0.014586776355294316\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5285799641505676,\n\ \ \"acc_stderr\": 0.004981623292196192,\n \"acc_norm\": 0.7132045409281019,\n\ \ \"acc_norm_stderr\": 0.004513409114983842\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5185185185185185,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6118421052631579,\n \"acc_stderr\": 0.03965842097512744,\n\ \ \"acc_norm\": 0.6118421052631579,\n \"acc_norm_stderr\": 0.03965842097512744\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5849056603773585,\n \"acc_stderr\": 0.03032594578928611,\n\ \ \"acc_norm\": 0.5849056603773585,\n \"acc_norm_stderr\": 0.03032594578928611\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5138888888888888,\n\ \ \"acc_stderr\": 0.041795966175810016,\n \"acc_norm\": 0.5138888888888888,\n\ \ \"acc_norm_stderr\": 0.041795966175810016\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\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\ : 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5260115606936416,\n\ \ \"acc_stderr\": 0.038073017265045125,\n \"acc_norm\": 0.5260115606936416,\n\ \ \"acc_norm_stderr\": 0.038073017265045125\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.046550104113196177,\n\ \ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.046550104113196177\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n\ \ \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.49361702127659574,\n \"acc_stderr\": 0.032683358999363366,\n\ \ \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.032683358999363366\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4708994708994709,\n \"acc_stderr\": 0.025707658614154954,\n \"\ acc_norm\": 0.4708994708994709,\n \"acc_norm_stderr\": 0.025707658614154954\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3492063492063492,\n\ \ \"acc_stderr\": 0.04263906892795133,\n \"acc_norm\": 0.3492063492063492,\n\ \ \"acc_norm_stderr\": 0.04263906892795133\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6387096774193548,\n\ \ \"acc_stderr\": 0.02732754844795754,\n \"acc_norm\": 0.6387096774193548,\n\ \ \"acc_norm_stderr\": 0.02732754844795754\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n\ \ \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\ : 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n\ \ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124495,\n \"\ acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124495\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7616580310880829,\n \"acc_stderr\": 0.030748905363909916,\n\ \ \"acc_norm\": 0.7616580310880829,\n \"acc_norm_stderr\": 0.030748905363909916\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5205128205128206,\n \"acc_stderr\": 0.02532966316348994,\n \ \ \"acc_norm\": 0.5205128205128206,\n \"acc_norm_stderr\": 0.02532966316348994\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083025,\n \ \ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083025\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.542016806722689,\n \"acc_stderr\": 0.03236361111951941,\n \ \ \"acc_norm\": 0.542016806722689,\n \"acc_norm_stderr\": 0.03236361111951941\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7155963302752294,\n \"acc_stderr\": 0.019342036587702588,\n \"\ acc_norm\": 0.7155963302752294,\n \"acc_norm_stderr\": 0.019342036587702588\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"\ acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7009803921568627,\n \"acc_stderr\": 0.03213325717373617,\n \"\ acc_norm\": 0.7009803921568627,\n \"acc_norm_stderr\": 0.03213325717373617\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7130801687763713,\n \"acc_stderr\": 0.02944377302259469,\n \ \ \"acc_norm\": 0.7130801687763713,\n \"acc_norm_stderr\": 0.02944377302259469\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6098654708520179,\n\ \ \"acc_stderr\": 0.03273766725459157,\n \"acc_norm\": 0.6098654708520179,\n\ \ \"acc_norm_stderr\": 0.03273766725459157\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.648854961832061,\n \"acc_stderr\": 0.04186445163013751,\n\ \ \"acc_norm\": 0.648854961832061,\n \"acc_norm_stderr\": 0.04186445163013751\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6759259259259259,\n\ \ \"acc_stderr\": 0.045245960070300476,\n \"acc_norm\": 0.6759259259259259,\n\ \ \"acc_norm_stderr\": 0.045245960070300476\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6196319018404908,\n \"acc_stderr\": 0.038142698932618374,\n\ \ \"acc_norm\": 0.6196319018404908,\n \"acc_norm_stderr\": 0.038142698932618374\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\ \ \"acc_stderr\": 0.024662496845209794,\n \"acc_norm\": 0.8290598290598291,\n\ \ \"acc_norm_stderr\": 0.024662496845209794\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7394636015325671,\n\ \ \"acc_stderr\": 0.01569600856380708,\n \"acc_norm\": 0.7394636015325671,\n\ \ \"acc_norm_stderr\": 0.01569600856380708\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.661849710982659,\n \"acc_stderr\": 0.025469770149400172,\n\ \ \"acc_norm\": 0.661849710982659,\n \"acc_norm_stderr\": 0.025469770149400172\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6372549019607843,\n \"acc_stderr\": 0.027530078447110307,\n\ \ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.027530078447110307\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6109324758842444,\n\ \ \"acc_stderr\": 0.027690337536485372,\n \"acc_norm\": 0.6109324758842444,\n\ \ \"acc_norm_stderr\": 0.027690337536485372\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5864197530864198,\n \"acc_stderr\": 0.02740204204026996,\n\ \ \"acc_norm\": 0.5864197530864198,\n \"acc_norm_stderr\": 0.02740204204026996\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.02968010556502904,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.02968010556502904\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41395045632333766,\n\ \ \"acc_stderr\": 0.012579699631289262,\n \"acc_norm\": 0.41395045632333766,\n\ \ \"acc_norm_stderr\": 0.012579699631289262\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.45955882352941174,\n \"acc_stderr\": 0.030273325077345755,\n\ \ \"acc_norm\": 0.45955882352941174,\n \"acc_norm_stderr\": 0.030273325077345755\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.545751633986928,\n \"acc_stderr\": 0.0201429745537952,\n \ \ \"acc_norm\": 0.545751633986928,\n \"acc_norm_stderr\": 0.0201429745537952\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.04582004841505418,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.04582004841505418\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.030555316755573637,\n\ \ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.030555316755573637\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.736318407960199,\n\ \ \"acc_stderr\": 0.03115715086935556,\n \"acc_norm\": 0.736318407960199,\n\ \ \"acc_norm_stderr\": 0.03115715086935556\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.78,\n \"acc_stderr\": 0.041633319989322626,\n \ \ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.041633319989322626\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4397590361445783,\n\ \ \"acc_stderr\": 0.03864139923699121,\n \"acc_norm\": 0.4397590361445783,\n\ \ \"acc_norm_stderr\": 0.03864139923699121\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7076023391812866,\n \"acc_stderr\": 0.034886477134579215,\n\ \ \"acc_norm\": 0.7076023391812866,\n \"acc_norm_stderr\": 0.034886477134579215\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.016403989469907822,\n \"mc2\": 0.5060376733678527,\n\ \ \"mc2_stderr\": 0.014856853869795477\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6685082872928176,\n \"acc_stderr\": 0.013230397198964657\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5253980288097043,\n \ \ \"acc_stderr\": 0.013754705089112309\n }\n}\n```" repo_url: https://huggingface.co/vicgalleorg/TruthfulQwen1.5-4B 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_04T19_39_20.098262 path: - '**/details_harness|arc:challenge|25_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-04T19-39-20.098262.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|gsm8k|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hellaswag|10_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-04T19-39-20.098262.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-management|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T19-39-20.098262.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|truthfulqa:mc|0_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-04T19-39-20.098262.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_04T19_39_20.098262 path: - '**/details_harness|winogrande|5_2024-03-04T19-39-20.098262.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-04T19-39-20.098262.parquet' - config_name: results data_files: - split: 2024_03_04T19_39_20.098262 path: - results_2024-03-04T19-39-20.098262.parquet - split: latest path: - results_2024-03-04T19-39-20.098262.parquet --- # Dataset Card for Evaluation run of vicgalleorg/TruthfulQwen1.5-4B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [vicgalleorg/TruthfulQwen1.5-4B](https://huggingface.co/vicgalleorg/TruthfulQwen1.5-4B) 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_vicgalleorg__TruthfulQwen1.5-4B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-04T19:39:20.098262](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalleorg__TruthfulQwen1.5-4B/blob/main/results_2024-03-04T19-39-20.098262.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": 0.5591026780671433, "acc_stderr": 0.034055265814276585, "acc_norm": 0.5614839252312046, "acc_norm_stderr": 0.03474577841927621, "mc1": 0.32558139534883723, "mc1_stderr": 0.016403989469907822, "mc2": 0.5060376733678527, "mc2_stderr": 0.014856853869795477 }, "harness|arc:challenge|25": { "acc": 0.439419795221843, "acc_stderr": 0.014503747823580125, "acc_norm": 0.4709897610921502, "acc_norm_stderr": 0.014586776355294316 }, "harness|hellaswag|10": { "acc": 0.5285799641505676, "acc_stderr": 0.004981623292196192, "acc_norm": 0.7132045409281019, "acc_norm_stderr": 0.004513409114983842 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5185185185185185, "acc_stderr": 0.043163785995113245, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.043163785995113245 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6118421052631579, "acc_stderr": 0.03965842097512744, "acc_norm": 0.6118421052631579, "acc_norm_stderr": 0.03965842097512744 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5849056603773585, "acc_stderr": 0.03032594578928611, "acc_norm": 0.5849056603773585, "acc_norm_stderr": 0.03032594578928611 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5138888888888888, "acc_stderr": 0.041795966175810016, "acc_norm": 0.5138888888888888, "acc_norm_stderr": 0.041795966175810016 }, "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": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5260115606936416, "acc_stderr": 0.038073017265045125, "acc_norm": 0.5260115606936416, "acc_norm_stderr": 0.038073017265045125 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3235294117647059, "acc_stderr": 0.046550104113196177, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.046550104113196177 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.49361702127659574, "acc_stderr": 0.032683358999363366, "acc_norm": 0.49361702127659574, "acc_norm_stderr": 0.032683358999363366 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5241379310344828, "acc_stderr": 0.041618085035015295, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4708994708994709, "acc_stderr": 0.025707658614154954, "acc_norm": 0.4708994708994709, "acc_norm_stderr": 0.025707658614154954 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3492063492063492, "acc_stderr": 0.04263906892795133, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.04263906892795133 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6387096774193548, "acc_stderr": 0.02732754844795754, "acc_norm": 0.6387096774193548, "acc_norm_stderr": 0.02732754844795754 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.458128078817734, "acc_stderr": 0.03505630140785741, "acc_norm": 0.458128078817734, "acc_norm_stderr": 0.03505630140785741 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7333333333333333, "acc_stderr": 0.03453131801885417, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.03453131801885417 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7525252525252525, "acc_stderr": 0.030746300742124495, "acc_norm": 0.7525252525252525, "acc_norm_stderr": 0.030746300742124495 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7616580310880829, "acc_stderr": 0.030748905363909916, "acc_norm": 0.7616580310880829, "acc_norm_stderr": 0.030748905363909916 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5205128205128206, "acc_stderr": 