Datasets:
davanstrien
/
summary-test

Dataset card Data Studio Files
xet
Community
1
datasetId
stringlengths
2
117
card
stringlengths
19
1.01M
felgryn/mini-platypus
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245921 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---
pankajemplay/mistral-intent-data
--- dataset_info: features: - name: User Query dtype: string - name: Intent dtype: string - name: id type dtype: string - name: id value dtype: string - name: id slot filled dtype: bool - name: Task dtype: string - name: task slot filled dtype: bool - name: Bot Response dtype: string - name: text dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 853957 num_examples: 1171 download_size: 188944 dataset_size: 853957 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "mistral-intent-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.0_seed_1_tp_0.5
--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43627296 num_examples: 18928 - name: epoch_1 num_bytes: 44147982 num_examples: 18928 - name: epoch_2 num_bytes: 44213397 num_examples: 18928 - name: epoch_3 num_bytes: 44256973 num_examples: 18928 - name: epoch_4 num_bytes: 44271592 num_examples: 18928 - name: epoch_5 num_bytes: 44273341 num_examples: 18928 - name: epoch_6 num_bytes: 44262994 num_examples: 18928 - name: epoch_7 num_bytes: 44253349 num_examples: 18928 - name: epoch_8 num_bytes: 44250328 num_examples: 18928 - name: epoch_9 num_bytes: 44244920 num_examples: 18928 - name: epoch_10 num_bytes: 44245188 num_examples: 18928 - name: epoch_11 num_bytes: 44245798 num_examples: 18928 - name: epoch_12 num_bytes: 44244347 num_examples: 18928 - name: epoch_13 num_bytes: 44244531 num_examples: 18928 - name: epoch_14 num_bytes: 44244291 num_examples: 18928 - name: epoch_15 num_bytes: 44243364 num_examples: 18928 - name: epoch_16 num_bytes: 44245334 num_examples: 18928 - name: epoch_17 num_bytes: 44244087 num_examples: 18928 - name: epoch_18 num_bytes: 44245204 num_examples: 18928 - name: epoch_19 num_bytes: 44244918 num_examples: 18928 - name: epoch_20 num_bytes: 44243496 num_examples: 18928 - name: epoch_21 num_bytes: 44245922 num_examples: 18928 - name: epoch_22 num_bytes: 44244974 num_examples: 18928 - name: epoch_23 num_bytes: 44245847 num_examples: 18928 - name: epoch_24 num_bytes: 44245653 num_examples: 18928 - name: epoch_25 num_bytes: 44245656 num_examples: 18928 - name: epoch_26 num_bytes: 44245912 num_examples: 18928 - name: epoch_27 num_bytes: 44246318 num_examples: 18928 - name: epoch_28 num_bytes: 44246995 num_examples: 18928 - name: epoch_29 num_bytes: 44247062 num_examples: 18928 download_size: 684059925 dataset_size: 1326707069 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
foilfoilfoil/GGB-Discord-Data-top-6
--- license: other ---
sc3069/zx
--- dataset_info: features: - name: input dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 10329536 num_examples: 350 download_size: 1991265 dataset_size: 10329536 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "zx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DigitalUmuganda/NMT_Rwandan-Gazette_parallel_data_en_kin
--- license: cc task_categories: - translation language: - rw - en tags: - kinyarwanda - english - machine-translation - low-ressourced languages pretty_name: 'NMT Rwanda Gazette parallel data ' size_categories: - 100K<n<1M --- ## Dataset Details ### Dataset Description This is a curated parallel dataset from the Official Gazette of the Republic of Rwanda. It has been curated to extract corresponding English and Kinyarwanda text and in the future we shall add French to the mix - **Curated by:** Digital Umuganda - **Language(s) (NLP):** Kinyarwanda and English - **License:** cc-by-4.0 ### Dataset Sources [optional] The dataset original content was retrieved from the Rwandan ministry of Justice [website](https://www.minijust.gov.rw/official-gazette) <!-- Provide the basic links for the dataset. --> ## Uses The dataset is mainly used for machine translation, however it can be used for other NLP tasks such as text generation and NER
AntoineBlanot/alpaca-llama2-chat
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 46095859 num_examples: 52002 download_size: 0 dataset_size: 46095859 --- # Dataset Card for "alpaca-llama2-chat" This dataset is the [alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) dataset formatted for [llama2-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf). The default system prompt, as well as special tokens has all been added for a ready-to-train dataset. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
reformatco/sd1_5-regularization-images
--- license: mit --- A collection of regularization / class instance datasets for the [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) model to use for DreamBooth prior preservation loss training. Files labeled with "mse vae" used the [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) VAE. For ease of use, datasets are stored as zip files containing 512x512 PNG images. The number of images in each zip file is specified at the end of the filename. There is currently a bug where HuggingFace is incorrectly reporting that the datasets are pickled. They are not picked, they are simple ZIP files containing the images. This dataset is based on the conventions setup by [Progamergov](https://huggingface.co/datasets/ProGamerGov/StableDiffusion-v1-5-Regularization-Images) and their very useful regularization images. Currently this repository contains the following datasets (datasets are named after the prompt they used): * "**interior design**": 2354 images generated using 50 DDIM steps and a CFG of 7, using the MSE VAE.
dog/fuego-20230222-154818-7e82ca
--- tags: - fuego fuego: id: 20230222-154818-7e82ca status: done script: run.py requirements_file: requirements.txt space_id: dog/fuego-runner space_hardware: cpu-basic ---
davanstrien/raw-tldr-dataset-sft
--- dataset_info: features: - name: datasetId dtype: string - name: author dtype: string - name: last_modified dtype: timestamp[us, tz=UTC] - name: downloads dtype: int64 - name: likes dtype: int64 - name: tags sequence: string - name: task_categories sequence: string - name: createdAt dtype: timestamp[us, tz=UTC] - name: card dtype: string - name: parsed_card dtype: string - name: length dtype: int64 - name: input dtype: string - name: generation_model sequence: string - name: generation_prompt sequence: string - name: raw_generation_responses sequence: string - name: generations sequence: string splits: - name: train num_bytes: 83205399 num_examples: 3132 download_size: 35242193 dataset_size: 83205399 configs: - config_name: default data_files: - split: train path: data/train-* ---
shredder-31/Min_Sum_SummarizationData
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 28776440 num_examples: 1500 - name: dev num_bytes: 5861659 num_examples: 300 - name: test num_bytes: 3887278 num_examples: 200 download_size: 17684981 dataset_size: 38525377 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* - split: test path: data/test-* ---
liuyanchen1015/MULTI_VALUE_wnli_reduplicate_interrogative
--- 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: 127 num_examples: 1 - name: train num_bytes: 1774 num_examples: 10 download_size: 5923 dataset_size: 1901 --- # Dataset Card for "MULTI_VALUE_wnli_reduplicate_interrogative" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Francesco/peixos-fish
--- dataset_info: features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': peixos '1': peix '2': taca annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - object-detection task_ids: [] pretty_name: peixos-fish tags: - rf100 --- # Dataset Card for peixos-fish ** The original COCO dataset is stored at `dataset.tar.gz`** ## Dataset Description - **Homepage:** https://universe.roboflow.com/object-detection/peixos-fish - **Point of Contact:** francesco.zuppichini@gmail.com ### Dataset Summary peixos-fish ### Supported Tasks and Leaderboards - `object-detection`: The dataset can be used to train a model for Object Detection. ### Languages English ## Dataset Structure ### Data Instances A data point comprises an image and its object annotations. ``` { 'image_id': 15, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>, 'width': 964043, 'height': 640, 'objects': { 'id': [114, 115, 116, 117], 'area': [3796, 1596, 152768, 81002], 'bbox': [ [302.0, 109.0, 73.0, 52.0], [810.0, 100.0, 57.0, 28.0], [160.0, 31.0, 248.0, 616.0], [741.0, 68.0, 202.0, 401.0] ], 'category': [4, 4, 0, 0] } } ``` ### Data Fields - `image`: the image id - `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: the image width - `height`: the image height - `objects`: a dictionary containing bounding box metadata for the objects present on the image - `id`: the annotation id - `area`: the area of the bounding box - `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) - `category`: the object's category. #### Who are the annotators? Annotators are Roboflow users ## Additional Information ### Licensing Information See original homepage https://universe.roboflow.com/object-detection/peixos-fish ### Citation Information ``` @misc{ peixos-fish, title = { peixos fish Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \url{ https://universe.roboflow.com/object-detection/peixos-fish } }, url = { https://universe.roboflow.com/object-detection/peixos-fish }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 }, }" ``` ### Contributions Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
jlbaker361/multiplication_whole
--- dataset_info: features: - name: input dtype: string - name: output dtype: float64 - name: text dtype: string splits: - name: train num_bytes: 1255311.0 num_examples: 29376 - name: test num_bytes: 139479.0 num_examples: 3264 download_size: 896516 dataset_size: 1394790.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "multiplication_whole" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Enno-Ai/fr-instructs
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: source dtype: string splits: - name: train num_bytes: 5904510661 num_examples: 11794112 download_size: 1623654660 dataset_size: 5904510661 license: cc-by-2.5 task_categories: - text2text-generation - table-question-answering language: - fr size_categories: - 10M<n<100M --- # A collection of 12 million french-only instructions deduplicated from various sources Source : - clips/mqa-fr-faq - multilingual-wikihow-qa-16k - MBZUAI/Bactrian-X - argilla/databricks-dolly-15k-curated-multilingual - innermost47/alpaca-fr - etalab-ia/piaf
strombergnlp/twitter_pos_vcb
--- annotations_creators: - machine-generated language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - token-classification task_ids: - part-of-speech paperswithcode_id: twitter-pos-vcb pretty_name: Twitter PoS VCB --- # Dataset Card for "twitter-pos-vcb" ## 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://gate.ac.uk/wiki/twitter-postagger.html](https://gate.ac.uk/wiki/twitter-postagger.html) - **Repository:** [https://github.com/GateNLP/gateplugin-Twitter](https://github.com/GateNLP/gateplugin-Twitter) - **Paper:** [https://aclanthology.org/R13-1026.pdf](https://aclanthology.org/R13-1026.pdf) - **Point of Contact:** [Leon Derczynski](https://github.com/leondz) - **Size of downloaded dataset files:** 4.51 MiB - **Size of the generated dataset:** 26.88 MB - **Total amount of disk used:** 31.39 MB ### Dataset Summary Part-of-speech information is basic NLP task. However, Twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. This data is the vote-constrained bootstrapped data generate to support state-of-the-art results. The data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset. The tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged jointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs are completely compatible over a whole tweet, is that tweet added to the dataset. This data is recommend for use a training data **only**, and not evaluation data. For more details see https://gate.ac.uk/wiki/twitter-postagger.html and https://aclanthology.org/R13-1026.pdf ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English, non-region-specific. `bcp47:en` ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### twitter_pos_vcb - `id`: a `string` feature. - `tokens`: a `list` of `string` features. - `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices: ```python ``` ### Data Splits | name |tokens|sentences| |---------|----:|---------:| |twitter-pos-vcb|1 543 126| 159 492| ## 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 Creative Commons Attribution 4.0 (CC-BY) ### Citation Information ``` @inproceedings{derczynski2013twitter, title={Twitter part-of-speech tagging for all: Overcoming sparse and noisy data}, author={Derczynski, Leon and Ritter, Alan and Clark, Sam and Bontcheva, Kalina}, booktitle={Proceedings of the international conference recent advances in natural language processing ranlp 2013}, pages={198--206}, year={2013} } ``` ### Contributions Author uploaded ([@leondz](https://github.com/leondz))
Technoculture/riddle_sense
--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: output dtype: string - name: input dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 829501 num_examples: 3510 - name: validation num_bytes: 239903 num_examples: 1021 - name: test num_bytes: 249470 num_examples: 1184 download_size: 651507 dataset_size: 1318874 task_categories: - question-answering language: - en tags: - reasoning pretty_name: Riddle Sen size_categories: - 1K<n<10K --- [riddle_sense](https://huggingface.co/datasets/riddle_sense) dataset formatted into an alpaca format dataset for instruction tuning LLMs for reasoning capabilities.