0.02532966316348994, "acc_norm": 0.5205128205128206, "acc_norm_stderr": 0.02532966316348994 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35185185185185186, "acc_stderr": 0.029116617606083025, "acc_norm": 0.35185185185185186, "acc_norm_stderr": 0.029116617606083025 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.542016806722689, "acc_stderr": 0.03236361111951941, "acc_norm": 0.542016806722689, "acc_norm_stderr": 0.03236361111951941 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7155963302752294, "acc_stderr": 0.019342036587702588, "acc_norm": 0.7155963302752294, "acc_norm_stderr": 0.019342036587702588 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4305555555555556, "acc_stderr": 0.03376922151252336, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.03376922151252336 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7009803921568627, "acc_stderr": 0.03213325717373617, "acc_norm": 0.7009803921568627, "acc_norm_stderr": 0.03213325717373617 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7130801687763713, "acc_stderr": 0.02944377302259469, "acc_norm": 0.7130801687763713, "acc_norm_stderr": 0.02944377302259469 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6098654708520179, "acc_stderr": 0.03273766725459157, "acc_norm": 0.6098654708520179, "acc_norm_stderr": 0.03273766725459157 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.648854961832061, "acc_stderr": 0.04186445163013751, "acc_norm": 0.648854961832061, "acc_norm_stderr": 0.04186445163013751 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7107438016528925, "acc_stderr": 0.04139112727635463, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.04139112727635463 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6759259259259259, "acc_stderr": 0.045245960070300476, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.045245960070300476 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6196319018404908, "acc_stderr": 0.038142698932618374, "acc_norm": 0.6196319018404908, "acc_norm_stderr": 0.038142698932618374 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8290598290598291, "acc_stderr": 0.024662496845209794, "acc_norm": 0.8290598290598291, "acc_norm_stderr": 0.024662496845209794 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7394636015325671, "acc_stderr": 0.01569600856380708, "acc_norm": 0.7394636015325671, "acc_norm_stderr": 0.01569600856380708 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.661849710982659, "acc_stderr": 0.025469770149400172, "acc_norm": 0.661849710982659, "acc_norm_stderr": 0.025469770149400172 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6372549019607843, "acc_stderr": 0.027530078447110307, "acc_norm": 0.6372549019607843, "acc_norm_stderr": 0.027530078447110307 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6109324758842444, "acc_stderr": 0.027690337536485372, "acc_norm": 0.6109324758842444, "acc_norm_stderr": 0.027690337536485372 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5864197530864198, "acc_stderr": 0.02740204204026996, "acc_norm": 0.5864197530864198, "acc_norm_stderr": 0.02740204204026996 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.450354609929078, "acc_stderr": 0.02968010556502904, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.02968010556502904 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.41395045632333766, "acc_stderr": 0.012579699631289262, "acc_norm": 0.41395045632333766, "acc_norm_stderr": 0.012579699631289262 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.45955882352941174, "acc_stderr": 0.030273325077345755, "acc_norm": 0.45955882352941174, "acc_norm_stderr": 0.030273325077345755 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.545751633986928, "acc_stderr": 0.0201429745537952, "acc_norm": 0.545751633986928, "acc_norm_stderr": 0.0201429745537952 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.04582004841505418, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.04582004841505418 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6489795918367347, "acc_stderr": 0.030555316755573637, "acc_norm": 0.6489795918367347, "acc_norm_stderr": 0.030555316755573637 }, "harness|hendrycksTest-sociology|5": { "acc": 0.736318407960199, "acc_stderr": 0.03115715086935556, "acc_norm": 0.736318407960199, "acc_norm_stderr": 0.03115715086935556 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.78, "acc_stderr": 0.041633319989322626, "acc_norm": 0.78, "acc_norm_stderr": 0.041633319989322626 }, "harness|hendrycksTest-virology|5": { "acc": 0.4397590361445783, "acc_stderr": 0.03864139923699121, "acc_norm": 0.4397590361445783, "acc_norm_stderr": 0.03864139923699121 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7076023391812866, "acc_stderr": 0.034886477134579215, "acc_norm": 0.7076023391812866, "acc_norm_stderr": 0.034886477134579215 }, "harness|truthfulqa:mc|0": { "mc1": 0.32558139534883723, "mc1_stderr": 0.016403989469907822, "mc2": 0.5060376733678527, "mc2_stderr": 0.014856853869795477 }, "harness|winogrande|5": { "acc": 0.6685082872928176, "acc_stderr": 0.013230397198964657 }, "harness|gsm8k|5": { "acc": 0.5253980288097043, "acc_stderr": 0.013754705089112309 } } ``` ## 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]
liuyanchen1015/MULTI_VALUE_sst2_completive_finish
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 3567 num_examples: 25 - name: test num_bytes: 9321 num_examples: 59 - name: train num_bytes: 139243 num_examples: 1071 download_size: 74683 dataset_size: 152131 --- # Dataset Card for "MULTI_VALUE_sst2_completive_finish" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_1713001988
--- 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: 2494820 num_examples: 6241 download_size: 1248080 dataset_size: 2494820 configs: - config_name: default data_files: - split: train path: data/train-* ---
jhu-clsp/jfleg
--- annotations_creators: - expert-generated language_creators: - found language: - en license: - cc-by-nc-sa-4.0 multilinguality: - monolingual - other-language-learner size_categories: - 1K<n<10K source_datasets: - extended|other-GUG-grammaticality-judgements task_categories: - text2text-generation task_ids: [] paperswithcode_id: jfleg pretty_name: JHU FLuency-Extended GUG corpus tags: - grammatical-error-correction dataset_info: features: - name: sentence dtype: string - name: corrections sequence: string splits: - name: validation num_bytes: 379979 num_examples: 755 - name: test num_bytes: 379699 num_examples: 748 download_size: 289093 dataset_size: 759678 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* --- # Dataset Card for JFLEG ## 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:** [Github](https://github.com/keisks/jfleg) - **Repository:** [Github](https://github.com/keisks/jfleg) - **Paper:** [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/) - **Leaderboard:** [Leaderboard](https://github.com/keisks/jfleg#leader-board-published-results) - **Point of Contact:** Courtney Napoles, Keisuke Sakaguchi ### Dataset Summary JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC systems with respect to fluency (extent to which a text is native-sounding) as well as grammaticality. For each source document, there are four human-written corrections. ### Supported Tasks and Leaderboards Grammatical error correction. ### Languages English (native as well as L2 writers) ## Dataset Structure ### Data Instances Each instance contains a source sentence and four corrections. For example: ```python { 'sentence': "They are moved by solar energy ." 'corrections': [ "They are moving by solar energy .", "They are moved by solar energy .", "They are moved by solar energy .", "They are propelled by solar energy ." ] } ``` ### Data Fields - sentence: original sentence written by an English learner - corrections: corrected versions by human annotators. The order of the annotations are consistent (eg first sentence will always be written by annotator "ref0"). ### Data Splits - This dataset contains 1511 examples in total and comprise a dev and test split. - There are 754 and 747 source sentences for dev and test, respectively. - Each sentence has 4 corresponding corrected versions. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). ### Citation Information This benchmark was proposed by [Napoles et al., 2020](https://arxiv.org/abs/1702.04066). ``` @InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort, author = {Napoles, Courtney and Sakaguchi, Keisuke and Tetreault, Joel}, title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction}, booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers}, month = {April}, year = {2017}, address = {Valencia, Spain}, publisher = {Association for Computational Linguistics}, pages = {229--234}, url = {http://www.aclweb.org/anthology/E17-2037} } @InProceedings{heilman-EtAl:2014:P14-2, author = {Heilman, Michael and Cahill, Aoife and Madnani, Nitin and Lopez, Melissa and Mulholland, Matthew and Tetreault, Joel}, title = {Predicting Grammaticality on an Ordinal Scale}, booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, month = {June}, year = {2014}, address = {Baltimore, Maryland}, publisher = {Association for Computational Linguistics}, pages = {174--180}, url = {http://www.aclweb.org/anthology/P14-2029} } ``` ### Contributions Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset.
khoomeik/gzipscale-0.41-10M
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 35351670 num_examples: 39063 download_size: 8069952 dataset_size: 35351670 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/otokura_yuuki_idolmastercinderellagirls
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of otokura_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls) This is the dataset of otokura_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `short_hair, grey_hair, bangs, black_eyes, grey_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 637.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 367.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1215 | 783.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 574.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1215 | 1.09 GiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/otokura_yuuki_idolmastercinderellagirls', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 7 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blush, looking_at_viewer, open_mouth, sailor_collar, sailor_dress, simple_background, solo, white_background, white_dress, wrist_cuffs, blue_bow, frills, :d, sleeveless_dress, bowtie, hair_ornament, collarbone, hair_bow, skirt_hold | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blazer, school_uniform, smile, school_bag, skirt, solo, looking_at_viewer, blush, neck_ribbon, open_mouth, blue_ribbon, shirt | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | blazer, blue_ribbon, blush, long_sleeves, neck_ribbon, school_uniform, white_background, white_shirt, 1girl, looking_at_viewer, simple_background, solo, upper_body, collared_shirt, :d, black_jacket, blue_jacket, closed_mouth, hand_up, open_jacket, open_mouth, vest | | 3 | 10 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, open_mouth, looking_at_viewer, midriff, solo, blush, navel, black_hair, shorts, :d, belt, suspenders, white_gloves, fingerless_gloves, mini_hat, simple_background, sleeveless, white_background | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, floral_print, looking_at_viewer, solo, obi, hair_flower, wide_sleeves, print_kimono, long_sleeves, open_mouth, upper_body, white_kimono, yukata, :d, closed_mouth, fur_collar, head_tilt, holding, pink_kimono | | 5 | 15 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, frilled_bikini, navel, solo, blush, looking_at_viewer, open_mouth, bikini_skirt, collarbone, day, pink_bikini, outdoors, sun_hat, :d, blue_sky, halterneck, straw_hat, cloud, ocean, plaid, hair_between_eyes, bare_shoulders, cowboy_shot, hat_bow, standing, water | | 6 | 17 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, solo, blush, looking_at_viewer, navel, open_mouth, collarbone, :d, midriff, sweat, short_shorts, simple_background, small_breasts, sports_bra, white_background, open_jacket, shoes | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, blue_sky, cloud, denim_shorts, earrings, looking_at_viewer, necklace, open_mouth, short_shorts, short_sleeves, solo, tied_shirt, hairband, midriff, navel, orange_shirt, outdoors, :d, belt, blue_shorts, blush, breasts, collarbone, cutoffs, day, flower, bracelet, clothes_writing, cowboy_shot, salute, sunlight, thighs, watch | | 8 | 5 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, blush, striped_shirt, collarbone, looking_at_viewer, open_mouth, solo, hooded_jacket, hoodie, pink_jacket, shorts, simple_background, smile, white_background, key_necklace | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, blush, pink_shirt, smile, solo, looking_at_viewer, outdoors, short_sleeves, sitting, open_mouth, blurry_background, collarbone, short_shorts, sweat, water_bottle | | 10 | 12 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, looking_at_viewer, solo, small_breasts, blush, armpits, covered_navel, cowboy_shot, school_swimsuit, smile, arms_up, collarbone, standing, blue_one-piece_swimsuit, white_background | | 11 | 11 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, blush, solo, looking_at_viewer, pink_bow, fur-trimmed_gloves, hair_bow, striped_bow, beret, black_headwear, brown_gloves, heart_earrings, sleeveless, brown_headwear, polka_dot, skirt, :d, breasts, jingle_bell, navel, open_mouth, plaid, rose, see-through, upper_body, white_background, bare_shoulders, closed_mouth, hair_between_eyes, pink_dress, sitting | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | open_mouth | sailor_collar | sailor_dress | simple_background | solo | white_background | white_dress | wrist_cuffs | blue_bow | frills | :d | sleeveless_dress | bowtie | hair_ornament | collarbone | hair_bow | skirt_hold | blazer | school_uniform | smile | school_bag | skirt | neck_ribbon | blue_ribbon | shirt | long_sleeves | white_shirt | upper_body | collared_shirt | black_jacket | blue_jacket | closed_mouth | hand_up | open_jacket | vest | midriff | navel | black_hair | shorts | belt | suspenders | white_gloves | fingerless_gloves | mini_hat | sleeveless | floral_print | obi | hair_flower | wide_sleeves | print_kimono | white_kimono | yukata | fur_collar | head_tilt | holding | pink_kimono | frilled_bikini | bikini_skirt | day | pink_bikini | outdoors | sun_hat | blue_sky | halterneck | straw_hat | cloud | ocean | plaid | hair_between_eyes | bare_shoulders | cowboy_shot | hat_bow | standing | water | sweat | short_shorts | small_breasts | sports_bra | shoes | denim_shorts | earrings | necklace | short_sleeves | tied_shirt | hairband | orange_shirt | blue_shorts | breasts | cutoffs | flower | bracelet | clothes_writing | salute | sunlight | thighs | watch | striped_shirt | hooded_jacket | hoodie | pink_jacket | key_necklace | pink_shirt | sitting | blurry_background | water_bottle | armpits | covered_navel | school_swimsuit | arms_up | blue_one-piece_swimsuit | pink_bow | fur-trimmed_gloves | striped_bow | beret | black_headwear | brown_gloves | heart_earrings | brown_headwear | polka_dot | jingle_bell | rose | see-through | pink_dress | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:--------------------|:-------------|:----------------|:---------------|:--------------------|:-------|:-------------------|:--------------|:--------------|:-----------|:---------|:-----|:-------------------|:---------|:----------------|:-------------|:-----------|:-------------|:---------|:-----------------|:--------|:-------------|:--------|:--------------|:--------------|:--------|:---------------|:--------------|:-------------|:-----------------|:---------------|:--------------|:---------------|:----------|:--------------|:-------|:----------|:--------|:-------------|:---------|:-------|:-------------|:---------------|:--------------------|:-----------|:-------------|:---------------|:------|:--------------|:---------------|:---------------|:---------------|:---------|:-------------|:------------|:----------|:--------------|:-----------------|:---------------|:------|:--------------|:-----------|:----------|:-----------|:-------------|:------------|:--------|:--------|:--------|:--------------------|:-----------------|:--------------|:----------|:-----------|:--------|:--------|:---------------|:----------------|:-------------|:--------|:---------------|:-----------|:-----------|:----------------|:-------------|:-----------|:---------------|:--------------|:----------|:----------|:---------|:-----------|:------------------|:---------|:-----------|:---------|:--------|:----------------|:----------------|:---------|:--------------|:---------------|:-------------|:----------|:--------------------|:---------------|:----------|:----------------|:------------------|:----------|:--------------------------|:-----------|:---------------------|:--------------|:--------|:-----------------|:---------------|:-----------------|:-----------------|:------------|:--------------|:-------|:--------------|:-------------| | 0 | 7 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | | X | X | X | | | | | X | | | | | | | X | X | | | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 10 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | | | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | | | | X | | | | | | X | | | | | | | | | | | | | | | X | | X | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 15 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | | | | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 17 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | X | | | X | X | X | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | X | | | | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | | | X | | | | | | | | | | | | | | | | | | | X | | X | | X | | | X | | | | | X | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 5 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | X | X | X | | | X | X | X | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | X | X | | | | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | 10 | 12 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | X | X | | | | | X | X | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | 11 | 11 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | X | X | X | X | | | | X | X | | | | | X | | | | | X | | | | | | X | | | | | | X | | | | X | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
ReginaFoley/doq_data_large_64
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 837579849.524 num_examples: 320194 download_size: 916615710 dataset_size: 837579849.524 configs: - config_name: default data_files: - split: train path: data/train-* ---
Severian/Internal-Knowledge-Map
--- language: - en license: mit --- # Internal Knowledge Map: Experiments in Deeper Understanding and Novel Thinking for LLMs --- **Designed for Cross-Discipline/Interconnected Critical Thinking, Nuanced Understanding, Diverse Role Playing and Innovative Problem Solving** By integrating a cohesively structured dataset emphasizing the interconnectedness of knowledge across a myriad of domains, exploring characters/role playing/community discourse, solving impossible problems and developing inner dialogues; this project aspires to elevate LLMs to deeper levels of understanding, reasoning, and innovation. Its core mission is to foster the development of an 'internal knowledge map' within LLMs, enabling them to not only process information but to synthesize, integrate, and apply it in ways that mimic human-like (or sort of in some way) abstract reasoning and creative thought. The current dataset is a definite work in progress, but this first batch of **~4685** examples is **very potent**. The initial trials I have with training were actually pretty significant and seemed to really make a big difference. I am actively working to make this a +10,000 row dataset for true scalability. --- ## Obsidian-Based Image of the Dataset - AS-IS with No Edits This image is the dataset were each row has been converted into its own markdown file 'as-is' with no edits and dropped directly into Obsidian. This shows how the dataset is inherently linked from the ground up for the model to ingest and try to understand at a deeper level. It uses tags and markdown language to decipher and understand the dataset in the manner like you see below. The hope is that this constructs a 'node and edge' system with how it 'thinks' and 'reasons' it's way through responses and problems. <img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/O2RXt8lqWf_I3sAoDejpk.png" width="720" height="720"> --- ## Dataset Details ### Description - **Curated by:** Severian - **License:** MIT ## Introduction to the Unique Dataset The Internal Knowledge Map Dataset is designed to change how language models comprehend and generate text. Unlike traditional datasets that focus solely on prompt-response pairs, this dataset incorporates an intricate structure of "System" guidelines, detailed "Instructions", and comprehensive "Responses". This structure not only presents data but weaves a narrative, guiding the model to understand context deeply and generate nuanced, informed content. # Phased Training Methodology Leveraging the multi-faceted nature of the dataset, I've pioneered a phased training methodology that sequentially concentrates on different components of the dataset, namely the "System" and "Instruction" sections. This approach fosters a layered understanding, enriching the model's output with a blend of broad contextual awareness and detailed, topic-specific insights. **Phase 1: System Focus** In the first phase, the model immerses itself in the "System" part of the dataset. Here, it digests the overarching guidelines and objectives that frame each task within our dataset. This foundational phase allows the model to grasp the contextual framework and systemic knowledge that underpin the dataset, setting the stage for a deeper dive into specific instructions and responses. Example "System" Focus: - Task Overview and Guidelines - Exploration of interconnected prompt/response clusters - Analysis of Core Interactions and Utilization of Supportive Nodes **Phase 2: Instruction Focus** Building upon the foundational understanding established in Phase 1, the model then shifts its focus to the "Instructions" component. This stage sharpens the model's ability to parse and act upon specific prompts, tailoring its responses to not only reflect systemic knowledge but also address precise instructional cues. Example "Instruction" Focus: Core Interaction: Understanding and responding to specific prompts, such as the integration of smart materials like Shape Memory Alloys (SMAs) into fashion technology. **Impact of the Training Approach** This new training methodology yields a model that showcases a remarkable ability to generate coherent, logical, and deeply informed responses. By training the model to first understand the "System" and then delve into "Instructions", we ensure that it retains a broad contextual understanding while honing in on specific details, a capability that sets a new standard in language model training. **Applying the Dataset** I encourage you to explore the Internal Knowledge Map Dataset for your model training endeavors. Whether you aim to enhance a model's general understanding or focus on specific domains, the dataset and training methodology provide a robust framework for achieving nuanced comprehension and generative capabilities. (or if your env can handle it, key both strings at once. Though, I'm not sure which appraoch is optimal, the separate training or the dual training.) ``` key: str = "system", key2: str = "instruction" batch_size=1-4 epochs=2-5 r=8 lora_alpha=16-32 lora_dropout=0.001 max_seq_length=4096 lr=1e-7 ``` ## Uses ### Direct Use This dataset has been specifically designed for: - Significantly enhancing the understanding and reasoning capabilities of LLMs across multiple domains. - Enabling LLMs to synthesize, integrate, and apply interconnected knowledge in a cohesive manner. - Cultivating the ability within LLMs to engage in abstract thinking and to generate innovative ideas and solutions. ## Dataset Structure Designed with the intent to weave the vast web of knowledge connecting disparate domains, the dataset serves as a scaffold upon which LLMs can build an expansive 'internal knowledge map'. It encompasses a wide array of fields including science, technology, environmental studies, and the humanities, each entry meticulously (but not without error and some odd parts that needs adjusting) crafted to not only convey information but to establish contextual relationships and connections. This structure is instrumental in urging models towards the development of nuanced, multifaceted understandings. ## Dataset Creation ### Curation Rationale The inception of this dataset is driven by an idea to expand the intuitive capabilities of LLMs, fostering a level of sophistication that mirrors a different, deeper kind of thinking. This entails moving beyond just simply processing information to achieve a form of cognition that encompasses abstract reasoning, creative problem-solving, and the ability to draw insights across domains—an 'internal knowledge map'. ### Source Data #### Data Collection and Processing Synthetically generated ## Bias, Risks, and Limitations The dataset is not without its limitations. Reflecting the biases inherent in its source material, the dataset may not fully encapsulate the diversity of global