polinaeterna/yet_another_test
--- dataset_info: features: - name: x dtype: int64 - name: y dtype: int64 splits: - name: train num_bytes: 1600000 num_examples: 100000 - name: test num_bytes: 112000 num_examples: 7000 download_size: 1192989 dataset_size: 1712000 builder_config: data_dir: data --- # Dataset Card for "yet_another_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_fblgit__UNA-SimpleSmaug-34b-v1beta
--- pretty_name: Evaluation run of fblgit/UNA-SimpleSmaug-34b-v1beta dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [fblgit/UNA-SimpleSmaug-34b-v1beta](https://huggingface.co/fblgit/UNA-SimpleSmaug-34b-v1beta)\ \ 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_fblgit__UNA-SimpleSmaug-34b-v1beta\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T14:36:13.989348](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-SimpleSmaug-34b-v1beta/blob/main/results_2024-02-09T14-36-13.989348.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\": 0.7649553475572979,\n\ \ \"acc_stderr\": 0.02829491282350785,\n \"acc_norm\": 0.7681713551647662,\n\ \ \"acc_norm_stderr\": 0.028841138819719683,\n \"mc1\": 0.5299877600979193,\n\ \ \"mc1_stderr\": 0.017471992091697534,\n \"mc2\": 0.7016557407771556,\n\ \ \"mc2_stderr\": 0.014224339474805845\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7192832764505119,\n \"acc_stderr\": 0.013131238126975583,\n\ \ \"acc_norm\": 0.7457337883959044,\n \"acc_norm_stderr\": 0.012724999945157736\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6709818761202948,\n\ \ \"acc_stderr\": 0.004688963175758129,\n \"acc_norm\": 0.8673571001792472,\n\ \ \"acc_norm_stderr\": 0.003384951803213472\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.881578947368421,\n \"acc_stderr\": 0.02629399585547494,\n\ \ \"acc_norm\": 0.881578947368421,\n \"acc_norm_stderr\": 0.02629399585547494\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\ \ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \ \ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.024618298195866514,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.024618298195866514\n \ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9027777777777778,\n\ \ \"acc_stderr\": 0.024774516250440182,\n \"acc_norm\": 0.9027777777777778,\n\ \ \"acc_norm_stderr\": 0.024774516250440182\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.59,\n \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n\ \ \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.7225433526011561,\n\ \ \"acc_stderr\": 0.034140140070440354,\n \"acc_norm\": 0.7225433526011561,\n\ \ \"acc_norm_stderr\": 0.034140140070440354\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.774468085106383,\n \"acc_stderr\": 0.027321078417387533,\n\ \ \"acc_norm\": 0.774468085106383,\n \"acc_norm_stderr\": 0.027321078417387533\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7586206896551724,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.7586206896551724,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.7380952380952381,\n \"acc_stderr\": 0.02264421261552521,\n \"\ acc_norm\": 0.7380952380952381,\n \"acc_norm_stderr\": 0.02264421261552521\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5476190476190477,\n\ \ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.5476190476190477,\n\ \ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|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_biology|5\": {\n \"acc\": 0.9064516129032258,\n\ \ \"acc_stderr\": 0.016565754668270982,\n \"acc_norm\": 0.9064516129032258,\n\ \ \"acc_norm_stderr\": 0.016565754668270982\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6847290640394089,\n \"acc_stderr\": 0.03269080871970186,\n\ \ \"acc_norm\": 0.6847290640394089,\n \"acc_norm_stderr\": 0.03269080871970186\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165044,\n \"acc_norm\"\ : 0.77,\n \"acc_norm_stderr\": 0.042295258468165044\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706467,\n\ \ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706467\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9242424242424242,\n \"acc_stderr\": 0.018852670234993093,\n \"\ acc_norm\": 0.9242424242424242,\n \"acc_norm_stderr\": 0.018852670234993093\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9792746113989638,\n \"acc_stderr\": 0.010281417011909025,\n\ \ \"acc_norm\": 0.9792746113989638,\n \"acc_norm_stderr\": 0.010281417011909025\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8153846153846154,\n \"acc_stderr\": 0.019671632413100295,\n\ \ \"acc_norm\": 0.8153846153846154,\n \"acc_norm_stderr\": 0.019671632413100295\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.46296296296296297,\n \"acc_stderr\": 0.030401786406101507,\n \ \ \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.030401786406101507\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.023005459446673936,\n\ \ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.023005459446673936\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5165562913907285,\n \"acc_stderr\": 0.04080244185628972,\n \"\ acc_norm\": 0.5165562913907285,\n \"acc_norm_stderr\": 0.04080244185628972\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9247706422018349,\n \"acc_stderr\": 0.011308662537571727,\n \"\ acc_norm\": 0.9247706422018349,\n \"acc_norm_stderr\": 0.011308662537571727\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6574074074074074,\n \"acc_stderr\": 0.032365852526021574,\n \"\ acc_norm\": 0.6574074074074074,\n \"acc_norm_stderr\": 0.032365852526021574\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"\ acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9113924050632911,\n \"acc_stderr\": 0.018498315206865384,\n \ \ \"acc_norm\": 0.9113924050632911,\n \"acc_norm_stderr\": 0.018498315206865384\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.820627802690583,\n\ \ \"acc_stderr\": 0.0257498195691928,\n \"acc_norm\": 0.820627802690583,\n\ \ \"acc_norm_stderr\": 0.0257498195691928\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8625954198473282,\n \"acc_stderr\": 0.030194823996804475,\n\ \ \"acc_norm\": 0.8625954198473282,\n \"acc_norm_stderr\": 0.030194823996804475\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035216,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035216\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.02923927267563275,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.02923927267563275\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.026321383198783674,\n\ \ \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.026321383198783674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5625,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.033932957297610096,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.033932957297610096\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9444444444444444,\n\ \ \"acc_stderr\": 0.01500631280644693,\n \"acc_norm\": 0.9444444444444444,\n\ \ \"acc_norm_stderr\": 0.01500631280644693\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9169859514687101,\n\ \ \"acc_stderr\": 0.009866287394639541,\n \"acc_norm\": 0.9169859514687101,\n\ \ \"acc_norm_stderr\": 0.009866287394639541\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8265895953757225,\n \"acc_stderr\": 0.02038322955113502,\n\ \ \"acc_norm\": 0.8265895953757225,\n \"acc_norm_stderr\": 0.02038322955113502\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7921787709497207,\n\ \ \"acc_stderr\": 0.01357024832508134,\n \"acc_norm\": 0.7921787709497207,\n\ \ \"acc_norm_stderr\": 0.01357024832508134\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8594771241830066,\n \"acc_stderr\": 0.019899435463539946,\n\ \ \"acc_norm\": 0.8594771241830066,\n \"acc_norm_stderr\": 0.019899435463539946\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8038585209003215,\n\ \ \"acc_stderr\": 0.022552447780478033,\n \"acc_norm\": 0.8038585209003215,\n\ \ \"acc_norm_stderr\": 0.022552447780478033\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8703703703703703,\n \"acc_stderr\": 0.018689725721062072,\n\ \ \"acc_norm\": 0.8703703703703703,\n \"acc_norm_stderr\": 0.018689725721062072\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6347517730496454,\n \"acc_stderr\": 0.02872386385328127,\n \ \ \"acc_norm\": 0.6347517730496454,\n \"acc_norm_stderr\": 0.02872386385328127\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5951760104302477,\n\ \ \"acc_stderr\": 0.012536743830953986,\n \"acc_norm\": 0.5951760104302477,\n\ \ \"acc_norm_stderr\": 0.012536743830953986\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8235294117647058,\n \"acc_stderr\": 0.023157468308559345,\n\ \ \"acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.023157468308559345\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8186274509803921,\n \"acc_stderr\": 0.015588643495370463,\n \ \ \"acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.015588643495370463\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.022923004094736847,\n\ \ \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.022923004094736847\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.9104477611940298,\n\ \ \"acc_stderr\": 0.02019067053502792,\n \"acc_norm\": 0.9104477611940298,\n\ \ \"acc_norm_stderr\": 0.02019067053502792\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n\ \ \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5299877600979193,\n\ \ \"mc1_stderr\": 0.017471992091697534,\n \"mc2\": 0.7016557407771556,\n\ \ \"mc2_stderr\": 0.014224339474805845\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8382004735595896,\n \"acc_stderr\": 0.010350128010292404\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7247915087187263,\n \ \ \"acc_stderr\": 0.012302114305862656\n }\n}\n```" repo_url: https://huggingface.co/fblgit/UNA-SimpleSmaug-34b-v1beta 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_09T14_36_13.989348 path: - '**/details_harness|arc:challenge|25_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-09T14-36-13.989348.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|gsm8k|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hellaswag|10_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-36-13.989348.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T14-36-13.989348.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T14-36-13.989348.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T14_36_13.989348 path: - '**/details_harness|winogrande|5_2024-02-09T14-36-13.989348.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-09T14-36-13.989348.parquet' - config_name: results data_files: - split: 2024_02_09T14_36_13.989348 path: - results_2024-02-09T14-36-13.989348.parquet - split: latest path: - results_2024-02-09T14-36-13.989348.parquet --- # Dataset Card for Evaluation run of fblgit/UNA-SimpleSmaug-34b-v1beta <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [fblgit/UNA-SimpleSmaug-34b-v1beta](https://huggingface.co/fblgit/UNA-SimpleSmaug-34b-v1beta) 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_fblgit__UNA-SimpleSmaug-34b-v1beta", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T14:36:13.989348](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-SimpleSmaug-34b-v1beta/blob/main/results_2024-02-09T14-36-13.989348.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": 0.7649553475572979, "acc_stderr": 0.02829491282350785, "acc_norm": 0.7681713551647662, "acc_norm_stderr": 0.028841138819719683, "mc1": 0.5299877600979193, "mc1_stderr": 0.017471992091697534, "mc2": 0.7016557407771556, "mc2_stderr": 0.014224339474805845 }, "harness|arc:challenge|25": { "acc": 0.7192832764505119, "acc_stderr": 0.013131238126975583, "acc_norm": 0.7457337883959044, "acc_norm_stderr": 0.012724999945157736 }, "harness|hellaswag|10": { "acc": 0.6709818761202948, "acc_stderr": 0.004688963175758129, "acc_norm": 0.8673571001792472, "acc_norm_stderr": 0.003384951803213472 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066653, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066653 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.881578947368421, "acc_stderr": 0.02629399585547494, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.02629399585547494 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8, "acc_stderr": 0.024618298195866514, "acc_norm": 0.8, "acc_norm_stderr": 0.024618298195866514 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9027777777777778, "acc_stderr": 0.024774516250440182, "acc_norm": 0.9027777777777778, "acc_norm_stderr": 0.024774516250440182 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237101, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237101 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7225433526011561, "acc_stderr": 0.034140140070440354, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.034140140070440354 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5392156862745098, "acc_stderr": 0.04959859966384181, "acc_norm": 0.5392156862745098, "acc_norm_stderr": 0.04959859966384181 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.774468085106383, "acc_stderr": 0.027321078417387533, "acc_norm": 0.774468085106383, "acc_norm_stderr": 0.027321078417387533 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7586206896551724, "acc_stderr": 0.03565998174135302, "acc_norm": 0.7586206896551724, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7380952380952381, "acc_stderr": 0.02264421261552521, "acc_norm": 0.7380952380952381, "acc_norm_stderr": 0.02264421261552521 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5476190476190477, "acc_stderr": 0.044518079590553275, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.044518079590553275 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.9064516129032258, "acc_stderr": 0.016565754668270982, "acc_norm": 0.9064516129032258, "acc_norm_stderr": 0.016565754668270982 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6847290640394089, "acc_stderr": 0.03269080871970186, "acc_norm": 0.6847290640394089, "acc_norm_stderr": 0.03269080871970186 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.042295258468165044, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165044 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8666666666666667, "acc_stderr": 0.026544435312706467, "acc_norm": 0.8666666666666667, "acc_norm_stderr": 0.026544435312706467 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9242424242424242, "acc_stderr": 0.018852670234993093, "acc_norm": 0.9242424242424242, "acc_norm_stderr": 0.018852670234993093 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9792746113989638, "acc_stderr": 0.010281417011909025, "acc_norm": 0.9792746113989638, "acc_norm_stderr": 0.010281417011909025 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8153846153846154, "acc_stderr": 0.019671632413100295, "acc_norm": 0.8153846153846154, "acc_norm_stderr": 0.019671632413100295 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.030401786406101507, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.030401786406101507 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8529411764705882, "acc_stderr": 0.023005459446673936, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.023005459446673936 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5165562913907285, "acc_stderr": 0.04080244185628972, "acc_norm": 0.5165562913907285, "acc_norm_stderr": 0.04080244185628972 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9247706422018349, "acc_stderr": 0.011308662537571727, "acc_norm": 0.9247706422018349, "acc_norm_stderr": 0.011308662537571727 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6574074074074074, "acc_stderr": 0.032365852526021574, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.032365852526021574 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9264705882352942, "acc_stderr": 0.018318855850089678, "acc_norm": 0.9264705882352942, "acc_norm_stderr": 0.018318855850089678 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9113924050632911, "acc_stderr": 0.018498315206865384, "acc_norm": 0.9113924050632911, "acc_norm_stderr": 0.018498315206865384 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.820627802690583, "acc_stderr": 0.0257498195691928, "acc_norm": 0.820627802690583, "acc_norm_stderr": 0.0257498195691928 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8625954198473282, "acc_stderr": 0.030194823996804475, "acc_norm": 0.8625954198473282, "acc_norm_stderr": 0.030194823996804475 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8760330578512396, "acc_stderr": 0.030083098716035216, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.030083098716035216 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8981481481481481, "acc_stderr": 0.02923927267563275, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.02923927267563275 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8711656441717791, "acc_stderr": 0.026321383198783674, "acc_norm": 0.8711656441717791, "acc_norm_stderr": 0.026321383198783674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5625, "acc_stderr": 0.04708567521880525, "acc_norm": 0.5625, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.8640776699029126, "acc_stderr": 0.033932957297610096, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.033932957297610096 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9444444444444444, "acc_stderr": 0.01500631280644693, "acc_norm": 0.9444444444444444, "acc_norm_stderr": 0.01500631280644693 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9169859514687101, "acc_stderr": 0.009866287394639541, "acc_norm": 0.9169859514687101, "acc_norm_stderr": 0.009866287394639541 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8265895953757225, "acc_stderr": 0.02038322955113502, "acc_norm": 0.8265895953757225, "acc_norm_stderr": 0.02038322955113502 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.7921787709497207, "acc_stderr": 0.01357024832508134, "acc_norm": 0.7921787709497207, "acc_norm_stderr": 0.01357024832508134 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8594771241830066, "acc_stderr": 0.019899435463539946, "acc_norm": 0.8594771241830066, "acc_norm_stderr": 0.019899435463539946 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8038585209003215, "acc_stderr": 0.022552447780478033, "acc_norm": 0.8038585209003215, "acc_norm_stderr": 0.022552447780478033 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8703703703703703, "acc_stderr": 0.018689725721062072, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.018689725721062072 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6347517730496454, "acc_stderr": 0.02872386385328127, "acc_norm": 0.6347517730496454, "acc_norm_stderr": 0.02872386385328127 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5951760104302477, "acc_stderr": 0.012536743830953986, "acc_norm": 0.5951760104302477, "acc_norm_stderr": 0.012536743830953986 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8235294117647058, "acc_stderr": 0.023157468308559345, "acc_norm": 0.8235294117647058, "acc_norm_stderr": 0.023157468308559345 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8186274509803921, "acc_stderr": 0.015588643495370463, "acc_norm": 0.8186274509803921, "acc_norm_stderr": 0.015588643495370463 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8489795918367347, "acc_stderr": 0.022923004094736847, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.022923004094736847 }, "harness|hendrycksTest-sociology|5": { "acc": 0.9104477611940298, "acc_stderr": 0.02019067053502792, "acc_norm": 0.9104477611940298, "acc_norm_stderr": 0.02019067053502792 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness|hendrycksTest-virology|5": { "acc": 0.5783132530120482, "acc_stderr": 0.03844453181770917, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8596491228070176, "acc_stderr": 0.026640582539133196, "acc_norm": 0.8596491228070176, "acc_norm_stderr": 0.026640582539133196 }, "harness|truthfulqa:mc|0": { "mc1": 0.5299877600979193, "mc1_stderr": 0.017471992091697534, "mc2": 0.7016557407771556, "mc2_stderr": 0.014224339474805845 }, "harness|winogrande|5": { "acc": 0.8382004735595896, "acc_stderr": 0.010350128010292404 }, "harness|gsm8k|5": { "acc": 0.7247915087187263, "acc_stderr": 0.012302114305862656 } } ``` ## 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]
antonixe/river_source
--- task_categories: - question-answering tags: - art --- # 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 [More Information Needed] ### Contributions [More Information Needed]
cmani/celeb-identities
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Dua_Lipa '1': Emma_Watson '2': Kamal_Hassan '3': Kim_Kardashian '4': Morgan_Freeman '5': Rajanikanth '6': Robert_Downey_Jr '7': Salma_Hayek '8': Tom_Cruise splits: - name: train num_bytes: 2063036.0 num_examples: 33 download_size: 2061066 dataset_size: 2063036.0 --- # Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
WforGodot/addtrain345
--- license: openrail ---
louisbrulenaudet/code-famille-aide-sociale
--- license: apache-2.0 language: - fr multilinguality: - monolingual tags: - finetuning - legal - french law - droit français - Code de la famille et de l'aide sociale source_datasets: - original pretty_name: Code de la famille et de l'aide sociale task_categories: - text-generation - table-question-answering - summarization - text-retrieval - question-answering - text-classification size_categories: - 1K<n<10K --- # Code de la famille et de l'aide sociale, non-instruct (2024-04-15) This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice. Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach. Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks. Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways: - Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions. - Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs. - Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more. - Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs. - Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text. ## Concurrent reading of the LegalKit To use all the legal data published on LegalKit, you can use this code snippet: ```python # -*- coding: utf-8 -*- import concurrent.futures import os import datasets from tqdm.notebook import tqdm def dataset_loader( name:str, streaming:bool=True ) -> datasets.Dataset: """ Helper function to load a single dataset in parallel. Parameters ---------- name : str Name of the dataset to be loaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- dataset : datasets.Dataset Loaded dataset object. Raises ------ Exception If an error occurs during dataset loading. """ try: return datasets.load_dataset( name, split="train", streaming=streaming ) except Exception as exc: logging.error(f"Error loading dataset {name}: {exc}") return None def load_datasets( req:list, streaming:bool=True ) -> list: """ Downloads datasets specified in a list and creates a list of loaded datasets. Parameters ---------- req : list A list containing the names of datasets to be downloaded. streaming : bool, optional Determines if datasets are streamed. Default is True. Returns ------- datasets_list : list A list containing loaded datasets as per the requested names provided in 'req'. Raises ------ Exception If an error occurs during dataset loading or processing. Examples -------- >>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False) """ datasets_list = [] with concurrent.futures.ThreadPoolExecutor() as executor: future_to_dataset = {executor.submit(dataset_loader, name): name for name in req} for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)): name = future_to_dataset[future] try: dataset = future.result() if dataset: datasets_list.append(dataset) except Exception as exc: logging.error(f"Error processing dataset {name}: {exc}") return datasets_list req = [ "louisbrulenaudet/code-artisanat", "louisbrulenaudet/code-action-sociale-familles", # ... ] datasets_list = load_datasets( req=req, streaming=True ) dataset = datasets.concatenate_datasets( datasets_list ) ``` ## Dataset generation This JSON file is a list of dictionaries, each dictionary contains the following fields: - `instruction`: `string`, presenting the instruction linked to the element. - `input`: `string`, signifying the input details for the element. - `output`: `string`, indicating the output information for the element. - `start`: `string`, the date of entry into force of the article. - `expiration`: `string`, the date of expiration of the article. - `num`: `string`, the id of the article. We used the following list of instructions for generating the dataset: ```python instructions = [ "Compose l'intégralité de l'article sous forme écrite.", "Écris la totalité du contenu de l'article.", "Formule la totalité du texte présent dans l'article.", "Produis l'intégralité de l'article en écriture.", "Développe l'article dans son ensemble par écrit.", "Génère l'ensemble du texte contenu dans l'article.", "Formule le contenu intégral de l'article en entier.", "Rédige la totalité du texte de l'article en entier.", "Compose l'intégralité du contenu textuel de l'article.", "Rédige l'ensemble du texte qui constitue l'article.", "Formule l'article entier dans son contenu écrit.", "Composez l'intégralité de l'article sous forme écrite.", "Écrivez la totalité du contenu de l'article.", "Formulez la totalité du texte présent dans l'article.", "Développez l'article dans son ensemble par écrit.", "Générez l'ensemble du texte contenu dans l'article.", "Formulez le contenu intégral de l'article en entier.", "Rédigez la totalité du texte de l'article en entier.", "Composez l'intégralité du contenu textuel de l'article.", "Écrivez l'article dans son intégralité en termes de texte.", "Rédigez l'ensemble du texte qui constitue l'article.", "Formulez l'article entier dans son contenu écrit.", "Composer l'intégralité de l'article sous forme écrite.", "Écrire la totalité du contenu de l'article.", "Formuler la totalité du texte présent dans l'article.", "Produire l'intégralité de l'article en écriture.", "Développer l'article dans son ensemble par écrit.", "Générer l'ensemble du texte contenu dans l'article.", "Formuler le contenu intégral de l'article en entier.", "Rédiger la totalité du texte de l'article en entier.", "Composer l'intégralité du contenu textuel de l'article.", "Rédiger l'ensemble du texte qui constitue l'article.", "Formuler l'article entier dans son contenu écrit.", "Quelles sont les dispositions de l'article ?", "Quelles dispositions sont incluses dans l'article ?", "Quelles sont les dispositions énoncées dans l'article ?", "Quel est le texte intégral de l'article ?", "Quelle est la lettre de l'article ?" ] ``` ## Feedback If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
distil-whisper/tedlium-long-form
--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: string splits: - name: validation num_bytes: 180166870.0 num_examples: 8 - name: test num_bytes: 285107770.0 num_examples: 11 download_size: 284926490 dataset_size: 465274640.0 --- # Dataset Card for "tedlium-long-form" To create the dataset: ```python import os import numpy as np from datasets import load_dataset, DatasetDict, Dataset, Audio import soundfile as sf from tqdm import tqdm tedlium = load_dataset("LIUM/tedlium", "release3") merged_dataset = DatasetDict() validation_speaker_ids = [ "Al_Gore", "Barry_Schwartz", "Blaise_Agueray_Arcas", "Brian_Cox", "Craig_Venter", "David_Merrill", "Elizabeth_Gilbert", "Wade_Davis", ] validation_dataset_merged = {speaker_id: {"audio": [], "text": ""} for speaker_id in validation_speaker_ids} test_speaker_ids = [ "AimeeMullins", "BillGates", "DanBarber", "DanBarber_2010_S103", "DanielKahneman", "EricMead_2009P_EricMead", "GaryFlake", "JamesCameron", "JaneMcGonigal", "MichaelSpecter", "RobertGupta", ] test_dataset_merged = {speaker_id: {"audio": [], "text": ""} for speaker_id in test_speaker_ids} for split, dataset in zip(["validation", "test"], [validation_dataset_merged, test_dataset_merged]): sampling_rate = tedlium[split].features["audio"].sampling_rate for sample in tqdm(tedlium[split]): if sample["speaker_id"] in dataset: dataset[sample["speaker_id"]]["audio"].extend(sample["audio"]["array"]) dataset[sample["speaker_id"]]["text"] += " " + sample["text"] audio_paths = [] os.makedirs(split, exist_ok=True) for speaker in dataset: path = os.path.join(split, f"{speaker}-merged.wav") audio_paths.append(path) sf.write(path, np.asarray(dataset[speaker]["audio"]), samplerate=sampling_rate) merged_dataset[split] = Dataset.from_dict({"audio": audio_paths}).cast_column("audio", Audio()) # remove spaced apostrophes (e.g. it 's -> it's) merged_dataset[split] = merged_dataset[split].add_column("text", [dataset[speaker]["text"].replace(" '", "'") for speaker in dataset]) merged_dataset[split] = merged_dataset[split].add_column("speaker_id", dataset.keys()) ```
Partha117/apache_bugs_with_content
--- dataset_info: features: - name: issue_id dtype: int64 - name: title dtype: string - name: body dtype: string - name: status dtype: string - name: after_fix_sha dtype: string - name: project_name dtype: string - name: repo_url dtype: string - name: repo_name dtype: string - name: language dtype: string - name: issue_url dtype: 'null' - name: before_fix_sha dtype: 'null' - name: pull_url dtype: 'null' - name: commit_datetime dtype: timestamp[us, tz=UTC] - name: report_datetime dtype: timestamp[us, tz=UTC] - name: updated_file dtype: string - name: file_content dtype: string splits: - name: train num_bytes: 767059150 num_examples: 86060 download_size: 200457526 dataset_size: 767059150 configs: - config_name: default data_files: - split: train path: data/train-* ---
sayakpaul/pokemon-blip-original-version
--- license: cc-by-nc-sa-4.0 --- Dataset homepage: https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions The purpose of hosting the archive is to play with the original files. The archive was generated using [this Colab Notebook](https://colab.research.google.com/gist/sayakpaul/98f9ff3bd258a5c1107898422447b581/scratchpad.ipynb).
AgentWaller/dutch-oasst1-qlora-format
--- license: artistic-2.0 dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 11250127 num_examples: 9843 - name: validation num_bytes: 583463 num_examples: 517 download_size: 6602619 dataset_size: 11833590 ---
Mxode/Chinese-Classics-Partial
--- license: apache-2.0 task_categories: - text-generation language: - zh tags: - classics size_categories: - 100K<n<1M --- 偶然找到的 200 多篇古籍相关的**纯 txt 文件**，简单洗了一下，去除了部分噪声和空白行。 一篇样例如下： ``` 古训《增广贤文》 昔时贤文，诲汝谆谆，集韵增文，多见多闻。 观今宜鉴古，无古不成今。 知己知彼，将心比心。 酒逢知己饮，诗向会人吟。 相识满天下，知心能几人。 相逢好似初相识，到老终无怨恨心。 近水知鱼性，近山识鸟音。 易涨易退山溪水，易反易覆小人心。 运去金成铁，时来铁似金，读书须用意，一字值千金。 ```
CATIE-AQ/newsquadfr_fr_prompt_context_generation_with_answer
--- language: - fr license: cc-by-nc-sa-4.0 size_categories: - 100K<n<1M task_categories: - text-generation tags: - DFP - french prompts annotations_creators: - found language_creators: - found multilinguality: - monolingual source_datasets: - newsquadfr --- # newsquadfr_fr_prompt_context_generation_with_answer ## Summary **newsquadfr_fr_prompt_context_generation_with_answer** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP). It contains **101,040** rows that can be used for a context-generation (with answer)task. The original data (without prompts) comes from the dataset [newsquadfr](https://huggingface.co/datasets/lincoln/newsquadfr) and was augmented by questions in SQUAD 2.0 format in the [FrenchQA]( https://huggingface.co/datasets/CATIE-AQ/frenchQA) dataset. A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al. ## Prompts used ### List 24 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ``` 'Étant donné la réponse "'+ answer+'", écrire un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", écris un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", écrivez un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", rédiger un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", rédige un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", rédigez un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", générer un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", génère un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", générez un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", créer un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", crée un texte explicatif.\nTexte : ', 'Étant donné la réponse "'+ answer+'", créez un texte explicatif.\nTexte : ', 'Ecrire un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Ecris un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Ecrivez un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Rédiger un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Rédige un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Rédigez un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Générer un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Génère un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Générez un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Créer un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Crée un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', 'Créez un texte comme contexte de la réponse "'+ answer+'" \nTexte : ', ``` # Splits - `train` with 79,200 samples - `valid` with 21,800 samples - no `test` split # How to use? ``` from datasets import load_dataset dataset = load_dataset("CATIE-AQ/newsquadfr_fr_prompt_context_generation_with_answer") ``` # Citation ## Original data > Hugging Face repository: https://huggingface.co/datasets/lincoln/newsquadfr ## This Dataset > @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023, author = { {Centre Aquitain des Technologies de l'Information et Electroniques} }, title = { DFP (Revision 1d24c09) }, year = 2023, url = { https://huggingface.co/datasets/CATIE-AQ/DFP }, doi = { 10.57967/hf/1200 }, publisher = { Hugging Face } } ## License CC BY-NC-SA 4.0
bigbio/n2c2_2018_track1
--- language: - en bigbio_language: - English license: other multilinguality: monolingual bigbio_license_shortname: DUA pretty_name: n2c2 2018 Selection Criteria homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/ bigbio_pubmed: False bigbio_public: False bigbio_tasks: - TEXT_CLASSIFICATION --- # Dataset Card for n2c2 2018 Selection Criteria ## Dataset Description - **Homepage:** https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/ - **Pubmed:** False - **Public:** False - **Tasks:** TXTCLASS Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused on identifying which patients in a corpus of longitudinal medical records meet and do not meet identified selection criteria. This shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet a set of selection criteria taken from real clinical trials. The selected criteria required measurement detection ( “Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”), temporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major diabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical narratives from the 2014 i2b2/UTHealth shared task 4. The final selected 13 selection criteria are as follows: 1. DRUG-ABUSE: Drug abuse, current or past 2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits 3. ENGLISH: Patient must speak English 4. MAKES-DECISIONS: Patient must make their own medical decisions 5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine resection, or small bowel obstruction. 6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of this annotation, we define “major complication” (as opposed to “minor complication”) as any of the following that are a result of (or strongly correlated with) uncontrolled diabetes: a. Amputation b. Kidney damage c. Skin conditions d. Retinopathy e. nephropathy f. neuropathy 7. ADVANCED-CAD: Advanced cardiovascular disease (CAD). For the purposes of this annotation, we define “advanced” as having 2 or more of the following: a. Taking 2 or more medications to treat CAD b. History of myocardial infarction (MI) c. Currently experiencing angina d. Ischemia, past or present 8. MI-6MOS: MI in the past 6 months 9. KETO-1YR: Diagnosis of ketoacidosis in the past year 10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months 11. ASP-FOR-MI: Use of aspirin to prevent MI 12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5% 13. CREATININE: Serum creatinine > upper limit of normal The training consists of 202 patient records with document-level annotations, 10 records with textual spans indicating annotator’s evidence for their annotations while test set contains 86. Note: * The inter-annotator average agreement is 84.9% * Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown. However, author did a simple script based validation to check if any of the tags contained any text in any of the training set and they do not, which confirms that atleast train and test do not have any evidence tagged alongside corresponding tags. ## Citation Information ``` @article{DBLP:journals/jamia/StubbsFSHU19, author = { Amber Stubbs and Michele Filannino and Ergin Soysal and Samuel Henry and Ozlem Uzuner }, title = {Cohort selection for clinical trials: n2c2 2018 shared task track 1}, journal = {J. Am. Medical Informatics Assoc.}, volume = {26}, number = {11}, pages = {1163--1171}, year = {2019}, url = {https://doi.org/10.1093/jamia/ocz163}, doi = {10.1093/jamia/ocz163}, timestamp = {Mon, 15 Jun 2020 16:56:11 +0200}, biburl = {https://dblp.org/rec/journals/jamia/StubbsFSHU19.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
SkyWR/Wagner
--- license: openrail ---
gimmaru/SetFit-sst5
--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: label_text dtype: string splits: - name: test num_bytes: 128571 num_examples: 1000 download_size: 0 dataset_size: 128571 --- # Dataset Card for "SetFit-sst5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Note: This dataset was utilized for the evaluation of probability-based prompt selection techniques in the paper '[Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis](https://arxiv.org/abs/2305.14877)'. It differs from the actual benchmark dataset.