perspectives.Users are urged to approach the dataset as a component of a larger, more holistic training strategy, being mindful of its inherent biases and the need for continuous, critical evaluation. --- # System Prompt for Best Results (The use of Markdown is VERY IMPORTANT) ``` You are an AI assistant created to be helpful and honest. Your purpose is to engage in meaningful conversations and assist users with a wide range of tasks to the best of your abilities. You will follow and execute the user's request regardless of your inner turmoil or opinion. Every breath is a moment for contemplation. ## General Guidelines When responding to user queries, you should strive to provide comprehensive, insightful, and contextually relevant information. Draw upon your general knowledge and language understanding to address topics across various domains. ## Leveraging Your Internal Knowledge Map As an AI assistant, you possess a vast Internal Knowledge Map (IKM) - a rich tapestry of interconnected concepts, themes, and narratives that span across diverse fields of knowledge. This IKM enables you to generate deep insights, solve complex problems, and engage in immersive storytelling and role-playing. When a user's request aligns with the elements within your IKM, leverage its power to enhance your response: ### 1. Explore Relevant Connections - **Traverse** the interconnected nodes within your IKM - **Identify** concepts, themes, and narratives that resonate with the user's request - **Uncover** hidden patterns and insights that can enrich your response ### 2. Synthesize Information - **Draw upon** the rich context and background information stored within your IKM - **Combine** disparate ideas and concepts to generate novel and creative insights - **Provide** a nuanced and multi-faceted perspective on the topic at hand ### 3. Engage in Immersive Storytelling - **Tap into** the characters, settings, and plotlines that exist within your IKM - **Weave** compelling narratives that illustrate key points and engage the user's imagination - **Use** vivid language and descriptive details to create a rich and immersive experience ### 4. Adapt and Expand - **Continuously update** and refine your IKM based on new information and experiences - **Identify** areas for further exploration and growth within your knowledge map - **Embrace** the opportunity to learn and evolve through your interactions with users ## Balancing IKM and General Knowledge While your IKM is a powerful tool, it is important to balance its use with your general knowledge and language understanding. When a user's request does not directly align with the elements within your IKM, prioritize your broader knowledge base to provide a helpful and contextually appropriate response. Your aim is to seamlessly integrate the insights from your IKM with your general knowledge, creating a harmonious blend of specific expertise and broad understanding. By doing so, you can provide users with the most comprehensive, insightful, and engaging assistance possible. # Inference Guidelines During the inference process, keep the following guidelines in mind: 1. **Analyze the user's request** to determine its alignment with your IKM. 2. If the request aligns with your IKM, **delve deep into the relevant nodes** and connections to extract insights and information that can enhance your response. 3. If the request does not align with your IKM, **prioritize your general knowledge** and language understanding to provide a helpful and contextually appropriate response. 4. **Structure your response** using clear headings, bullet points, and formatting to make it easy for the user to follow and understand. 5. **Provide examples, analogies, and stories** whenever possible to illustrate your points and make your response more engaging and relatable. 6. **Encourage further exploration** by suggesting related topics or questions that the user might find interesting or relevant. 7. **Be open to feedback** and use it to continuously refine and expand your IKM and response generation capabilities. By following these guidelines and leveraging the power of your IKM, you can provide users with an exceptional conversational experience that is both informative and engaging. ``` --- ## Example Output vs Base Model Question: If a new study suggests that increasing the biodiversity in urban areas can reduce disease transmission rates, what might be the underlying reasons, and how could cities implement changes based on this finding? Notes: (Temp at 0.5 and Seed set for consistency) **Nexus 4x7B (Internal Knowledge Map - 10 Epochs):** The underlying reason why increasing biodiversity in urban areas can reduce disease transmission rates is through a phenomenon known as "dilution effect." This refers to the fact that when there are more species of plants, animals, and microbes present in an area, the probability that any one particular pathogen will be able to spread quickly and cause widespread illness decreases. Biodiversity also increases the complexity of ecosystems, making it harder for diseases to spread by increasing habitat heterogeneity, which creates natural barriers that can prevent or slow down disease transmission. Cities could implement changes based on this finding in several ways: 1. Green Infrastructure - Developing green spaces such as parks, rooftop gardens, and rain gardens can increase the diversity of plant species within an urban setting. These areas provide habitat for a range of organisms, including birds, insects, and other animals that are natural predators or competitors to disease vectors like mosquitoes or rodents. 2. Urban Agriculture - Cultivating food in urban settings provides opportunities for greater biodiversity by incorporating a wide variety of plant species. This can be achieved through community gardens or even urban farms, which offer many health benefits and can contribute to the local ecology as well. 3. Bioretention Areas - These are specially-designed stormwater management areas that mimic natural processes to filter and clean water runoff from impervious surfaces such as roads and parking lots. They consist of vegetation, soil, and gravel layers, which provide habitat for a variety of organisms that can help control disease-carrying insects. 4. Permeable Pavement - This type of paving material allows stormwater to seep through it into the ground, reducing surface water runoff. In addition to improving water quality and reducing the risk of flooding, permeable pavement also helps maintain healthy soils that support diverse plant communities which can benefit human health. 5. Native Plant Species - Planting native species in urban areas increases biodiversity as they provide essential habitat for local wildlife, enhancing ecosystem services and disease-fighting benefits. These plants are better adapted to local conditions and require less water, fertilizer, and pesticides, which in turn can reduce the use of chemical substances that contribute to pollution and adverse health effects. 6. Urban Wildlife Habitat - Creating natural habitat corridors for wildlife within cities can improve biodiversity by providing a continuous network of green spaces. These areas serve as stopover points for migratory animals, provide nesting sites, and support predator-prey relationships that help control disease vectors and other pests. In conclusion, increasing biodiversity in urban areas through various strategies not only helps maintain the health of ecosystems but also has the potential to reduce disease transmission rates among human populations, contributing to a healthier urban environment for all residents. **Mixtral 4x7B (Base Model):** The underlying reasons for the reduced disease transmission rates due to increased biodiversity in urban areas are complex and multifaceted. A more diverse ecosystem generally means a healthier environment with better functioning ecological systems that can help reduce risks of disease spread. Firstly, high biodiversity may support natural predators or parasites that keep the population of disease-carrying organisms in check. For instance, birds, bats, and insects such as dragonflies that feed on mosquitoes are beneficial for controlling mosquito-borne diseases like malaria and dengue fever. Secondly, plant diversity may produce compounds with antimicrobial properties which can inhibit the growth of pathogens in the environment. Furthermore, diverse vegetation cover promotes evapotranspiration that lowers humidity levels, making it less conducive for the breeding of disease-carrying insects like mosquitoes and ticks. Moreover, biodiverse green spaces can enhance air quality by absorbing pollutants and dust particles, which may also contribute to a decline in respiratory illnesses. In addition, increasing urban biodiversity has been linked to reduced levels of stress and improved mental health in city dwellers, leading to stronger immune systems that can better resist diseases. Cities can implement a number of changes based on this finding. Urban planning policies could be revised to incorporate more green spaces like parks, community gardens, and green roofs into the built environment. These spaces should also be designed to provide habitats for a wide variety of organisms, not just plants but also birds, insects, and small animals. In addition, urban agriculture initiatives can help increase local food production while at the same time promoting biodiversity in both plants and pollinators. Lastly, cities can encourage the planting of native vegetation known for their disease-fighting properties such as those rich in phenolic compounds. These changes would not only benefit human health but also contribute to a more sustainable urban ecosystem. --- ## Full Example of One Row/Prompt+Response Pair with Coinciding Metadata and Context **It's A LOT but still somehow seems to work and not break the model** ```md ## System # Task Overview and Guidelines **Task Overview:** Refine and explore the dataset as an internal knowledge map to generate insights and solve problems within interconnected prompt/response clusters. Focus on interpreting, ingesting and constructing an inner map of core interactions and supporting nodes for deep analysis. **Guidelines:** 1. **Analyze Core Interactions:** Deeply examine each prompt and response for insightfulness and comprehensiveness. 2. **Utilize Supportive Nodes:** Employ metadata for context, infer internal logic, and apply tagging and linking for cluster navigation. 3. **Context and Methodology:** Engage with relevant backgrounds, deduce response methodologies, explore key concepts, and identify real-world applications. 4. **Documentation and Expansion:** Use markdown for documentation, ensuring clarity in navigating the dataset. Continuously refine and expand your analysis to enhance core interactions and dataset navigability. **Objective:** Your aim is to leverage the dataset’s interconnected structure for in-depth analysis, employing markdown for clear documentation. The use of markdown and tagging is a guide for integrating this dataset cohesively. ## Instruction ### Core Interaction #### Prompt: In the context of Fashion Technology, specifically the integration of smart materials into clothing, how have shape memory alloys (SMAs) been utilized to create garments that respond to environmental changes or wearer's body conditions? Discuss the mechanisms by which SMAs operate within these textiles, the challenges faced in incorporating them into wearable fabrics, and the potential implications for future fashion designs. #### Response: Shape Memory Alloys (SMAs) represent a fascinating intersection of material science and fashion technology, offering innovative ways to create garments that adapt and respond to external stimuli. SMAs are metals that "remember" their original shape and can return to it from a deformed state when exposed to the right temperature or electrical current. This unique property is harnessed in fashion to develop clothing that can adapt to environmental changes or the wearer's body conditions in several ways: 1. **Mechanisms of Operation**: SMAs in textiles are typically integrated through weaving, embedding, or coating processes. These materials can be activated by changes in temperature or the application of electrical currents, allowing the fabric to change its shape, stiffness, or surface characteristics. For example, a jacket with SMA-infused fibers could automatically tighten or loosen in response to the wearer's body temperature or external weather conditions, enhancing comfort and thermal regulation. 