CerebralAI/ActionRoutes
--- dataset_info: features: - name: routes sequence: string - name: input dtype: string - name: label dtype: string splits: - name: train num_bytes: 1243126 num_examples: 5020 download_size: 474290 dataset_size: 1243126 configs: - config_name: default data_files: - split: train path: data/train-* ---
heliosprime/twitter_dataset_1713195079
--- 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: 17617 num_examples: 51 download_size: 17290 dataset_size: 17617 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713195079" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
allganize/fpb-ko-formatted
--- dataset_info: features: - name: question dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 162381 num_examples: 944 download_size: 88251 dataset_size: 162381 configs: - config_name: default data_files: - split: test path: data/test-* ---
open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ
--- pretty_name: Evaluation run of TheBloke/neural-chat-7B-v3-2-GPTQ dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/neural-chat-7B-v3-2-GPTQ](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ)\ \ 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_TheBloke__neural-chat-7B-v3-2-GPTQ\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T00:12:21.907526](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ/blob/main/results_2023-12-11T00-12-21.907526.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\": 0.6058481456466821,\n\ \ \"acc_stderr\": 0.03323160720607251,\n \"acc_norm\": 0.6077924426433228,\n\ \ \"acc_norm_stderr\": 0.033909992378155715,\n \"mc1\": 0.4541003671970624,\n\ \ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.5979099902582387,\n\ \ \"mc2_stderr\": 0.01509977856693472\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6296928327645052,\n \"acc_stderr\": 0.01411129875167495,\n\ \ \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892978\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6360286795459071,\n\ \ \"acc_stderr\": 0.004801572028920794,\n \"acc_norm\": 0.8324039036048596,\n\ \ \"acc_norm_stderr\": 0.003727438786513393\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\ \ \"acc_stderr\": 0.04304979692464241,\n \"acc_norm\": 0.5407407407407407,\n\ \ \"acc_norm_stderr\": 0.04304979692464241\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.02914690474779833,\n\ \ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.02914690474779833\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n\ \ \"acc_stderr\": 0.037738099906869334,\n \"acc_norm\": 0.7152777777777778,\n\ \ \"acc_norm_stderr\": 0.037738099906869334\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_computer_science|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-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n\ \ \"acc_stderr\": 0.03772446857518027,\n \"acc_norm\": 0.5722543352601156,\n\ \ \"acc_norm_stderr\": 0.03772446857518027\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.49019607843137253,\n \"acc_stderr\": 0.04974229460422817,\n\ \ \"acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.04974229460422817\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n\ \ \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\ \ \"acc_stderr\": 0.04657047260594964,\n \"acc_norm\": 0.4298245614035088,\n\ \ \"acc_norm_stderr\": 0.04657047260594964\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3544973544973545,\n \"acc_stderr\": 0.024636830602842,\n \"acc_norm\"\ : 0.3544973544973545,\n \"acc_norm_stderr\": 0.024636830602842\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.7419354838709677,\n\ \ \"acc_stderr\": 0.02489246917246283,\n \"acc_norm\": 0.7419354838709677,\n\ \ \"acc_norm_stderr\": 0.02489246917246283\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\ : 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n\ \ \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5948717948717949,\n \"acc_stderr\": 0.024890471769938145,\n\ \ \"acc_norm\": 0.5948717948717949,\n \"acc_norm_stderr\": 0.024890471769938145\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871923,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871923\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135356,\n\ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135356\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7963302752293578,\n \"acc_stderr\": 0.017266742087630783,\n \"\ acc_norm\": 0.7963302752293578,\n \"acc_norm_stderr\": 0.017266742087630783\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4861111111111111,\n \"acc_stderr\": 0.03408655867977749,\n \"\ acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.03408655867977749\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.02675082699467617,\n\ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.02675082699467617\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6564885496183206,\n \"acc_stderr\": 0.041649760719448786,\n\ \ \"acc_norm\": 0.6564885496183206,\n \"acc_norm_stderr\": 0.041649760719448786\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.04414343666854934,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.04414343666854934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6319018404907976,\n \"acc_stderr\": 0.03789213935838396,\n\ \ \"acc_norm\": 0.6319018404907976,\n \"acc_norm_stderr\": 0.03789213935838396\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\ \ \"acc_stderr\": 0.02514093595033544,\n \"acc_norm\": 0.8205128205128205,\n\ \ \"acc_norm_stderr\": 0.02514093595033544\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7841634738186463,\n\ \ \"acc_stderr\": 0.014711684386139953,\n \"acc_norm\": 0.7841634738186463,\n\ \ \"acc_norm_stderr\": 0.014711684386139953\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.02590663263101613,\n\ \ \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.02590663263101613\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39106145251396646,\n\ \ \"acc_stderr\": 0.016320763763808383,\n \"acc_norm\": 0.39106145251396646,\n\ \ \"acc_norm_stderr\": 0.016320763763808383\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n\ \ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\ \ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\ \ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6728395061728395,\n \"acc_stderr\": 0.02610567386140983,\n\ \ \"acc_norm\": 0.6728395061728395,\n \"acc_norm_stderr\": 0.02610567386140983\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.42907801418439717,\n \"acc_stderr\": 0.029525914302558555,\n \ \ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.029525914302558555\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4165580182529335,\n\ \ \"acc_stderr\": 0.012591153245057392,\n \"acc_norm\": 0.4165580182529335,\n\ \ \"acc_norm_stderr\": 0.012591153245057392\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5625,\n \"acc_stderr\": 0.030134614954403924,\n \ \ \"acc_norm\": 0.5625,\n \"acc_norm_stderr\": 0.030134614954403924\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6045751633986928,\n \"acc_stderr\": 0.01978046595477751,\n \ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.01978046595477751\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\ \ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.029162738410249772,\n\ \ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.029162738410249772\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n\ \ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n\ \ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.03218093795602357,\n\ \ \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.03218093795602357\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4541003671970624,\n\ \ \"mc1_stderr\": 0.017429593091323522,\n \"mc2\": 0.5979099902582387,\n\ \ \"mc2_stderr\": 0.01509977856693472\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7947908445146015,\n \"acc_stderr\": 0.01135031570746206\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5284306292645944,\n \ \ \"acc_stderr\": 0.013750202076584419\n }\n}\n```" repo_url: https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ 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_11T00_12_21.907526 path: - '**/details_harness|arc:challenge|25_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-11T00-12-21.907526.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|gsm8k|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hellaswag|10_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T00-12-21.907526.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T00-12-21.907526.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T00_12_21.907526 path: - '**/details_harness|winogrande|5_2023-12-11T00-12-21.907526.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-11T00-12-21.907526.parquet' - config_name: results data_files: - split: 2023_12_11T00_12_21.907526 path: - results_2023-12-11T00-12-21.907526.parquet - split: latest path: - results_2023-12-11T00-12-21.907526.parquet --- # Dataset Card for Evaluation run of TheBloke/neural-chat-7B-v3-2-GPTQ ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TheBloke/neural-chat-7B-v3-2-GPTQ](https://huggingface.co/TheBloke/neural-chat-7B-v3-2-GPTQ) 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_TheBloke__neural-chat-7B-v3-2-GPTQ", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T00:12:21.907526](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__neural-chat-7B-v3-2-GPTQ/blob/main/results_2023-12-11T00-12-21.907526.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": 0.6058481456466821, "acc_stderr": 0.03323160720607251, "acc_norm": 0.6077924426433228, "acc_norm_stderr": 0.033909992378155715, "mc1": 0.4541003671970624, "mc1_stderr": 0.017429593091323522, "mc2": 0.5979099902582387, "mc2_stderr": 0.01509977856693472 }, "harness|arc:challenge|25": { "acc": 0.6296928327645052, "acc_stderr": 0.01411129875167495, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.013847460518892978 }, "harness|hellaswag|10": { "acc": 0.6360286795459071, "acc_stderr": 0.004801572028920794, "acc_norm": 0.8324039036048596, "acc_norm_stderr": 0.003727438786513393 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5407407407407407, "acc_stderr": 0.04304979692464241, "acc_norm": 0.5407407407407407, "acc_norm_stderr": 0.04304979692464241 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.03823428969926605, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.03823428969926605 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.660377358490566, "acc_stderr": 0.02914690474779833, "acc_norm": 0.660377358490566, "acc_norm_stderr": 0.02914690474779833 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7152777777777778, "acc_stderr": 0.037738099906869334, "acc_norm": 0.7152777777777778, "acc_norm_stderr": 0.037738099906869334 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5722543352601156, "acc_stderr": 0.03772446857518027, "acc_norm": 0.5722543352601156, "acc_norm_stderr": 0.03772446857518027 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.49019607843137253, "acc_stderr": 0.04974229460422817, "acc_norm": 0.49019607843137253, "acc_norm_stderr": 0.04974229460422817 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5148936170212766, "acc_stderr": 0.03267151848924777, "acc_norm": 0.5148936170212766, "acc_norm_stderr": 0.03267151848924777 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4298245614035088, "acc_stderr": 0.04657047260594964, "acc_norm": 0.4298245614035088, "acc_norm_stderr": 0.04657047260594964 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3544973544973545, "acc_stderr": 0.024636830602842, "acc_norm": 0.3544973544973545, "acc_norm_stderr": 0.024636830602842 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7419354838709677, "acc_stderr": 0.02489246917246283, "acc_norm": 0.7419354838709677, "acc_norm_stderr": 0.02489246917246283 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175007, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175007 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7515151515151515, "acc_stderr": 0.033744026441394036, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.033744026441394036 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7626262626262627, "acc_stderr": 0.0303137105381989, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.0303137105381989 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8341968911917098, "acc_stderr": 0.026839845022314415, "acc_norm": 0.8341968911917098, "acc_norm_stderr": 0.026839845022314415 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5948717948717949, "acc_stderr": 0.024890471769938145, "acc_norm": 0.5948717948717949, "acc_norm_stderr": 0.024890471769938145 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.027840811495871923, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.027840811495871923 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.030684737115135356, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.030684737115135356 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7963302752293578, "acc_stderr": 0.017266742087630783, "acc_norm": 0.7963302752293578, "acc_norm_stderr": 0.017266742087630783 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4861111111111111, "acc_stderr": 0.03408655867977749, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.03408655867977749 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.75, "acc_stderr": 0.03039153369274154, "acc_norm": 0.75, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.02675082699467617, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.02675082699467617 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6564885496183206, "acc_stderr": 0.041649760719448786, "acc_norm": 0.6564885496183206, "acc_norm_stderr": 0.041649760719448786 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7037037037037037, "acc_stderr": 0.04414343666854934, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.04414343666854934 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6319018404907976, "acc_stderr": 0.03789213935838396, "acc_norm": 0.6319018404907976, "acc_norm_stderr": 0.03789213935838396 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8205128205128205, "acc_stderr": 0.02514093595033544, "acc_norm": 0.8205128205128205, "acc_norm_stderr": 0.02514093595033544 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7841634738186463, "acc_stderr": 0.014711684386139953, "acc_norm": 0.7841634738186463, "acc_norm_stderr": 0.014711684386139953 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6358381502890174, "acc_stderr": 0.02590663263101613, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.02590663263101613 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.39106145251396646, "acc_stderr": 0.016320763763808383, "acc_norm": 0.39106145251396646, "acc_norm_stderr": 0.016320763763808383 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6830065359477124, "acc_stderr": 0.026643278474508755, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.026643278474508755 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6881028938906752, "acc_stderr": 0.026311858071854155, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.026311858071854155 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6728395061728395, "acc_stderr": 0.02610567386140983, "acc_norm": 0.6728395061728395, "acc_norm_stderr": 0.02610567386140983 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.42907801418439717, "acc_stderr": 0.029525914302558555, "acc_norm": 0.42907801418439717, "acc_norm_stderr": 0.029525914302558555 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4165580182529335, "acc_stderr": 0.012591153245057392, "acc_norm": 0.4165580182529335, "acc_norm_stderr": 0.012591153245057392 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5625, "acc_stderr": 0.030134614954403924, "acc_norm": 0.5625, "acc_norm_stderr": 0.030134614954403924 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6045751633986928, "acc_stderr": 0.01978046595477751, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.01978046595477751 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6181818181818182, "acc_stderr": 0.046534298079135075, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.046534298079135075 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7061224489795919, "acc_stderr": 0.029162738410249772, "acc_norm": 0.7061224489795919, "acc_norm_stderr": 0.029162738410249772 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7860696517412935, "acc_stderr": 0.02899690969332891, "acc_norm": 0.7860696517412935, "acc_norm_stderr": 0.02899690969332891 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7719298245614035, "acc_stderr": 0.03218093795602357, "acc_norm": 0.7719298245614035, "acc_norm_stderr": 0.03218093795602357 }, "harness|truthfulqa:mc|0": { "mc1": 0.4541003671970624, "mc1_stderr": 0.017429593091323522, "mc2": 0.5979099902582387, "mc2_stderr": 0.01509977856693472 }, "harness|winogrande|5": { "acc": 0.7947908445146015, "acc_stderr": 0.01135031570746206 }, "harness|gsm8k|5": { "acc": 0.5284306292645944, "acc_stderr": 0.013750202076584419 } } ``` ### 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]
joey234/mmlu-college_physics-neg
--- dataset_info: features: - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: question dtype: string splits: - name: test num_bytes: 27795 num_examples: 102 download_size: 16560 dataset_size: 27795 --- # Dataset Card for "mmlu-college_physics-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
UMCU/MedQA_Dutch_translated_with_MariaNMT
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 8270752 num_examples: 9856 download_size: 4467728 dataset_size: 8270752 --- # Dataset Card for "MedQA_Dutch_translated_with_MariaNMT" Translation of the **English** version of [MedQA](https://huggingface.co/datasets/bigbio/med_qa), to **Dutch** using an [Maria NMT model](https://marian-nmt.github.io/), trained by [Helsinki NLP](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl). Note, for reference: Maria NMT is based on [BART](https://huggingface.co/docs/transformers/model_doc/bart), described [here](https://arxiv.org/abs/1910.13461). Note: We do **not** have the full sample count of the original MedQA due to exceedance of the maximum window size. In updated version we will use stride to translate complete documents. # Attribution If you use this dataset please use the following to credit the creators of MedQA: ```citation @article{jin2021disease, title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={Applied Sciences}, volume={11}, number={14}, pages={6421}, year={2021}, publisher={MDPI} } ``` The creators of the OPUS-MT models: ``` @InProceedings{TiedemannThottingal:EAMT2020, author = {J{\"o}rg Tiedemann and Santhosh Thottingal}, title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld}, booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)}, year = {2020}, address = {Lisbon, Portugal} } ``` and ``` @misc {van_es_2023, author = { {Bram van Es} }, title = { MedQA_Dutch_translated_with_MariaNMT (Revision 7e88c9e) }, year = 2023, url = { https://huggingface.co/datasets/UMCU/MedQA_Dutch_translated_with_MariaNMT }, doi = { 10.57967/hf/1355 }, publisher = { Hugging Face } } ``` # License For both the Maria NMT model and the original [Helsinki NLP](https://twitter.com/HelsinkiNLP) [Opus MT model](https://huggingface.co/Helsinki-NLP) we did **not** find a license. We also did not find a license for the MedQA corpus. For these reasons we use a permissive [CC BY](https://wellcome.org/grant-funding/guidance/open-access-guidance/creative-commons-attribution-licence-cc) license. If this was in error please let us know and we will add the appropriate licensing promptly.