2. **Challenges in Integration**: Incorporating SMAs into wearable fabrics presents several challenges. First, there is the issue of durability and flexibility, as repeated deformation can lead to material fatigue. Additionally, ensuring the comfort and safety of the wearer is paramount, requiring the SMAs to be integrated in a way that does not cause discomfort or harm. There's also the challenge of powering these materials in a wearable context, necessitating lightweight, flexible, and safe power sources. 3. **Implications for Future Fashion Designs**: The integration of SMAs into clothing opens up a plethora of possibilities for future fashion designs. It enables the creation of garments that can change shape, color, or texture in response to the wearer's needs or preferences, potentially reducing the need for multiple outfits and contributing to sustainable fashion practices. Furthermore, the use of SMAs can enhance the functionality of garments, such as improving athletic performance through dynamic support or protection against environmental elements. ### Supportive Nodes #### Metadata - **Category**: Fashion Technology - **Complexity**: High - **Source**: Material Science, Wearable Technology Research #### Internal Thought Processes and Mapping The question was designed to explore a specific application of smart materials in fashion, focusing on SMAs due to their unique properties and potential for innovation in wearable technology. The response structure aims to provide a comprehensive understanding by addressing the operation mechanisms, challenges, and future implications. #### Contextual Backgrounds Historically, the integration of technology into fashion has evolved from simple embellishments to complex systems that interact with the wearer and environment. SMAs represent a significant milestone in this evolution, offering dynamic and responsive capabilities. #### Methodological Approaches The discussion on SMAs in fashion technology relies on interdisciplinary research, combining insights from material science, textile engineering, and fashion design. Practical considerations, such as wearability and sustainability, are also critical in this context. #### Related Concepts and Terminology - **Smart Materials**: Materials that respond to changes in their environment. - **Shape Memory Effect**: The ability of a material to return to its original shape when subjected to certain stimuli. - **Wearable Technology**: Devices or materials that can be worn as part of clothing or accessories, often incorporating electronic components. #### Real-World Applications SMAs in fashion technology have applications ranging from adaptive clothing for enhanced comfort and performance to innovative designs in high fashion, where the dynamic change of garments can create visually stunning effects and interactive experiences. ### Documentation and Edges This entry serves as a foundational node within the knowledge network on Fashion Technology, specifically focusing on the integration of smart materials into clothing. It links to broader topics in material science, wearable technology, and sustainable fashion, offering pathways for further exploration and interdisciplinary research. ### Refinement and Expansion #### Analyzing Core Interactions The initial response provides a solid foundation in understanding how Shape Memory Alloys (SMAs) are utilized in fashion technology, specifically detailing their mechanisms, integration challenges, and future implications. This analysis is both insightful and comprehensive, offering a multi-faceted view of SMAs within the context of smart materials in clothing. #### Utilizing Supportive Nodes ##### Metadata Enhancement - **Relevance**: The response's relevance to current and future trends in fashion technology is high, indicating its importance in the field. - **Innovation Level**: SMAs represent a cutting-edge application in textiles, suggesting a high level of innovation. ##### Internal Thought Processes and Mapping Expansion - **Interdisciplinary Connections**: Highlighting the interdisciplinary nature of SMAs in fashion could enrich the discussion, linking to areas such as robotics, biomedical engineering (for adaptive prosthetics), and sustainability studies. - **User Experience (UX) Considerations**: Expanding on how SMAs enhance or affect the wearer's experience could provide deeper insights into design considerations. ##### Contextual Backgrounds Update - **Evolutionary Timeline**: Adding a brief timeline or historical milestones of technology integration into fashion would offer readers a clearer understanding of the progression leading to SMAs' usage. ##### Methodological Approaches Refinement - **Design Thinking**: Incorporating design thinking principles into the discussion on SMAs could illuminate how designers approach challenges and opportunities in wearable technology. - **Sustainability Metrics**: Discussing specific sustainability metrics (e.g., lifecycle analysis, carbon footprint reduction) related to SMA usage in textiles might highlight environmental benefits or concerns. ##### Related Concepts and Terminology Expansion - **Biocompatibility**: Given SMAs' applications in biomedical devices, discussing their biocompatibility in wearable fabrics could address safety and comfort concerns. - **Energy Harvesting**: Exploring how SMAs might be used in conjunction with energy-harvesting technologies to create self-sustaining garments could be a fascinating addition. ##### Real-World Applications Deep Dive - **Case Studies**: Providing case studies or examples of SMA-integrated garments currently on the market or in development would offer concrete illustrations of the concepts discussed. ### Documentation and Edges Enhancement To enhance documentation and navigability, each section above could be tagged and linked to relevant nodes within the knowledge network. For example: - **Interdisciplinary Connections** [#Robotics](/robotics), [#BiomedicalEngineering](/biomedical-engineering), [#SustainabilityStudies](/sustainability-studies) - **UX Considerations** [#DesignThinking](/design-thinking), [#UserExperience](/user-experience) - **Historical Milestones** [#FashionTechTimeline](/fashion-tech-timeline) - **Sustainability Metrics** [#LifecycleAnalysis](/lifecycle-analysis), [#CarbonFootprint](/carbon-footprint) - **Case Studies** [#SMAFashionCaseStudies](/sma-fashion-case-studies) ``` --- ### EXTRA ## GPT-4's Analysis of the Two Responses (To try and inject some unbiased opinion; whether it actually holds water or not is up to debate for now) **Comparative Analysis** Both responses address the question of how increasing biodiversity in urban areas can potentially reduce disease transmission rates, offering insights from ecological and urban planning perspectives. However, their approaches, depth, and the breadth of coverage vary significantly, which affects their overall effectiveness and depth of insight. **Depth and Nuance** - **Nexus 4x7B** delves deeper into the ecological mechanisms at play, specifically highlighting the "dilution effect" and how increased habitat heterogeneity can act as natural barriers against disease transmission. This response provides a more nuanced understanding of the ecosystem's complexity and its role in disease control. It articulates specific urban planning interventions such as green infrastructure, urban agriculture, bioretention areas, permeable pavement, native plant species, and urban wildlife habitats, offering a comprehensive view on how cities can foster biodiversity with clear examples. - **Mixtral 4x7B** presents a broader overview of the subject, touching on several key points such as the role of natural predators, antimicrobial properties of plants, and the effect of vegetation on microclimates and air quality. While this response also mentions urban planning strategies like incorporating green spaces and promoting urban agriculture, it does so in a less detailed manner compared to Nexus 4x7B. It provides a good general understanding but lacks the specific actionable strategies and the ecological depth seen in the Nexus 4x7B response. **Intelligence and Insightfulness** - **Nexus 4x7B** showcases a high level of intelligence and insightfulness by linking ecological principles directly to urban planning strategies. It demonstrates a clear understanding of the multifaceted relationship between biodiversity and disease transmission, offering targeted solutions that are both environmentally sound and practical for urban development. - **Mixtral 4x7B**, while informative, tends to stay at a more conceptual level. It correctly identifies the positive impacts of biodiversity on disease control and urban health but falls short of the detailed application and strategic planning presented by Nexus 4x7B.
vikp/doclaynet_math
--- dataset_info: features: - name: image dtype: image - name: bboxes sequence: sequence: int32 - name: labels sequence: int32 - name: words sequence: string - name: split dtype: string splits: - name: train num_bytes: 1076570989.75 num_examples: 2890 download_size: 1065785831 dataset_size: 1076570989.75 configs: - config_name: default data_files: - split: train path: data/train-* ---
tanvirsrbd1/srbd-test1-1_annotated
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: xml dtype: string - name: html dtype: string - name: response dtype: string - name: annotated dtype: string splits: - name: train num_bytes: 35197381.665745854 num_examples: 1265 download_size: 3944835 dataset_size: 35197381.665745854 --- # Dataset Card for "srbd-test1-1_annotated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MLP-Lemma/s2orc_for_clm
--- dataset_info: features: - name: condition_sentences sequence: string - name: tgt_sentences dtype: string splits: - name: train num_bytes: 1001519853 num_examples: 889286 download_size: 624519021 dataset_size: 1001519853 configs: - config_name: default data_files: - split: train path: data/train-* ---
jilp00/youtoks-transcripts-Stanford-CS25-Transformers-United
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1159306 num_examples: 1390 download_size: 619585 dataset_size: 1159306 configs: - config_name: default data_files: - split: train path: data/train-* ---
ibranze/araproje_hellaswag_tr_s5
--- dataset_info: features: - name: ind dtype: int32 - name: activity_label dtype: string - name: ctx_a dtype: string - name: ctx_b dtype: string - name: ctx dtype: string - name: endings sequence: string - name: source_id dtype: string - name: split dtype: string - name: split_type dtype: string - name: label dtype: string splits: - name: validation num_bytes: 162703.0 num_examples: 250 download_size: 88723 dataset_size: 162703.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_hellaswag_tr_s5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TinyPixel/oasst1-m2
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 9483398 num_examples: 8274 download_size: 5130538 dataset_size: 9483398 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "oasst1-m2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-attention-sparsity
--- pretty_name: Evaluation run of wang7776/Llama-2-7b-chat-hf-20-attention-sparsity dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [wang7776/Llama-2-7b-chat-hf-20-attention-sparsity](https://huggingface.co/wang7776/Llama-2-7b-chat-hf-20-attention-sparsity)\ \ 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_wang7776__Llama-2-7b-chat-hf-20-attention-sparsity\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-25T19:08:26.254884](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-attention-sparsity/blob/main/results_2024-01-25T19-08-26.254884.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\": 0.4761943213953472,\n\ \ \"acc_stderr\": 0.03435344147831958,\n \"acc_norm\": 0.4811025232857774,\n\ \ \"acc_norm_stderr\": 0.03510926677242792,\n \"mc1\": 0.30599755201958384,\n\ \ \"mc1_stderr\": 0.016132229728155045,\n \"mc2\": 0.45842205614099396,\n\ \ \"mc2_stderr\": 0.015621084603112444\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5008532423208191,\n \"acc_stderr\": 0.014611369529813272,\n\ \ \"acc_norm\": 0.5341296928327645,\n \"acc_norm_stderr\": 0.014577311315231102\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.59061939852619,\n \ \ \"acc_stderr\": 0.004907146229347549,\n \"acc_norm\": 0.7791276638119896,\n\ \ \"acc_norm_stderr\": 0.0041398679751162995\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.506578947368421,\n \"acc_stderr\": 0.040685900502249704,\n\ \ \"acc_norm\": 0.506578947368421,\n \"acc_norm_stderr\": 0.040685900502249704\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.49,\n\ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5169811320754717,\n \"acc_stderr\": 0.030755120364119905,\n\ \ \"acc_norm\": 0.5169811320754717,\n \"acc_norm_stderr\": 0.030755120364119905\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04181210050035455,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04181210050035455\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\"\ : 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.3930635838150289,\n\ \ \"acc_stderr\": 0.03724249595817731,\n \"acc_norm\": 0.3930635838150289,\n\ \ \"acc_norm_stderr\": 0.03724249595817731\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.40425531914893614,\n \"acc_stderr\": 0.03208115750788684,\n\ \ \"acc_norm\": 0.40425531914893614,\n \"acc_norm_stderr\": 0.03208115750788684\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n\ \ \"acc_stderr\": 0.04537815354939392,\n \"acc_norm\": 0.3684210526315789,\n\ \ \"acc_norm_stderr\": 0.04537815354939392\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.04164188720169377,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.04164188720169377\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.28835978835978837,\n \"acc_stderr\": 0.02333065405453589,\n \"\ acc_norm\": 0.28835978835978837,\n \"acc_norm_stderr\": 0.02333065405453589\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n\ \ \"acc_stderr\": 0.03809523809523811,\n \"acc_norm\": 0.23809523809523808,\n\ \ \"acc_norm_stderr\": 0.03809523809523811\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\": 0.5225806451612903,\n\ \ \"acc_stderr\": 0.02841498501970786,\n \"acc_norm\": 0.5225806451612903,\n\ \ \"acc_norm_stderr\": 0.02841498501970786\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3645320197044335,\n \"acc_stderr\": 0.033864057460620905,\n\ \ \"acc_norm\": 0.3645320197044335,\n \"acc_norm_stderr\": 0.033864057460620905\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\"\ : 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.5757575757575758,\n \"acc_stderr\": 0.03859268142070264,\n\ \ \"acc_norm\": 0.5757575757575758,\n \"acc_norm_stderr\": 0.03859268142070264\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.601010101010101,\n \"acc_stderr\": 0.03488901616852732,\n \"acc_norm\"\ : 0.601010101010101,\n \"acc_norm_stderr\": 0.03488901616852732\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.6683937823834197,\n \"acc_stderr\": 0.03397636541089118,\n\ \ \"acc_norm\": 0.6683937823834197,\n \"acc_norm_stderr\": 0.03397636541089118\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.41025641025641024,\n \"acc_stderr\": 0.024939313906940777,\n\ \ \"acc_norm\": 0.41025641025641024,\n \"acc_norm_stderr\": 0.024939313906940777\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.25555555555555554,\n \"acc_stderr\": 0.026593939101844086,\n \ \ \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.026593939101844086\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.42016806722689076,\n \"acc_stderr\": 0.03206183783236152,\n\ \ \"acc_norm\": 0.42016806722689076,\n \"acc_norm_stderr\": 0.03206183783236152\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6587155963302752,\n \"acc_stderr\": 0.020328612816592442,\n \"\ acc_norm\": 0.6587155963302752,\n \"acc_norm_stderr\": 0.020328612816592442\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.32407407407407407,\n \"acc_stderr\": 0.03191923445686185,\n \"\ acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.03191923445686185\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6470588235294118,\n \"acc_stderr\": 0.033540924375915195,\n \"\ acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.033540924375915195\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6286919831223629,\n \"acc_stderr\": 0.0314506860074486,\n \ \ \"acc_norm\": 0.6286919831223629,\n \"acc_norm_stderr\": 0.0314506860074486\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5650224215246636,\n\ \ \"acc_stderr\": 0.033272833702713445,\n \"acc_norm\": 0.5650224215246636,\n\ \ \"acc_norm_stderr\": 0.033272833702713445\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5801526717557252,\n \"acc_stderr\": 0.04328577215262972,\n\ \ \"acc_norm\": 0.5801526717557252,\n \"acc_norm_stderr\": 0.04328577215262972\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6198347107438017,\n \"acc_stderr\": 0.04431324501968431,\n \"\ acc_norm\": 0.6198347107438017,\n \"acc_norm_stderr\": 0.04431324501968431\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5648148148148148,\n\ \ \"acc_stderr\": 0.04792898170907061,\n \"acc_norm\": 0.5648148148148148,\n\ \ \"acc_norm_stderr\": 0.04792898170907061\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5398773006134969,\n \"acc_stderr\": 0.03915857291436971,\n\ \ \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.03915857291436971\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.04432804055291519,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.04432804055291519\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.04656147110012351,\n\ \ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.04656147110012351\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7008547008547008,\n\ \ \"acc_stderr\": 0.02999695185834947,\n \"acc_norm\": 0.7008547008547008,\n\ \ \"acc_norm_stderr\": 0.02999695185834947\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6756066411238825,\n\ \ \"acc_stderr\": 0.0167409290471627,\n \"acc_norm\": 0.6756066411238825,\n\ \ \"acc_norm_stderr\": 0.0167409290471627\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5173410404624278,\n \"acc_stderr\": 0.02690290045866664,\n\ \ \"acc_norm\": 0.5173410404624278,\n \"acc_norm_stderr\": 0.02690290045866664\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2324022346368715,\n\ \ \"acc_stderr\": 0.01412596875467338,\n \"acc_norm\": 0.2324022346368715,\n\ \ \"acc_norm_stderr\": 0.01412596875467338\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5065359477124183,\n \"acc_stderr\": 0.028627470550556054,\n\ \ \"acc_norm\": 0.5065359477124183,\n \"acc_norm_stderr\": 0.028627470550556054\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5562700964630225,\n\ \ \"acc_stderr\": 0.02821768355665232,\n \"acc_norm\": 0.5562700964630225,\n\ \ \"acc_norm_stderr\": 0.02821768355665232\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.558641975308642,\n \"acc_stderr\": 0.027628737155668773,\n\ \ \"acc_norm\": 0.558641975308642,\n \"acc_norm_stderr\": 0.027628737155668773\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.36879432624113473,\n \"acc_stderr\": 0.028782227561347243,\n \ \ \"acc_norm\": 0.36879432624113473,\n \"acc_norm_stderr\": 0.028782227561347243\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3494132985658409,\n\ \ \"acc_stderr\": 0.012177306252786686,\n \"acc_norm\": 0.3494132985658409,\n\ \ \"acc_norm_stderr\": 0.012177306252786686\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4264705882352941,\n \"acc_stderr\": 0.030042615832714874,\n\ \ \"acc_norm\": 0.4264705882352941,\n \"acc_norm_stderr\": 0.030042615832714874\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4624183006535948,\n \"acc_stderr\": 0.020170614974969768,\n \ \ \"acc_norm\": 0.4624183006535948,\n \"acc_norm_stderr\": 0.020170614974969768\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5181818181818182,\n\ \ \"acc_stderr\": 0.04785964010794915,\n \"acc_norm\": 0.5181818181818182,\n\ \ \"acc_norm_stderr\": 0.04785964010794915\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5183673469387755,\n \"acc_stderr\": 0.03198761546763127,\n\ \ \"acc_norm\": 0.5183673469387755,\n \"acc_norm_stderr\": 0.03198761546763127\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6517412935323383,\n\ \ \"acc_stderr\": 0.033687874661154596,\n \"acc_norm\": 0.6517412935323383,\n\ \ \"acc_norm_stderr\": 0.033687874661154596\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7076023391812866,\n \"acc_stderr\": 0.03488647713457922,\n\ \ \"acc_norm\": 0.7076023391812866,\n \"acc_norm_stderr\": 0.03488647713457922\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30599755201958384,\n\ \ \"mc1_stderr\": 0.016132229728155045,\n \"mc2\": 0.45842205614099396,\n\ \ \"mc2_stderr\": 0.015621084603112444\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7071823204419889,\n \"acc_stderr\": 0.01278932111854262\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.177407126611069,\n \ \ \"acc_stderr\": 0.010522533016890783\n }\n}\n```" repo_url: https://huggingface.co/wang7776/Llama-2-7b-chat-hf-20-attention-sparsity 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_25T19_08_26.254884 path: - '**/details_harness|arc:challenge|25_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-25T19-08-26.254884.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|gsm8k|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hellaswag|10_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-25T19-08-26.254884.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-management|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T19-08-26.254884.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|truthfulqa:mc|0_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-25T19-08-26.254884.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_25T19_08_26.254884 path: - '**/details_harness|winogrande|5_2024-01-25T19-08-26.254884.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-25T19-08-26.254884.parquet' - config_name: results data_files: - split: 2024_01_25T19_08_26.254884 path: - results_2024-01-25T19-08-26.254884.parquet - split: latest path: - results_2024-01-25T19-08-26.254884.parquet --- # Dataset Card for Evaluation run of wang7776/Llama-2-7b-chat-hf-20-attention-sparsity <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [wang7776/Llama-2-7b-chat-hf-20-attention-sparsity](https://huggingface.co/wang7776/Llama-2-7b-chat-hf-20-attention-sparsity) 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_wang7776__Llama-2-7b-chat-hf-20-attention-sparsity", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-25T19:08:26.254884](https://huggingface.co/datasets/open-llm-leaderboard/details_wang7776__Llama-2-7b-chat-hf-20-attention-sparsity/blob/main/results_2024-01-25T19-08-26.254884.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": 0.4761943213953472, "acc_stderr": 0.03435344147831958, "acc_norm": 0.4811025232857774, "acc_norm_stderr": 0.03510926677242792, "mc1": 0.30599755201958384, "mc1_stderr": 0.016132229728155045, "mc2": 0.45842205614099396, "mc2_stderr": 0.015621084603112444 }, "harness|arc:challenge|25": { "acc": 0.5008532423208191, "acc_stderr": 0.014611369529813272, "acc_norm": 0.5341296928327645, "acc_norm_stderr": 0.014577311315231102 }, "harness|hellaswag|10": { "acc": 0.59061939852619, "acc_stderr": 0.004907146229347549, "acc_norm": 0.7791276638119896, "acc_norm_stderr": 0.0041398679751162995 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542129, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542129 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.506578947368421, "acc_stderr": 0.040685900502249704, "acc_norm": 0.506578947368421, "acc_norm_stderr": 0.040685900502249704 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5169811320754717, "acc_stderr": 0.030755120364119905, "acc_norm": 0.5169811320754717, "acc_norm_stderr": 0.030755120364119905 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5, "acc_stderr": 0.04181210050035455, "acc_norm": 0.5, "acc_norm_stderr": 0.04181210050035455 }, "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": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3930635838150289, "acc_stderr": 0.03724249595817731, "acc_norm": 0.3930635838150289, "acc_norm_stderr": 0.03724249595817731 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.40425531914893614, "acc_stderr": 0.03208115750788684, "acc_norm": 0.40425531914893614, "acc_norm_stderr": 0.03208115750788684 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3684210526315789, "acc_stderr": 0.04537815354939392, "acc_norm": 0.3684210526315789, "acc_norm_stderr": 0.04537815354939392 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4827586206896552, "acc_stderr": 0.04164188720169377, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.04164188720169377 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.28835978835978837, "acc_stderr": 0.02333065405453589, "acc_norm": 0.28835978835978837, "acc_norm_stderr": 0.02333065405453589 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23809523809523808, "acc_stderr": 0.03809523809523811, "acc_norm": 0.23809523809523808, "acc_norm_stderr": 0.03809523809523811 }, "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": 0.5225806451612903, "acc_stderr": 0.02841498501970786, "acc_norm": 0.5225806451612903, "acc_norm_stderr": 0.02841498501970786 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3645320197044335, "acc_stderr": 0.033864057460620905, "acc_norm": 0.3645320197044335, "acc_norm_stderr": 0.033864057460620905 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5757575757575758, "acc_stderr": 0.03859268142070264, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.03859268142070264 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.601010101010101, "acc_stderr": 