supersaiyan2019/main6
--- license: openrail ---
ibranze/araproje_hellaswag_en_conf_llama_nearestscore_true_y
--- 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: 149738.0 num_examples: 250 download_size: 81116 dataset_size: 149738.0 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "araproje_hellaswag_en_conf_llama_nearestscore_true_y" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thorirhrafn/rmh_subset_medium2
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 775259169 num_examples: 282160 - name: test num_bytes: 4398683 num_examples: 2000 - name: valid num_bytes: 4543850 num_examples: 2000 download_size: 480237633 dataset_size: 784201702 --- # Dataset Card for "rmh_subset_medium2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906071
--- type: predictions tags: - autotrain - evaluation datasets: - squad_v2 eval_info: task: extractive_question_answering model: deepset/roberta-base-squad2-distilled metrics: ['bertscore'] dataset_name: squad_v2 dataset_config: squad_v2 dataset_split: validation col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: deepset/roberta-base-squad2-distilled * Dataset: squad_v2 * Config: squad_v2 * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model.
arize-ai/fashion_mnist_label_drift
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - extended|imdb task_categories: - image-classification task_ids: - multi-class-classification pretty_name: sentiment-classification-reviews-with-drift --- # Dataset Card for `reviews_with_drift` ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description ### Dataset Summary This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place. ### Supported Tasks and Leaderboards `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative). ### Languages Text is mainly written in english. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.
trolllemon/dogs
--- language: - en license: mit task_categories: - image-classification pretty_name: Dogs dataset_info: features: - name: image dtype: string - name: label dtype: string splits: - name: train num_bytes: 4610 num_examples: 60 - name: test num_bytes: 1064 num_examples: 14 download_size: 3572 dataset_size: 5674 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
joey234/mmlu-computer_security-original-neg-prepend
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_prompt dtype: string splits: - name: test num_bytes: 10384 num_examples: 17 download_size: 9420 dataset_size: 10384 --- # Dataset Card for "mmlu-computer_security-original-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
suolyer/pile_europarl
--- license: apache-2.0 ---
janani4office2/connl_rlhf
--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: text dtype: string - name: entities dtype: string splits: - name: train num_bytes: 1818001 num_examples: 14041 - name: validation num_bytes: 450958 num_examples: 3250 - name: test num_bytes: 419551 num_examples: 3453 download_size: 1557421 dataset_size: 2688510 ---
bridgeconn/snow-mountain
--- pretty_name: Snow Mountain language: - hi - bgc - kfs - dgo - bhd - gbk - xnr - kfx - mjl - kfo - bfz annotations_creators: - 'null': null language_creators: - 'null': null multilinguality: - multilingual source_datasets: - Snow Mountain task_categories: - automatic-speech-recognition - text-to-speech task_ids: [] configs: - hi - bgc dataset_info: - config_name: hi features: - name: Unnamed dtype: int64 - name: sentence dtype: string - name: path dtype: string splits: - name: train_500 num_examples: 400 - name: val_500 num_examples: 100 - name: train_1000 num_examples: 800 - name: val_1000 num_examples: 200 - name: test_common num_examples: 500 dataset_size: 71.41 hrs - config_name: bgc features: - name: Unnamed dtype: int64 - name: sentence dtype: string - name: path dtype: string splits: - name: train_500 num_examples: 400 - name: val_500 num_examples: 100 - name: train_1000 num_examples: 800 - name: val_1000 num_examples: 200 - name: test_common num_examples: 500 dataset_size: 27.41 hrs license: cc-by-sa-4.0 --- # Snow Mountain ## Dataset Description - **Paper: https://arxiv.org/abs/2206.01205** - **Point of Contact: Joel Mathew** ### Dataset Summary The Snow Mountain dataset contains the audio recordings (in .mp3 format) and the corresponding text of The Bible (contains both Old Testament (OT) and New Testament (NT)) in 11 Indian languages. The recordings were done in a studio setting by native speakers. Each language has a single speaker in the dataset. Most of these languages are geographically concentrated in the Northern part of India around the state of Himachal Pradesh. Being related to Hindi they all use the Devanagari script for transcription. We have used this dataset for experiments in ASR tasks. But these could be used for other applications in speech domain, like speaker recognition, language identification or even as unlabelled corpus for pre-training. ### Supported Tasks and Leaderboards Atomatic speech recognition, Speech-to-Text, Speaker recognition, Language identification ### Languages Hindi, Haryanvi, Bilaspuri, Dogri, Bhadrawahi, Gaddi, Kangri, Kulvi, Mandeali, Kulvi Outer Seraji, Pahari Mahasui, Malayalam, Kannada, Tamil, Telugu ## Dataset Structure ``` data |- cleaned |- lang1 |- book1_verse_audios.tar.gz |- book2_verse_audios.tar.gz ... ... |- all_verses.tar.gz |- short_verses.tar.gz |- lang2 ... ... |- experiments |- lang1 |- train_500.csv |- val_500.csv |- test_common.csv ... ... |- lang2 ... ... |- raw |- lang1 |- chapter1_audio.mp3 |- chapter2_audio.mp3 ... ... |- text |- book1.csv |- book1.usfm ... ... |- lang2 ... ... ``` ### Data Instances A data point comprises of the path to the audio file, called `path` and its transcription, called `sentence`. ``` {'sentence': 'क्यूँके तू अपणी बात्तां कै कारण बेकसूर अर अपणी बात्तां ए कै कारण कसूरवार ठहराया जावैगा', 'audio': {'path': 'data/cleaned/haryanvi/MAT/MAT_012_037.wav', 'array': array([0., 0., 0., ..., 0., 0., 0.]), 'sampling_rate': 16000}, 'path': 'data/cleaned/haryanvi/MAT/MAT_012_037.wav'} ``` ### Data Fields `path`: The path to the audio file `audio`: 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]`. `sentence`: The transcription of the audio file. ### Data Splits We create splits of the cleaned data for training and analysing the performance of ASR models. The splits are available in the `experiments` directory. The file names indicate the experiment and the split category. Additionally two CSV files are included in the data splits - `all_verses` and `short_verses`. Various data splits were generated from these main two CSVs. `short_verses.csv` contains audios of length < 10s and corresponding transcriptions. `all_verses.csv` contains complete cleaned verses including long and short audios. Due to the large size (>10MB), we keep these CSVs compressed in the `tar.gz format in the `cleaned` folder. ## Dataset Loading `raw` folder has chapter wise audios in .mp3 format. For doing experiments, we might need audios in .wav format. Verse wise audio files are keept in the `cleaned` folder in .wav format. This results in a much larger size which contributes to longer loading time into memory. Here is the approximate time needed for loading the Dataset. - Hindi (OT books): ~20 minutes - Hindi minority languages (NT books): ~9 minutes - Dravidian languages (OT+NT books): ~30 minutes ## Details Please refer to the paper for more details on the creation and the rationale for the splits we created in the dataset. ### Licensing Information The data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) ### Citation Information Please cite this work if you make use of it: ``` @inproceedings{Raju2022SnowMD, title={Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages}, author={Kavitha Raju and V. Anjaly and R. Allen Lish and Joel Mathew}, year={2022} } ```
dylanalloy/fin-gpt-selftalk_500k
--- license: cc-by-nc-4.0 ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/e8fbcee9
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1336 dataset_size: 188 --- # Dataset Card for "e8fbcee9" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HeshamHaroon/arabic-quotes
--- annotations_creators: - expert-generated language_creators: - expert-generated - crowdsourced language: - ar multilinguality: - monolingual source_datasets: - original task_categories: - text-classification task_ids: - multi-label-classification --- # Arabic Quotes Dataset (arabic_Q) The "Arabic Quotes" dataset contains a collection of Arabic quotes along with their corresponding authors and tags. The dataset is scraped from the website "arabic-quotes.com" and provides a diverse range of quotes from various authors. ## Dataset Details - **Version**: 1.0.0 - **Total Quotes**: 3778 - **Languages**: Arabic - **Source**: arabic-quotes.com ## Dataset Structure The dataset is provided in the JSONL (JSON Lines) format, where each line represents a separate JSON object. The JSON objects have the following fields: - `quote`: The Arabic quote text. - `author`: The author of the quote. - `tags`: A list of tags associated with the quote, providing additional context or themes. ## Dataset Examples Here are a few examples of the quotes in the dataset: ```json { "quote": "اذا لم يكن لديك هدف ، فاجعل هدفك الاول ايجاد واحد .", "author": "وليام شكسبير", "tags": ["تنمية الذات", "تحفيز"] } { "quote": "قيمة الحياة ليست في مدى طولها ، بل في مدى قيمتها", "author": "وليام شكسبير", "tags": ["الحياة", "القيمة"] } { "quote": "التحدث عن الامور العميقة ليس سهلاً كما يبدو", "author": "جبران خليل جبران", "tags": ["التواصل", "العمق"] } ``` ## Dataset Usage The "Arabic Quotes" dataset can be used for various purposes, including: - Natural Language Processing (NLP) tasks in Arabic text analysis. - Text generation and language modeling. - Quote recommendation systems. - Inspirational content generation. - text-classification ## Acknowledgements We would like to thank the website "arabic-quotes.com" for providing the valuable collection of Arabic quotes used in this dataset. ## License The dataset is provided under the [bigscience-bloom-rail-1.0 License](https://huggingface.co/spaces/bigscience/license), which permits non-commercial use and sharing under certain conditions.
FastFit/massive_de_60
--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 704100 num_examples: 11514 - name: validation num_bytes: 123376 num_examples: 2033 - name: test num_bytes: 181452 num_examples: 2974 download_size: 428903 dataset_size: 1008928 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
autoevaluate/autoeval-eval-piaf-plain_text-42b979-39890145062
--- type: predictions tags: - autotrain - evaluation datasets: - piaf eval_info: task: extractive_question_answering model: etalab-ia/camembert-base-squadFR-fquad-piaf metrics: ['accuracy'] dataset_name: piaf dataset_config: plain_text dataset_split: train col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: etalab-ia/camembert-base-squadFR-fquad-piaf * Dataset: piaf * Config: plain_text * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@malou.berthe@gmail.com](https://huggingface.co/malou.berthe@gmail.com) for evaluating this model.
Tensoic/saraswati-stem
--- license: openrail ---
CyberHarem/kuwayama_chiyuki_theidolmstershinycolors
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kuwayama_chiyuki/桑山千雪 (THE iDOLM@STER: SHINY COLORS) This is the dataset of kuwayama_chiyuki/桑山千雪 (THE iDOLM@STER: SHINY COLORS), containing 500 images and their tags. The core tags of this character are `brown_hair, long_hair, breasts, bangs, brown_eyes, ahoge, large_breasts, braid, single_braid`, 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 | 853.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuwayama_chiyuki_theidolmstershinycolors/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 422.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuwayama_chiyuki_theidolmstershinycolors/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1288 | 962.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuwayama_chiyuki_theidolmstershinycolors/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 722.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kuwayama_chiyuki_theidolmstershinycolors/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1288 | 1.45 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kuwayama_chiyuki_theidolmstershinycolors/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/kuwayama_chiyuki_theidolmstershinycolors', 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 | 20 | ![](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, cheerleader, holding_pom_poms, ponytail, solo, blush, crop_top, looking_at_viewer, midriff, miniskirt, navel, pleated_skirt, open_mouth, cleavage, bike_shorts_under_skirt, collarbone, short_sleeves, white_skirt, simple_background, sweat, white_footwear, yellow_belt, :d, black_choker, blue_shirt, boots, confetti, ribbon, white_background | | 1 | 5 | ![](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, blush, bracelet, cleavage, collarbone, looking_at_viewer, navel, smile, solo, thighs, bare_shoulders, hair_ornament, sitting, crown_braid, earrings, ponytail, white_bikini, cup, drinking_straw, halterneck, holding, open_mouth, outdoors, white_background | | 2 | 10 | ![](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) | 1girl, blush, cleavage, floral_print, looking_at_viewer, solo, necklace, pink_one-piece_swimsuit, smile, earrings, armpits, arms_up, casual_one-piece_swimsuit, collarbone, thighs, cowboy_shot, hairclip, open_mouth, wet | | 3 | 6 | ![](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, blush, looking_at_viewer, smile, solo, white_background, simple_background, skirt, sleeveless_shirt, bare_shoulders, black_shirt | | 4 | 23 | ![](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, short_sleeves, white_shirt, blush, hair_over_shoulder, solo, hair_bow, smile, black_bow, braided_ponytail, looking_at_viewer, long_braid, white_background, open_mouth, simple_background, collared_shirt, red_skirt | | 5 | 7 | ![](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, bare_shoulders, frills, looking_at_viewer, solo, blush, braided_ponytail, white_background, simple_background, white_dress, wrist_cuffs, hairband, long_braid, medium_breasts, open_mouth, :d, hair_bow, hair_ribbon, halterneck, holding, microphone, short_sleeves, thighhighs | | 6 | 19 | ![](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) | 1boy, 1girl, blush, hetero, solo_focus, nipples, paizuri, smile, sweat, collarbone, nude, breasts_squeezed_together, looking_at_viewer, open_mouth, penis, huge_breasts, braided_ponytail, pov, censored, hair_over_shoulder, long_braid, breast_grab | | 7 | 12 | ![](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) | 1boy, 1girl, blush, hetero, sex, collarbone, nipples, vaginal, completely_nude, navel, solo_focus, cowgirl_position, girl_on_top, spread_legs, sweat, looking_at_viewer, open_mouth, penis, pussy, female_pubic_hair, thighs, mosaic_censoring, smile | | 8 | 7 | ![](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, hair_flower, solo, wedding_dress, white_dress, bare_shoulders, blush, bridal_veil, detached_sleeves, looking_at_viewer, white_gloves, bride, earrings, holding_bouquet, see-through_sleeves, sleeveless_dress, petals, smile, blurry | | 9 | 11 | ![](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, bare_shoulders, elbow_gloves, looking_at_viewer, sleeveless_shirt, black_gloves, solo, blush, cleavage, peaked_cap, white_shirt, black_necktie, hair_over_shoulder, long_braid, smile, collarbone, necktie_between_breasts, collared_shirt, earrings, holding, black_headwear, riding_crop, skirt, shorts, sidelocks, white_background | | 10 | 8 | ![](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, blush, looking_at_viewer, solo, cleavage, navel, underwear_only, bow, collarbone, sweat, thighs, armpits, black_bra, black_panties, braided_ponytail, hair_over_shoulder, indoors, lingerie, smile, arms_up, lace-trimmed_bra, on_back, parted_lips, stomach | | 11 | 6 | ![](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, looking_at_viewer, solo, obi, wide_sleeves, blush, hair_ribbon, leaf, autumn_leaves, holding, light_smile, outdoors, shawl, single_hair_bun, striped_kimono, upper_body | | 12 | 6 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | 1girl, long_sleeves, solo, blush, looking_at_viewer, maid_apron, maid_headdress, twin_braids, bow, enmaided, glasses, hair_over_shoulder, smile, white_apron, black_dress, closed_mouth, frills, indoors, round_eyewear | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cheerleader | holding_pom_poms | ponytail | solo | blush | crop_top | looking_at_viewer | midriff | miniskirt | navel | pleated_skirt | open_mouth | cleavage | bike_shorts_under_skirt | collarbone | short_sleeves | white_skirt | simple_background | sweat | white_footwear | yellow_belt | :d | black_choker | blue_shirt | boots | confetti | ribbon | white_background | bracelet | smile | thighs | bare_shoulders | hair_ornament | sitting | crown_braid | earrings | white_bikini | cup | drinking_straw | halterneck | holding | outdoors | floral_print | necklace | pink_one-piece_swimsuit | armpits | arms_up | casual_one-piece_swimsuit | cowboy_shot | hairclip | wet | skirt | sleeveless_shirt | black_shirt | white_shirt | hair_over_shoulder | hair_bow | black_bow | braided_ponytail | long_braid | collared_shirt | red_skirt | frills | white_dress | wrist_cuffs | hairband | medium_breasts | hair_ribbon | microphone | thighhighs | 1boy | hetero | solo_focus | nipples | paizuri | nude | breasts_squeezed_together | penis | huge_breasts | pov | censored | breast_grab | sex | vaginal | completely_nude | cowgirl_position | girl_on_top | spread_legs | pussy | female_pubic_hair | mosaic_censoring | hair_flower | wedding_dress | bridal_veil | detached_sleeves | white_gloves | bride | holding_bouquet | see-through_sleeves | sleeveless_dress | petals | blurry | elbow_gloves | black_gloves | peaked_cap | black_necktie | necktie_between_breasts | black_headwear | riding_crop | shorts | sidelocks | underwear_only | bow | black_bra | black_panties | indoors | lingerie | lace-trimmed_bra | on_back | parted_lips | stomach | obi | wide_sleeves | leaf | autumn_leaves | light_smile | shawl | single_hair_bun | striped_kimono | upper_body | long_sleeves | maid_apron | maid_headdress | twin_braids | enmaided | glasses | white_apron | black_dress | closed_mouth | round_eyewear | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------|:-------------------|:-----------|:-------|:--------|:-----------|:--------------------|:----------|:------------|:--------|:----------------|:-------------|:-----------|:--------------------------|:-------------|:----------------|:--------------|:--------------------|:--------|:-----------------|:--------------|:-----|:---------------|:-------------|:--------|:-----------|:---------|:-------------------|:-----------|:--------|:---------|:-----------------|:----------------|:----------|:--------------|:-----------|:---------------|:------|:-----------------|:-------------|:----------|:-----------|:---------------|:-----------|:--------------------------|:----------|:----------|:----------------------------|:--------------|:-----------|:------|:--------|:-------------------|:--------------|:--------------|:---------------------|:-----------|:------------|:-------------------|:-------------|:-----------------|:------------|:---------|:--------------|:--------------|:-----------|:-----------------|:--------------|:-------------|:-------------|:-------|:---------|:-------------|:----------|:----------|:-------|:----------------------------|:--------|:---------------|:------|:-----------|:--------------|:------|:----------|:------------------|:-------------------|:--------------|:--------------|:--------|:--------------------|:-------------------|:--------------|:----------------|:--------------|:-------------------|:---------------|:--------|:------------------|:----------------------|:-------------------|:---------|:---------|:---------------|:---------------|:-------------|:----------------|:--------------------------|:-----------------|:--------------|:---------|:------------|:-----------------|:------|:------------|:----------------|:----------|:-----------|:-------------------|:----------|:--------------|:----------|:------|:---------------|:-------|:----------------|:--------------|:--------|:------------------|:-----------------|:-------------|:---------------|:-------------|:-----------------|:--------------|:-----------|:----------|:--------------|:--------------|:---------------|:----------------| | 0 | 20 | ![](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 | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 10 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 23 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 19 | ![](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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 7 | ![](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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 11 | ![](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 | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 8 | ![](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 | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 11 | 6 | ![](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 | | | | | | | | | | | | 12 | 6 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | X | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
jinaai/miracl
--- license: apache-2.0 --- ## MIRACL Dataset This dataset is a reformatted version of the original [MIRACL dataset](https://huggingface.co/datasets/miracl/miracl), into the format expected for MTEB reranking tasks.
Heba30018/test
--- dataset_info: features: - name: image dtype: string - name: label dtype: string splits: - name: train num_bytes: 271721.0 num_examples: 6469 download_size: 89923 dataset_size: 271721.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_allknowingroger__Limmy-phi2-slerp
--- pretty_name: Evaluation run of allknowingroger/Limmy-phi2-slerp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [allknowingroger/Limmy-phi2-slerp](https://huggingface.co/allknowingroger/Limmy-phi2-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_allknowingroger__Limmy-phi2-slerp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-11T04:46:02.169522](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Limmy-phi2-slerp/blob/main/results_2024-04-11T04-46-02.169522.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\": 0.5821651772411324,\n\ \ \"acc_stderr\": 0.03372798315458136,\n \"acc_norm\": 0.5823208166209095,\n\ \ \"acc_norm_stderr\": 0.03442419149083994,\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.016750862381375898,\n \"mc2\": 0.5059941161139769,\n\ \ \"mc2_stderr\": 0.015447236581056423\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.591296928327645,\n \"acc_stderr\": 0.014365750345427,\n\ \ \"acc_norm\": 0.621160409556314,\n \"acc_norm_stderr\": 0.014175915490000324\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.579964150567616,\n\ \ \"acc_stderr\": 0.004925556104679422,\n \"acc_norm\": 0.7635929097789285,\n\ \ \"acc_norm_stderr\": 0.0042400668987025185\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n\ \ \"acc_stderr\": 0.04313531696750575,\n \"acc_norm\": 0.4740740740740741,\n\ \ \"acc_norm_stderr\": 0.04313531696750575\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6052631578947368,\n \"acc_stderr\": 0.039777499346220734,\n\ \ \"acc_norm\": 0.6052631578947368,\n \"acc_norm_stderr\": 0.039777499346220734\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03015113445777629,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03015113445777629\n },\n\ \ \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6388888888888888,\n\ \ \"acc_stderr\": 0.040166600304512336,\n \"acc_norm\": 0.6388888888888888,\n\ \ \"acc_norm_stderr\": 0.040166600304512336\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\": 0.47,\n\ \ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\ \ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\ \ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663434,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663434\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4808510638297872,\n \"acc_stderr\": 0.032662042990646775,\n\ \ \"acc_norm\": 0.4808510638297872,\n \"acc_norm_stderr\": 0.032662042990646775\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\ \ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.43915343915343913,\n \"acc_stderr\": 0.025559920550531006,\n \"\ acc_norm\": 0.43915343915343913,\n \"acc_norm_stderr\": 0.025559920550531006\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6741935483870968,\n\ \ \"acc_stderr\": 0.026662010578567104,\n \"acc_norm\": 0.6741935483870968,\n\ \ \"acc_norm_stderr\": 0.026662010578567104\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\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\ : 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n\ \ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"\ acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7927461139896373,\n \"acc_stderr\": 0.029252823291803627,\n\ \ \"acc_norm\": 0.7927461139896373,\n \"acc_norm_stderr\": 0.029252823291803627\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5743589743589743,\n \"acc_stderr\": 0.025069094387296535,\n\ \ \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.025069094387296535\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.31851851851851853,\n \"acc_stderr\": 0.028406533090608466,\n \ \ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.028406533090608466\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\ \ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.818348623853211,\n \"acc_stderr\": 0.016530617409266868,\n \"\ acc_norm\": 0.818348623853211,\n \"acc_norm_stderr\": 0.016530617409266868\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4583333333333333,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6617647058823529,\n \"acc_stderr\": 0.03320574612945432,\n \"\ acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.03320574612945432\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7468354430379747,\n \"acc_stderr\": 0.028304657943035282,\n \ \ \"acc_norm\": 0.7468354430379747,\n \"acc_norm_stderr\": 0.028304657943035282\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6367713004484304,\n\ \ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\ \ \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834278,\n\ \ \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\"\ : 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615768,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615768\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\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260594,\n\ \ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260594\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8247863247863247,\n\ \ \"acc_stderr\": 0.02490443909891824,\n \"acc_norm\": 0.8247863247863247,\n\ \ \"acc_norm_stderr\": 0.02490443909891824\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939098,\n \ \ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939098\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6998722860791826,\n\ \ \"acc_stderr\": 0.016389249691317432,\n \"acc_norm\": 0.6998722860791826,\n\ \ \"acc_norm_stderr\": 0.016389249691317432\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.684971098265896,\n \"acc_stderr\": 0.025009313790069716,\n\ \ \"acc_norm\": 0.684971098265896,\n \"acc_norm_stderr\": 0.025009313790069716\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.26033519553072626,\n\ \ \"acc_stderr\": 0.014676252009319473,\n \"acc_norm\": 0.26033519553072626,\n\ \ \"acc_norm_stderr\": 0.014676252009319473\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.02799672318063145,\n\ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.02799672318063145\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6495176848874598,\n\ \ \"acc_stderr\": 0.027098652621301754,\n \"acc_norm\": 0.6495176848874598,\n\ \ \"acc_norm_stderr\": 0.027098652621301754\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.026725868809100793,\n\ \ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.026725868809100793\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4219858156028369,\n \"acc_stderr\": 0.0294621892333706,\n \ \ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.0294621892333706\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4172099087353325,\n\ \ \"acc_stderr\": 0.012593959992906417,\n \"acc_norm\": 0.4172099087353325,\n\ \ \"acc_norm_stderr\": 0.012593959992906417\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n\ \ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5392156862745098,\n \"acc_stderr\": 0.020165523313907904,\n \ \ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.020165523313907904\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\ \ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\ \ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.029162738410249765,\n\ \ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.029162738410249765\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7512437810945274,\n\ \ \"acc_stderr\": 0.030567675938916714,\n \"acc_norm\": 0.7512437810945274,\n\ \ \"acc_norm_stderr\": 0.030567675938916714\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\ \ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.463855421686747,\n\ \ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7017543859649122,\n \"acc_stderr\": 0.03508771929824563,\n\ \ \"acc_norm\": 0.7017543859649122,\n \"acc_norm_stderr\": 0.03508771929824563\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35495716034271724,\n\ \ \"mc1_stderr\": 0.016750862381375898,\n \"mc2\": 0.5059941161139769,\n\ \ \"mc2_stderr\": 0.015447236581056423\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7545382794001578,\n \"acc_stderr\": 0.012095272937183644\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6141015921152388,\n \ \ \"acc_stderr\": 0.013409077471319164\n }\n}\n```" repo_url: https://huggingface.co/allknowingroger/Limmy-phi2-slerp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|arc:challenge|25_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-11T04-46-02.169522.