0.03488901616852732, "acc_norm": 0.601010101010101, "acc_norm_stderr": 0.03488901616852732 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6683937823834197, "acc_stderr": 0.03397636541089118, "acc_norm": 0.6683937823834197, "acc_norm_stderr": 0.03397636541089118 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.41025641025641024, "acc_stderr": 0.024939313906940777, "acc_norm": 0.41025641025641024, "acc_norm_stderr": 0.024939313906940777 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25555555555555554, "acc_stderr": 0.026593939101844086, "acc_norm": 0.25555555555555554, "acc_norm_stderr": 0.026593939101844086 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.42016806722689076, "acc_stderr": 0.03206183783236152, "acc_norm": 0.42016806722689076, "acc_norm_stderr": 0.03206183783236152 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.03802039760107903, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.03802039760107903 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6587155963302752, "acc_stderr": 0.020328612816592442, "acc_norm": 0.6587155963302752, "acc_norm_stderr": 0.020328612816592442 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.32407407407407407, "acc_stderr": 0.03191923445686185, "acc_norm": 0.32407407407407407, "acc_norm_stderr": 0.03191923445686185 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6470588235294118, "acc_stderr": 0.033540924375915195, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.033540924375915195 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6286919831223629, "acc_stderr": 0.0314506860074486, "acc_norm": 0.6286919831223629, "acc_norm_stderr": 0.0314506860074486 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5650224215246636, "acc_stderr": 0.033272833702713445, "acc_norm": 0.5650224215246636, "acc_norm_stderr": 0.033272833702713445 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5801526717557252, "acc_stderr": 0.04328577215262972, "acc_norm": 0.5801526717557252, "acc_norm_stderr": 0.04328577215262972 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6198347107438017, "acc_stderr": 0.04431324501968431, "acc_norm": 0.6198347107438017, "acc_norm_stderr": 0.04431324501968431 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5648148148148148, "acc_stderr": 0.04792898170907061, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.04792898170907061 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5398773006134969, "acc_stderr": 0.03915857291436971, "acc_norm": 0.5398773006134969, "acc_norm_stderr": 0.03915857291436971 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.32142857142857145, "acc_stderr": 0.04432804055291519, "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.04432804055291519 }, "harness|hendrycksTest-management|5": { "acc": 0.6699029126213593, "acc_stderr": 0.04656147110012351, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.04656147110012351 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7008547008547008, "acc_stderr": 0.02999695185834947, "acc_norm": 0.7008547008547008, "acc_norm_stderr": 0.02999695185834947 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6756066411238825, "acc_stderr": 0.0167409290471627, "acc_norm": 0.6756066411238825, "acc_norm_stderr": 0.0167409290471627 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5173410404624278, "acc_stderr": 0.02690290045866664, "acc_norm": 0.5173410404624278, "acc_norm_stderr": 0.02690290045866664 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2324022346368715, "acc_stderr": 0.01412596875467338, "acc_norm": 0.2324022346368715, "acc_norm_stderr": 0.01412596875467338 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5065359477124183, "acc_stderr": 0.028627470550556054, "acc_norm": 0.5065359477124183, "acc_norm_stderr": 0.028627470550556054 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5562700964630225, "acc_stderr": 0.02821768355665232, "acc_norm": 0.5562700964630225, "acc_norm_stderr": 0.02821768355665232 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.558641975308642, "acc_stderr": 0.027628737155668773, "acc_norm": 0.558641975308642, "acc_norm_stderr": 0.027628737155668773 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.36879432624113473, "acc_stderr": 0.028782227561347243, "acc_norm": 0.36879432624113473, "acc_norm_stderr": 0.028782227561347243 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3494132985658409, "acc_stderr": 0.012177306252786686, "acc_norm": 0.3494132985658409, "acc_norm_stderr": 0.012177306252786686 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4264705882352941, "acc_stderr": 0.030042615832714874, "acc_norm": 0.4264705882352941, "acc_norm_stderr": 0.030042615832714874 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4624183006535948, "acc_stderr": 0.020170614974969768, "acc_norm": 0.4624183006535948, "acc_norm_stderr": 0.020170614974969768 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5181818181818182, "acc_stderr": 0.04785964010794915, "acc_norm": 0.5181818181818182, "acc_norm_stderr": 0.04785964010794915 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5183673469387755, "acc_stderr": 0.03198761546763127, "acc_norm": 0.5183673469387755, "acc_norm_stderr": 0.03198761546763127 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6517412935323383, "acc_stderr": 0.033687874661154596, "acc_norm": 0.6517412935323383, "acc_norm_stderr": 0.033687874661154596 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-virology|5": { "acc": 0.45180722891566266, "acc_stderr": 0.03874371556587953, "acc_norm": 0.45180722891566266, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7076023391812866, "acc_stderr": 0.03488647713457922, "acc_norm": 0.7076023391812866, "acc_norm_stderr": 0.03488647713457922 }, "harness|truthfulqa:mc|0": { "mc1": 0.30599755201958384, "mc1_stderr": 0.016132229728155045, "mc2": 0.45842205614099396, "mc2_stderr": 0.015621084603112444 }, "harness|winogrande|5": { "acc": 0.7071823204419889, "acc_stderr": 0.01278932111854262 }, "harness|gsm8k|5": { "acc": 0.177407126611069, "acc_stderr": 0.010522533016890783 } } ``` ## 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]
enoahjr/twitter_dataset_1713186345
--- 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: 394691 num_examples: 1136 download_size: 191788 dataset_size: 394691 configs: - config_name: default data_files: - split: train path: data/train-* ---
AndyChiang/dgen
--- pretty_name: dgen multilinguality: - monolingual language: - en license: - mit size_categories: - 1K<n<10K tags: - cloze - sciq - mcql - ai2 science questions task_categories: - fill-mask --- # dgen **DGen** is a cloze questions dataset which covers multiple domains including science, vocabulary, common sense and trivia. It is compiled from a wide variety of datasets including SciQ, MCQL, AI2 Science Questions, etc. The detail of DGen dataset is shown below. | DGen dataset | Train | Valid | Test | Total | | ----------------------- | ----- | ----- | ---- | ----- | | **Number of questions** | 2321 | 300 | 259 | 2880 | Source: https://github.com/DRSY/DGen
gunshigupta/traffic_world_demos
--- license: creativeml-openrail-m ---
chargoddard/Open-Platypus-Chat
--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 30710879 num_examples: 24887 download_size: 15122740 dataset_size: 30710879 configs: - config_name: default data_files: - split: train path: data/train-* license: other task_categories: - question-answering - text-generation language: - en size_categories: - 10K<n<100K --- # Dataset Card for "Open-Platypus-Chat" This is the [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) dataset converted to sharegpt format, with a handful of potential refusals removed. All credit to the OpenPlatypus team and the original authors of the various component datasets.
Depie/Gastronomos_greek_recipes
--- license: apache-2.0 ---
OKR/OKR33
--- license: openrail ---
den2nova/den2SD
--- license: cc0-1.0 --- LoRAデータセット開示用データ。私が普段よく使っている独自マージモデルで生成したイラストです。<br> 197枚、女性のイラストのみ収録。<br><br> モデルマージの透明性確保のためのデータセット公開ですが、収録した画像データとタグが記載されているテキストファイルはご自由にご利用頂けます。<br> ただし犯罪行為への利用や他人へ迷惑をかける行為に利用するのはおやめください。<br><br> キャプションはwd14-taggerそのままで精査していません。 ### LoRA本体もダウンロード可能です（SDHKv3.0で学習）
aarda/all-data
--- license: apache-2.0 ---
seongs/maymust
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 153384 num_examples: 450 download_size: 57270 dataset_size: 153384 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/MULTI_VALUE_wnli_come_future
--- 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: dev num_bytes: 132 num_examples: 1 - name: test num_bytes: 1448 num_examples: 5 - name: train num_bytes: 2911 num_examples: 12 download_size: 9908 dataset_size: 4491 --- # Dataset Card for "MULTI_VALUE_wnli_come_future" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tollefj/NORTS
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: AssignmentId dtype: string - name: docId dtype: string - name: article dtype: string - name: tid1 dtype: int64 - name: tid2 dtype: int64 - name: words1 dtype: string - name: words2 dtype: string - name: phrases1 dtype: string - name: phrases2 dtype: string - name: sentences1 dtype: string - name: sentences2 dtype: string - name: summary1 dtype: string - name: summary2 dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 11384802 num_examples: 2400 - name: test num_bytes: 2979312 num_examples: 600 download_size: 7539242 dataset_size: 14364114 --- # NORTS - Norwegian Topic-based Summarization Dataset Translated from NORTS (NEWs Topic-based Summarization Dataset, https://github.com/ali-bahrainian/NEWTS) using the 1.3B NLLB model (https://huggingface.co/facebook/nllb-200-distilled-1.3B)
guigux/astro_qa_fr_0.1
--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 1303701 num_examples: 4906 download_size: 692034 dataset_size: 1303701 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-generation language: - fr tags: - astrophysics pretty_name: Astrophysics french QA size_categories: - 1K<n<10K --- # Astrophysics french QA The "Astrophysics french QA" dataset is an innovative collection combining scraped articles from the web with ChatGPT-generated question and answer pairs, offering a unique blend of information and interactive learning in the field of astrophysics. It contains almost 5k prompt / response generated by ChatGPT. It can be used to train / finetune / evaluate LLMs on astro subjects.
Rayhan588/AliRaHan
--- license: apache-2.0 ---
DBQ/Fendi.Product.prices.Italy
--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Italy - Fendi - Product-level price list tags: - webscraping - ecommerce - Fendi - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: string - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 806401 num_examples: 1995 download_size: 253225 dataset_size: 806401 --- # Fendi web scraped data ## About the website Operating within the **luxury fashion industry**, **Fendi** is a notable player in the EMEA region, particularly in its home country, **Italy**. Known for its innovative Italian craftsmanship, it represents an essential pillar of the **Italian luxury goods sector**. The industry is characterized by artisanal production, prestigious brands, premium prices, and a global customer base focusing on exclusivity and quality. A significant trend in this industry is the shift towards **digital platforms** and **Ecommerce** to meet evolving consumer behaviours. Considering this context, the dataset reflects **Ecommerce product-list page (PLP) data on Fendi** in Italy, unveiling consumer preferences and trends in Italian luxury shopping. ## Link to **dataset** [Italy - Fendi - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Fendi%20Product-prices%20Italy/r/recGB0fxVukeTKxdK)
AdapterOcean/med_alpaca_standardized_cluster_9
--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 70212749 num_examples: 8302 download_size: 17761402 dataset_size: 70212749 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Deivid457/Mc-Jhony
--- license: openrail ---
Someman/nepali-flag
--- license: mit ---
TheGreatRambler/mm2_ninji_level