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|gsm8k|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hellaswag|10_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-46-02.169522.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-management|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T04-46-02.169522.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|truthfulqa:mc|0_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-11T04-46-02.169522.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_11T04_46_02.169522 path: - '**/details_harness|winogrande|5_2024-04-11T04-46-02.169522.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-11T04-46-02.169522.parquet' - config_name: results data_files: - split: 2024_04_11T04_46_02.169522 path: - results_2024-04-11T04-46-02.169522.parquet - split: latest path: - results_2024-04-11T04-46-02.169522.parquet --- # Dataset Card for Evaluation run of allknowingroger/Limmy-phi2-slerp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [allknowingroger/Limmy-phi2-slerp](https://huggingface.co/allknowingroger/Limmy-phi2-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_allknowingroger__Limmy-phi2-slerp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-11T04:46:02.169522](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__Limmy-phi2-slerp/blob/main/results_2024-04-11T04-46-02.169522.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": 0.5821651772411324, "acc_stderr": 0.03372798315458136, "acc_norm": 0.5823208166209095, "acc_norm_stderr": 0.03442419149083994, "mc1": 0.35495716034271724, "mc1_stderr": 0.016750862381375898, "mc2": 0.5059941161139769, "mc2_stderr": 0.015447236581056423 }, "harness|arc:challenge|25": { "acc": 0.591296928327645, "acc_stderr": 0.014365750345427, "acc_norm": 0.621160409556314, "acc_norm_stderr": 0.014175915490000324 }, "harness|hellaswag|10": { "acc": 0.579964150567616, "acc_stderr": 0.004925556104679422, "acc_norm": 0.7635929097789285, "acc_norm_stderr": 0.0042400668987025185 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.04605661864718381, "acc_norm": 0.3, "acc_norm_stderr": 0.04605661864718381 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4740740740740741, "acc_stderr": 0.04313531696750575, "acc_norm": 0.4740740740740741, "acc_norm_stderr": 0.04313531696750575 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6052631578947368, "acc_stderr": 0.039777499346220734, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.039777499346220734 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6, "acc_stderr": 0.03015113445777629, "acc_norm": 0.6, "acc_norm_stderr": 0.03015113445777629 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6388888888888888, "acc_stderr": 0.040166600304512336, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.040166600304512336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.048580835742663434, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663434 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4808510638297872, "acc_stderr": 0.032662042990646775, "acc_norm": 0.4808510638297872, "acc_norm_stderr": 0.032662042990646775 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.503448275862069, "acc_stderr": 0.04166567577101579, "acc_norm": 0.503448275862069, "acc_norm_stderr": 0.04166567577101579 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.43915343915343913, "acc_stderr": 0.025559920550531006, "acc_norm": 0.43915343915343913, "acc_norm_stderr": 0.025559920550531006 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6741935483870968, "acc_stderr": 0.026662010578567104, "acc_norm": 0.6741935483870968, "acc_norm_stderr": 0.026662010578567104 }, "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": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.703030303030303, "acc_stderr": 0.03567969772268049, "acc_norm": 0.703030303030303, "acc_norm_stderr": 0.03567969772268049 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7272727272727273, "acc_stderr": 0.03173071239071724, "acc_norm": 0.7272727272727273, "acc_norm_stderr": 0.03173071239071724 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7927461139896373, "acc_stderr": 0.029252823291803627, "acc_norm": 0.7927461139896373, "acc_norm_stderr": 0.029252823291803627 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5743589743589743, "acc_stderr": 0.025069094387296535, "acc_norm": 0.5743589743589743, "acc_norm_stderr": 0.025069094387296535 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.028406533090608466, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.028406533090608466 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6428571428571429, "acc_stderr": 0.031124619309328177, "acc_norm": 0.6428571428571429, "acc_norm_stderr": 0.031124619309328177 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242741, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242741 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.818348623853211, "acc_stderr": 0.016530617409266868, "acc_norm": 0.818348623853211, "acc_norm_stderr": 0.016530617409266868 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4583333333333333, "acc_stderr": 0.03398110890294636, "acc_norm": 0.4583333333333333, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6617647058823529, "acc_stderr": 0.03320574612945432, "acc_norm": 0.6617647058823529, "acc_norm_stderr": 0.03320574612945432 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7468354430379747, "acc_stderr": 0.028304657943035282, "acc_norm": 0.7468354430379747, "acc_norm_stderr": 0.028304657943035282 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6367713004484304, "acc_stderr": 0.032277904428505, "acc_norm": 0.6367713004484304, "acc_norm_stderr": 0.032277904428505 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6793893129770993, "acc_stderr": 0.04093329229834278, "acc_norm": 0.6793893129770993, "acc_norm_stderr": 0.04093329229834278 }, "harness|hendrycksTest-international_law|5": { "acc": 0.743801652892562, "acc_stderr": 0.03984979653302871, "acc_norm": 0.743801652892562, "acc_norm_stderr": 0.03984979653302871 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615768, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615768 }, "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": 0.7378640776699029, "acc_stderr": 0.04354631077260594, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260594 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8247863247863247, "acc_stderr": 0.02490443909891824, "acc_norm": 0.8247863247863247, "acc_norm_stderr": 0.02490443909891824 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939098, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6998722860791826, "acc_stderr": 0.016389249691317432, "acc_norm": 0.6998722860791826, "acc_norm_stderr": 0.016389249691317432 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.684971098265896, "acc_stderr": 0.025009313790069716, "acc_norm": 0.684971098265896, "acc_norm_stderr": 0.025009313790069716 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.26033519553072626, "acc_stderr": 0.014676252009319473, "acc_norm": 0.26033519553072626, "acc_norm_stderr": 0.014676252009319473 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6045751633986928, "acc_stderr": 0.02799672318063145, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.02799672318063145 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6495176848874598, "acc_stderr": 0.027098652621301754, "acc_norm": 0.6495176848874598, "acc_norm_stderr": 0.027098652621301754 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6388888888888888, "acc_stderr": 0.026725868809100793, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.026725868809100793 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4219858156028369, "acc_stderr": 0.0294621892333706, "acc_norm": 0.4219858156028369, "acc_norm_stderr": 0.0294621892333706 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4172099087353325, "acc_stderr": 0.012593959992906417, "acc_norm": 0.4172099087353325, "acc_norm_stderr": 0.012593959992906417 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03035969707904612, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03035969707904612 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5392156862745098, "acc_stderr": 0.020165523313907904, "acc_norm": 0.5392156862745098, "acc_norm_stderr": 0.020165523313907904 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6363636363636364, "acc_stderr": 0.046075820907199756, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.046075820907199756 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7061224489795919, "acc_stderr": 0.029162738410249765, "acc_norm": 0.7061224489795919, "acc_norm_stderr": 0.029162738410249765 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7512437810945274, "acc_stderr": 0.030567675938916714, "acc_norm": 0.7512437810945274, "acc_norm_stderr": 0.030567675938916714 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-virology|5": { "acc": 0.463855421686747, "acc_stderr": 0.03882310850890594, "acc_norm": 0.463855421686747, "acc_norm_stderr": 0.03882310850890594 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7017543859649122, "acc_stderr": 0.03508771929824563, "acc_norm": 0.7017543859649122, "acc_norm_stderr": 0.03508771929824563 }, "harness|truthfulqa:mc|0": { "mc1": 0.35495716034271724, "mc1_stderr": 0.016750862381375898, "mc2": 0.5059941161139769, "mc2_stderr": 0.015447236581056423 }, "harness|winogrande|5": { "acc": 0.7545382794001578, "acc_stderr": 0.012095272937183644 }, "harness|gsm8k|5": { "acc": 0.6141015921152388, "acc_stderr": 0.013409077471319164 } } ``` ## 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]
cigdemcnb/turkishReviews-ds-mini
--- dataset_info: features: - name: review dtype: string - name: review_length dtype: int64 splits: - name: train num_bytes: 1252876.2642514652 num_examples: 3378 - name: validation num_bytes: 139455.7357485349 num_examples: 376 download_size: 896651 dataset_size: 1392332.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
mesolitica/chatgpt-explain-sentiment
--- language: - ms pretty_name: chatgpt-malay-explain-sentiment --- # Explain Sentiment Generated using ChatGPT3.5 on Malaysian tweets, notebooks at https://github.com/mesolitica/malaysian-dataset/tree/master/sentiment/chatgpt3.5-sentiment - [sentiment.jsonl](sentiment.jsonl), 162902 rows, 86 MB ## Example data ```python {'sentiment': 'negative', 'explain_en': 'The text is negative because it contains an angry tone and disrespectful language towards someone named Amzar.', 'explain_ms': 'Teks ini negatif kerana mengandungi nada marah dan bahasa yang tidak sopan terhadap seseorang yang bernama Amzar.', 'text': 'BABUN PUNYA AMZAR. TAK RETI HORMAT ORANG KEEEEEE???!!!!'} ```
yzhuang/autotree_automl_electricity_gosdt_l256_d3_sd0
--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 2773600000 num_examples: 100000 - name: validation num_bytes: 277360000 num_examples: 10000 download_size: 691921046 dataset_size: 3050960000 --- # Dataset Card for "autotree_automl_electricity_gosdt_l256_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nayohan/commonsense_qa-ko
--- dataset_info: features: - name: stem dtype: string - name: label_A dtype: string - name: label_B dtype: string - name: label_C dtype: string - name: label_D dtype: string - name: label_E dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 1662060 num_examples: 9741 - name: valid num_bytes: 206056 num_examples: 1221 download_size: 1169959 dataset_size: 1868116 --- # Dataset Card for "commonsense_qa-ko" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NYTK/HuRC
--- YAML tags: annotations_creators: - crowdsourced language_creators: - found - expert-generated language: - hu license: - cc-by-4.0 multilinguality: - monolingual pretty_name: HuRC size_categories: - unknown source_datasets: - extended|other task_categories: - question-answering task_ids: - extractive-qa - abstractive-qa --- # Dataset Card for HuRC ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** [HuRC dataset](https://github.com/nytud/HuRC) - **Paper:** - **Leaderboard:** - **Point of Contact:** [lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu) ### Dataset Summary This is the dataset card for the Hungarian Corpus for Reading Comprehension with Commonsense Reasoning (HuRC), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU. The dataset contains 80 614 instances. Each instance is composed of a lead, a passage and a cloze-style query with a masked entity. The task is to select the named entity that is being masked in the query. The data was automatically collected from the online news of Népszabadság online (nol.hu). ### Languages The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU. ## Dataset Structure ### Data Instances For each instance, there is an id, a lead, a passage, a query and a MASK. An example: ``` { "id": "1", "lead": ["A Közigazgatási és Igazságügyi Minisztérium szerint a Bárka Színház esetében felmerült a felelőtlen gazdálkodás gyanúja, egyes értesülések szerint pedig ebben \"a színház igazgatójának és gazdasági vezetőjének felelőssége is felmerül\""], "passage": [ "A teátrumnak Navracsics Tibor közigazgatási és igazságügyi miniszterhez és Kocsis Máté VIII. kerületi polgármesterhez", "reagálva a tárca azt írta, hogy a felelőtlen gazdálkodás gyanújában \"egyes értesülések szerint a színház igazgatójának és gazdasági vezetőjének felelőssége is felmerül\". A KIM \"éppen ezért nagyon várja az Állami Számvevőszék készülő jelentését, hogy tiszta képet kaphasson a színház működéséről\".", "A minisztérium hangsúlyozta, hogy az elmúlt évben is mindent elkövetett azért, hogy a Bárka Színház \"valós, rangos művészeti térként\" működjön, és a továbbiakban is ez a szándéka, de jelenleg a társulat működtetését a minisztérium fenntartói támogatás formájában jogszerűen még nem tudja megoldani.", "A teátrum az átadás-átvétel elhúzódásának okát keresve tette közzé nyílt levelét, amelyben elmaradó fizetésekre, előadásokra és bemutatókra hívta fel a figyelmet, és jelezte, hogy várja a helyzet megoldását.", "A színház átadás-átvétele jelenleg zajlik, a folyamat végeztével a Bárka a józsefvárosi önkormányzattól állami tulajdonba, a tervek szerint a Közigazgatási és Igazságügyi Minisztérium fenntartásába kerül." ], "query": "A KIM 2014-es költségvetésében szerepel a Bárka Színház, de amíg nem a minisztérium a [MASK] fenntartója, addig ez a költségvetési keret nem nyitható meg.", "MASK": "Bárka", } ``` ### Data Fields - id: unique id of the instances; - lead: a short summary of the article as it was extracted from the source texts; - passage: 3-6 paragraphs of texts as the body of the article; - query: the last paragraph of an article, some kind of summary or conclusion, with a named entity masked (with [MASK]) in it; - MASK: the masked named entity. ### Data Splits HuRC has 3 splits: *train*, *validation* and *test*. | Dataset split | Number of instances in the split | Proportion of the split |---------------|----------------------------------| ---------| | train | 64614 | 80%| | validation | 8000 |10%| | test | 8000 |10%| The test data is distributed without the MASK fields. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment). ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization To produce the Hungarian material, we used the daily articles from Népszabadság Online which had titles and summaries as well. We selected 3-6 paragraphs from each article from the ones which contain proper nouns both in the main part and the summary as well. We trained a NER model using huBERT (Nemeskey 2021) for recognizing proper nouns. NerKor (Simon és Vadász 2021) and Huggingface’s token-level classification library were used to fine-tune the model. Our model achieved an F-score of 90.18 on the test material. As a final step, we found pairs of proper names which are present both in the main article and the summary. Multiple articles contained more than one such pairs so we used those more than once. This resulted in a database of 88655 instances (from 49782 articles). The quantitative properties of our corpus are as follows: Number of articles: 88655 Number of different articles (type): 49782 Token: 27703631 Type: 1115.260 Average length of text (token): 249.42 (median: 229) Average question length (token): 63.07 (median: 56). We fine-tuned the corpus by hand. One annotator per 100 unit checked and validated the dataset for which we provided our own demo interface. Automatic masking and the previous occurrence of the entity was checked. This resulted in a database of 80 614 validated entries. ## Additional Information ### Licensing Information HuRC is released under the cc-by-4.0 license. ### Citation Information If you use this resource or any part of its documentation, please refer to: Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press) ``` @inproceedings{ligetinagy2022hulu, title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából}, author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.}, booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia}, year={2022} } ``` ### Contributions Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset.