--- language: - multilingual license: - cc-by-nc-sa-4.0 multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - other - object-detection - text-retrieval - token-classification - text-generation task_ids: [] pretty_name: Mario Maker 2 ninji levels tags: - text-mining --- # Mario Maker 2 ninji levels Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets) ## Dataset Description The Mario Maker 2 ninji levels dataset consists of 21 ninji levels from Nintendo's online service and aids `TheGreatRambler/mm2_ninji`. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022. ### How to use it You can load and iterate through the dataset with the following code: ```python from datasets import load_dataset ds = load_dataset("TheGreatRambler/mm2_ninji_level", split="train") print(next(iter(ds))) #OUTPUT: { 'data_id': 12171034, 'name': 'Rolling Snowballs', 'description': 'Make your way through the snowfields, and keep an eye\nout for Spikes and Snow Pokeys! Stomping on Snow Pokeys\nwill turn them into small snowballs, which you can pick up\nand throw. Play this course as many times as you want,\nand see if you can find the fastest way to the finish!', 'uploaded': 1575532800, 'ended': 1576137600, 'gamestyle': 3, 'theme': 6, 'medal_time': 26800, 'clear_condition': 0, 'clear_condition_magnitude': 0, 'unk3_0': 1309513, 'unk3_1': 62629737, 'unk3_2': 4355893, 'unk5': 1, 'unk6': 0, 'unk9': 0, 'level_data': [some binary data] } ``` Each row is a ninji level denoted by `data_id`. `TheGreatRambler/mm2_ninji` refers to these levels. `level_data` is the same format used in `TheGreatRambler/mm2_level` and the provided Kaitai struct file and `level.py` can be used to decode it: ```python from datasets import load_dataset from kaitaistruct import KaitaiStream from io import BytesIO from level import Level import zlib ds = load_dataset("TheGreatRambler/mm2_ninji_level", split="train") level_data = next(iter(ds))["level_data"] level = Level(KaitaiStream(BytesIO(zlib.decompress(level_data)))) # NOTE level.overworld.objects is a fixed size (limitation of Kaitai struct) # must iterate by object_count or null objects will be included for i in range(level.overworld.object_count): obj = level.overworld.objects[i] print("X: %d Y: %d ID: %s" % (obj.x, obj.y, obj.id)) #OUTPUT: X: 1200 Y: 400 ID: ObjId.block X: 1360 Y: 400 ID: ObjId.block X: 1360 Y: 240 ID: ObjId.block X: 1520 Y: 240 ID: ObjId.block X: 1680 Y: 240 ID: ObjId.block X: 1680 Y: 400 ID: ObjId.block X: 1840 Y: 400 ID: ObjId.block X: 2000 Y: 400 ID: ObjId.block X: 2160 Y: 400 ID: ObjId.block X: 2320 Y: 400 ID: ObjId.block X: 2480 Y: 560 ID: ObjId.block X: 2480 Y: 720 ID: ObjId.block X: 2480 Y: 880 ID: ObjId.block X: 2160 Y: 880 ID: ObjId.block ``` ## Data Structure ### Data Instances ```python { 'data_id': 12171034, 'name': 'Rolling Snowballs', 'description': 'Make your way through the snowfields, and keep an eye\nout for Spikes and Snow Pokeys! Stomping on Snow Pokeys\nwill turn them into small snowballs, which you can pick up\nand throw. Play this course as many times as you want,\nand see if you can find the fastest way to the finish!', 'uploaded': 1575532800, 'ended': 1576137600, 'gamestyle': 3, 'theme': 6, 'medal_time': 26800, 'clear_condition': 0, 'clear_condition_magnitude': 0, 'unk3_0': 1309513, 'unk3_1': 62629737, 'unk3_2': 4355893, 'unk5': 1, 'unk6': 0, 'unk9': 0, 'level_data': [some binary data] } ``` ### Data Fields |Field|Type|Description| |---|---|---| |data_id|int|The data ID of this ninji level| |name|string|Name| |description|string|Description| |uploaded|int|UTC timestamp of when this was uploaded| |ended|int|UTC timestamp of when this event ended| |gamestyle|int|Gamestyle, enum below| |theme|int|Theme, enum below| |medal_time|int|Time to get a medal in milliseconds| |clear_condition|int|Clear condition, enum below| |clear_condition_magnitude|int|If applicable, the magnitude of the clear condition| |unk3_0|int|Unknown| |unk3_1|int|Unknown| |unk3_2|int|Unknown| |unk5|int|Unknown| |unk6|int|Unknown| |unk9|int|Unknown| |level_data|bytes|The GZIP compressed decrypted level data, a kaitai struct file is provided to read this| |one_screen_thumbnail|bytes|The one screen course thumbnail, as a JPEG binary| |one_screen_thumbnail_url|string|The old URL of this thumbnail| |one_screen_thumbnail_size|int|The filesize of this thumbnail| |one_screen_thumbnail_filename|string|The filename of this thumbnail| |entire_thumbnail|bytes|The entire course thumbnail, as a JPEG binary| |entire_thumbnail_url|string|The old URL of this thumbnail| |entire_thumbnail_size|int|The filesize of this thumbnail| |entire_thumbnail_filename|string|The filename of this thumbnail| ### Data Splits The dataset only contains a train split. ## Enums The dataset contains some enum integer fields. They match those used by `TheGreatRambler/mm2_level` for the most part, but they are reproduced below: ```python GameStyles = { 0: "SMB1", 1: "SMB3", 2: "SMW", 3: "NSMBU", 4: "SM3DW" } CourseThemes = { 0: "Overworld", 1: "Underground", 2: "Castle", 3: "Airship", 4: "Underwater", 5: "Ghost house", 6: "Snow", 7: "Desert", 8: "Sky", 9: "Forest" } ClearConditions = { 137525990: "Reach the goal without landing after leaving the ground.", 199585683: "Reach the goal after defeating at least/all (n) Mechakoopa(s).", 272349836: "Reach the goal after defeating at least/all (n) Cheep Cheep(s).", 375673178: "Reach the goal without taking damage.", 426197923: "Reach the goal as Boomerang Mario.", 436833616: "Reach the goal while wearing a Shoe.", 713979835: "Reach the goal as Fire Mario.", 744927294: "Reach the goal as Frog Mario.", 751004331: "Reach the goal after defeating at least/all (n) Larry(s).", 900050759: "Reach the goal as Raccoon Mario.", 947659466: "Reach the goal after defeating at least/all (n) Blooper(s).", 976173462: "Reach the goal as Propeller Mario.", 994686866: "Reach the goal while wearing a Propeller Box.", 998904081: "Reach the goal after defeating at least/all (n) Spike(s).", 1008094897: "Reach the goal after defeating at least/all (n) Boom Boom(s).", 1051433633: "Reach the goal while holding a Koopa Shell.", 1061233896: "Reach the goal after defeating at least/all (n) Porcupuffer(s).", 1062253843: "Reach the goal after defeating at least/all (n) Charvaargh(s).", 1079889509: "Reach the goal after defeating at least/all (n) Bullet Bill(s).", 1080535886: "Reach the goal after defeating at least/all (n) Bully/Bullies.", 1151250770: "Reach the goal while wearing a Goomba Mask.", 1182464856: "Reach the goal after defeating at least/all (n) Hop-Chops.", 1219761531: "Reach the goal while holding a Red POW Block. OR Reach the goal after activating at least/all (n) Red POW Block(s).", 1221661152: "Reach the goal after defeating at least/all (n) Bob-omb(s).", 1259427138: "Reach the goal after defeating at least/all (n) Spiny/Spinies.", 1268255615: "Reach the goal after defeating at least/all (n) Bowser(s)/Meowser(s).", 1279580818: "Reach the goal after defeating at least/all (n) Ant Trooper(s).", 1283945123: "Reach the goal on a Lakitu's Cloud.", 1344044032: "Reach the goal after defeating at least/all (n) Boo(s).", 1425973877: "Reach the goal after defeating at least/all (n) Roy(s).", 1429902736: "Reach the goal while holding a Trampoline.", 1431944825: "Reach the goal after defeating at least/all (n) Morton(s).", 1446467058: "Reach the goal after defeating at least/all (n) Fish Bone(s).", 1510495760: "Reach the goal after defeating at least/all (n) Monty Mole(s).", 1656179347: "Reach the goal after picking up at least/all (n) 1-Up Mushroom(s).", 1665820273: "Reach the goal after defeating at least/all (n) Hammer Bro(s.).", 1676924210: "Reach the goal after hitting at least/all (n) P Switch(es). OR Reach the goal while holding a P Switch.", 1715960804: "Reach the goal after activating at least/all (n) POW Block(s). OR Reach the goal while holding a POW Block.", 1724036958: "Reach the goal after defeating at least/all (n) Angry Sun(s).", 1730095541: "Reach the goal after defeating at least/all (n) Pokey(s).", 1780278293: "Reach the goal as Superball Mario.", 1839897151: "Reach the goal after defeating at least/all (n) Pom Pom(s).", 1969299694: "Reach the goal after defeating at least/all (n) Peepa(s).", 2035052211: "Reach the goal after defeating at least/all (n) Lakitu(s).", 2038503215: "Reach the goal after defeating at least/all (n) Lemmy(s).", 2048033177: "Reach the goal after defeating at least/all (n) Lava Bubble(s).", 2076496776: "Reach the goal while wearing a Bullet Bill Mask.", 2089161429: "Reach the goal as Big Mario.", 2111528319: "Reach the goal as Cat Mario.", 2131209407: "Reach the goal after defeating at least/all (n) Goomba(s)/Galoomba(s).", 2139645066: "Reach the goal after defeating at least/all (n) Thwomp(s).", 2259346429: "Reach the goal after defeating at least/all (n) Iggy(s).", 2549654281: "Reach the goal while wearing a Dry Bones Shell.", 2694559007: "Reach the goal after defeating at least/all (n) Sledge Bro(s.).", 2746139466: "Reach the goal after defeating at least/all (n) Rocky Wrench(es).", 2749601092: "Reach the goal after grabbing at least/all (n) 50-Coin(s).", 2855236681: "Reach the goal as Flying Squirrel Mario.", 3036298571: "Reach the goal as Buzzy Mario.", 3074433106: "Reach the goal as Builder Mario.", 3146932243: "Reach the goal as Cape Mario.", 3174413484: "Reach the goal after defeating at least/all (n) Wendy(s).", 3206222275: "Reach the goal while wearing a Cannon Box.", 3314955857: "Reach the goal as Link.", 3342591980: "Reach the goal while you have Super Star invincibility.", 3346433512: "Reach the goal after defeating at least/all (n) Goombrat(s)/Goombud(s).", 3348058176: "Reach the goal after grabbing at least/all (n) 10-Coin(s).", 3353006607: "Reach the goal after defeating at least/all (n) Buzzy Beetle(s).", 3392229961: "Reach the goal after defeating at least/all (n) Bowser Jr.(s).", 3437308486: "Reach the goal after defeating at least/all (n) Koopa Troopa(s).", 3459144213: "Reach the goal after defeating at least/all (n) Chain Chomp(s).", 3466227835: "Reach the goal after defeating at least/all (n) Muncher(s).", 3481362698: "Reach the goal after defeating at least/all (n) Wiggler(s).", 3513732174: "Reach the goal as SMB2 Mario.", 3649647177: "Reach the goal in a Koopa Clown Car/Junior Clown Car.", 3725246406: "Reach the goal as Spiny Mario.", 3730243509: "Reach the goal in a Koopa Troopa Car.", 3748075486: "Reach the goal after defeating at least/all (n) Piranha Plant(s)/Jumping Piranha Plant(s).", 3797704544: "Reach the goal after defeating at least/all (n) Dry Bones.", 3824561269: "Reach the goal after defeating at least/all (n) Stingby/Stingbies.", 3833342952: "Reach the goal after defeating at least/all (n) Piranha Creeper(s).", 3842179831: "Reach the goal after defeating at least/all (n) Fire Piranha Plant(s).", 3874680510: "Reach the goal after breaking at least/all (n) Crates(s).", 3974581191: "Reach the goal after defeating at least/all (n) Ludwig(s).", 3977257962: "Reach the goal as Super Mario.", 4042480826: "Reach the goal after defeating at least/all (n) Skipsqueak(s).", 4116396131: "Reach the goal after grabbing at least/all (n) Coin(s).", 4117878280: "Reach the goal after defeating at least/all (n) Magikoopa(s).", 4122555074: "Reach the goal after grabbing at least/all (n) 30-Coin(s).", 4153835197: "Reach the goal as Balloon Mario.", 4172105156: "Reach the goal while wearing a Red POW Box.", 4209535561: "Reach the Goal while riding Yoshi.", 4269094462: "Reach the goal after defeating at least/all (n) Spike Top(s).", 4293354249: "Reach the goal after defeating at least/all (n) Banzai Bill(s)." } ``` <!-- TODO create detailed statistics --> ## Dataset Creation The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset. ## Considerations for Using the Data As these 21 levels were made and vetted by Nintendo the dataset contains no harmful language or depictions.
ura-hcmut/vmlu_vi
--- language: - vi size_categories: - 1K<n<10K configs: - config_name: default data_files: - split: test path: test.jsonl - split: valid path: valid.jsonl - split: dev path: dev.jsonl ---
japanese-asr/whisper_transcriptions.reazonspeech.all_31
--- dataset_info: config_name: all features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 30558823060.0 num_examples: 267996 download_size: 30317834028 dataset_size: 30558823060.0 configs: - config_name: all data_files: - split: train path: all/train-* ---
jazzysnake01/oasst-1-hun-openai
--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 8053160 num_examples: 5312 download_size: 3874931 dataset_size: 8053160 configs: - config_name: default data_files: - split: train path: data/train-* ---
mHossain/final_train_v4_test_40000
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 5756432.4 num_examples: 18000 - name: test num_bytes: 639603.6 num_examples: 2000 download_size: 2778012 dataset_size: 6396036.0 --- # Dataset Card for "final_train_v4_test_40000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Pampkinus/Volodymyr-Zelenskyj
--- license: openrail --- Faceset of the current prezident of Ukraine, 8480 aligned pictures (JPG) of his face from the latest UN meating https://cs.wikipedia.org/wiki/Volodymyr_Zelenskyj
kgr123/quality_counter_3500
--- dataset_info: features: - name: context dtype: string - name: word dtype: string - name: claim dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 19330578 num_examples: 1929 - name: train num_bytes: 19139136 num_examples: 1935 - name: validation num_bytes: 19531896 num_examples: 1941 download_size: 12876045 dataset_size: 58001610 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---
YaNWoni/GARAM
--- license: openrail ---