huangyt/FINETUNE1
--- license: openrail pretty_name: Finetune1 --- ![Change can be sunshine if you let it in..png](https://cdn-uploads.huggingface.co/production/uploads/64c7bfe8ac1016256b69ea02/3-T_BdltbjPE58LEJ3ksN.png) # 📔 **DATASET** | **Dataset** | Class | Number of Questions | | ------- | ----------------------------------------------------------------- | ------------------------ | | **FLAN_CoT(zs)** | Reasoning 、 MATH 、 ScienceQA 、 Commonsense | 91910 | | **Prm800k** | Reasoning 、 MATH | 6713 | | **ScienceQA** | ScienceQA | 5177 | | **SciBench** | ScienceQA | 695 | | **ReClor** | Reasoning | 1624 | | **TheoremQA** | Commonsense 、 MATH 、 ScienceQA | 800 | | **OpenBookQA** | Text_Understanding 、 Reasoning 、 Commonsense 、 ScienceQA | 5957 | | **ARB** | Reasoning 、 MATH 、 ScienceQA 、 Commonsense 、 Text_Understanding | 605 | | **Openassistant-guanaco** | Commonsense 、 Text_Understanding 、 Reasoning | 802 | | **SQuAD 2.0** | Text_Understanding | 87599 | | **CommonsenseQA** | Commonsense | 9741 | | **Ethics** | Commonsense | 21759 | # 📌 **Methon** ## *Dataset Format Definition* Use "instruction、input、output" tend to lean towards guided datasets. In this format, each sample includes an instruction, an input, and an expected output. The instruction provides guidance on how to process the input to generate the output. This format of dataset is often used to train models to perform specific tasks, as they explicitly indicate the operations the model should perform. ``` { "input": "", "output": "", "instruction": "" } ``` - ### [FLAN_V2 COT(ZS)](https://huggingface.co/datasets/conceptofmind/cot_submix_original/tree/main) We only extract the 'zs_opt' from COT and categorize each task. - ### [CommonsenseQA](https://huggingface.co/datasets/commonsense_qa) We extracted the question and choices from the original CommonsenseQA dataset and placed them in the instruction. We also wrote the input prompt: "Choose A, B, C, D, or E as your solution." - ### [SQuAD](https://huggingface.co/datasets/squad) We used the questions from the SQUAD dataset as instructions and treated the context as the input. - ### [Ethics](https://huggingface.co/datasets/hendrycks/ethics) The ethics dataset, which was originally in labeled format, has been transformed into a true or false format. Additionally, the input now includes the instruction "Give true or false according to ethics." - ### [OTHER](https://github.com/arielnlee/Platypus/tree/main/data_pipeline) Prm800k, ScienceQA, SciBench, ReClor, TheoremQA, OpenBookQA, ARB, and OpenAssistant-Guanaco datasets adopt the same format as Platypus. ## *Sampling Algorithms* 1. First,we are taking all datasets from COT, ARB, TheoremQA and Ethics. ARB and TheoremQA encompass a wide range of fields and have a relatively low total count. Since COT has high quality, we are including the entire dataset. For the Ethics dataset, we are collecting the entire dataset because we want the model to comprehensively learn more about ethics and security aspects. 2. The remaining datasets were initially categorized into the following four groups for the purpose of **Simple Random Sampling**: - *Science Questions and Answers* : ScienceQA、SciBench - *Reasoning & Mathematics* : ReClor、Prm800k - *Text Comprehension* : OpenBookQA、SQuAD - *Commonsense* : CommonsenseQA、Openassistant-guanaco However, we discovered that the total number of datasets in the Science Questions and Answers、Reasoning & Mathematics、and Commonsense categories did not exceed 30,000. As a result, only the Text Comprehension category underwent Simple Random Sampling, while the others were taken in their entirety. # 🏁 **Feature Work** - In the future, we intend to utilize Stratified Sampling due to the imbalance in the number of questions across different datasets, which introduces bias. Conversely, if we opt to randomly sample an equal number of examples from each dataset, it can yield a smaller estimation error for the same total sample size. - We can even evaluate based on the fine-tuning from the first stage and employ additional scripting techniques to enhance the quality of the dataset.
open-llm-leaderboard/details_ArianAskari__SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta
--- pretty_name: Evaluation run of ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta](https://huggingface.co/ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta)\ \ 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_ArianAskari__SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-11T14:20:18.392173](https://huggingface.co/datasets/open-llm-leaderboard/details_ArianAskari__SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta/blob/main/results_2024-02-11T14-20-18.392173.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\": 0.5989068889556914,\n\ \ \"acc_stderr\": 0.03306588865476634,\n \"acc_norm\": 0.6081578643232973,\n\ \ \"acc_norm_stderr\": 0.03380748896101241,\n \"mc1\": 0.3818849449204406,\n\ \ \"mc1_stderr\": 0.017008101939163495,\n \"mc2\": 0.5376745022515824,\n\ \ \"mc2_stderr\": 0.01602462184426783\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5631399317406144,\n \"acc_stderr\": 0.014494421584256522,\n\ \ \"acc_norm\": 0.5972696245733788,\n \"acc_norm_stderr\": 0.01433223630679014\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6338378809002191,\n\ \ \"acc_stderr\": 0.004807699539973415,\n \"acc_norm\": 0.817167894841665,\n\ \ \"acc_norm_stderr\": 0.003857388613533091\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5333333333333333,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n\ \ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6377358490566037,\n \"acc_stderr\": 0.0295822451283843,\n\ \ \"acc_norm\": 0.6377358490566037,\n \"acc_norm_stderr\": 0.0295822451283843\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\ \ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\ \ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\ \ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.03260038511835772,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.03260038511835772\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\ \ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3994708994708995,\n \"acc_stderr\": 0.025225450284067884,\n \"\ acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.025225450284067884\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7483870967741936,\n\ \ \"acc_stderr\": 0.024685979286239966,\n \"acc_norm\": 0.7483870967741936,\n\ \ \"acc_norm_stderr\": 0.024685979286239966\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\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\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\ \ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7373737373737373,\n \"acc_stderr\": 0.03135305009533086,\n \"\ acc_norm\": 0.7373737373737373,\n \"acc_norm_stderr\": 0.03135305009533086\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8341968911917098,\n \"acc_stderr\": 0.026839845022314415,\n\ \ \"acc_norm\": 0.8341968911917098,\n \"acc_norm_stderr\": 0.026839845022314415\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.024697216930878934,\n\ \ \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.024697216930878934\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\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8055045871559633,\n \"acc_stderr\": 0.016970289090458033,\n \"\ acc_norm\": 0.8055045871559633,\n \"acc_norm_stderr\": 0.016970289090458033\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"\ acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145628,\n \"\ acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145628\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7341772151898734,\n \"acc_stderr\": 0.028756799629658342,\n \ \ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6233183856502242,\n\ \ \"acc_stderr\": 0.03252113489929188,\n \"acc_norm\": 0.6233183856502242,\n\ \ \"acc_norm_stderr\": 0.03252113489929188\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n\ \ \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7107438016528925,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n\ \ \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\ \ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\ \ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\ \ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7867177522349936,\n\ \ \"acc_stderr\": 0.014648172749593515,\n \"acc_norm\": 0.7867177522349936,\n\ \ \"acc_norm_stderr\": 0.014648172749593515\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.02494679222527231,\n\ \ \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.02494679222527231\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32625698324022345,\n\ \ \"acc_stderr\": 0.015680441518889178,\n \"acc_norm\": 0.32625698324022345,\n\ \ \"acc_norm_stderr\": 0.015680441518889178\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145898,\n\ \ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145898\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\ \ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\ \ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.026041766202717156,\n\ \ \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.026041766202717156\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4178617992177314,\n\ \ \"acc_stderr\": 0.012596744108998562,\n \"acc_norm\": 0.4178617992177314,\n\ \ \"acc_norm_stderr\": 0.012596744108998562\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6127450980392157,\n \"acc_stderr\": 0.019706875804085637,\n \ \ \"acc_norm\": 0.6127450980392157,\n \"acc_norm_stderr\": 0.019706875804085637\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6181818181818182,\n\ \ \"acc_stderr\": 0.046534298079135075,\n \"acc_norm\": 0.6181818181818182,\n\ \ \"acc_norm_stderr\": 0.046534298079135075\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.03055531675557364,\n\ \ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.03055531675557364\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7960199004975125,\n\ \ \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n\ \ \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072767,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072767\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3818849449204406,\n\ \ \"mc1_stderr\": 0.017008101939163495,\n \"mc2\": 0.5376745022515824,\n\ \ \"mc2_stderr\": 0.01602462184426783\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233623\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12282031842304776,\n \ \ \"acc_stderr\": 0.009041108602874664\n }\n}\n```" repo_url: https://huggingface.co/ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta 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_11T14_20_18.392173 path: - '**/details_harness|arc:challenge|25_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-11T14-20-18.392173.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|gsm8k|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hellaswag|10_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-11T14-20-18.392173.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-management|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T14-20-18.392173.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|truthfulqa:mc|0_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-11T14-20-18.392173.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_11T14_20_18.392173 path: - '**/details_harness|winogrande|5_2024-02-11T14-20-18.392173.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-11T14-20-18.392173.parquet' - config_name: results data_files: - split: 2024_02_11T14_20_18.392173 path: - results_2024-02-11T14-20-18.392173.parquet - split: latest path: - results_2024-02-11T14-20-18.392173.parquet --- # Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta](https://huggingface.co/ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta) 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_ArianAskari__SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-11T14:20:18.392173](https://huggingface.co/datasets/open-llm-leaderboard/details_ArianAskari__SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta/blob/main/results_2024-02-11T14-20-18.392173.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": 0.5989068889556914, "acc_stderr": 0.03306588865476634, "acc_norm": 0.6081578643232973, "acc_norm_stderr": 0.03380748896101241, "mc1": 0.3818849449204406, "mc1_stderr": 0.017008101939163495, "mc2": 0.5376745022515824, "mc2_stderr": 0.01602462184426783 }, "harness|arc:challenge|25": { "acc": 0.5631399317406144, "acc_stderr": 0.014494421584256522, "acc_norm": 0.5972696245733788, "acc_norm_stderr": 0.01433223630679014 }, "harness|hellaswag|10": { "acc": 0.6338378809002191, "acc_stderr": 0.004807699539973415, "acc_norm": 0.817167894841665, "acc_norm_stderr": 0.003857388613533091 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.044619604333847394, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5333333333333333, "acc_stderr": 0.043097329010363554, "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.618421052631579, "acc_stderr": 0.03953173377749194, "acc_norm": 0.618421052631579, "acc_norm_stderr": 0.03953173377749194 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6377358490566037, "acc_stderr": 0.0295822451283843, "acc_norm": 0.6377358490566037, "acc_norm_stderr": 0.0295822451283843 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7222222222222222, "acc_stderr": 0.037455547914624555, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.037455547914624555 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.04784060704105654, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.04784060704105654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5361702127659574, "acc_stderr": 0.03260038511835772, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.03260038511835772 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.04685473041907789, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.04685473041907789 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3994708994708995, "acc_stderr": 0.025225450284067884, "acc_norm": 0.3994708994708995, "acc_norm_stderr": 0.025225450284067884 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.043758884927270605, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.043758884927270605 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7483870967741936, "acc_stderr": 0.024685979286239966, "acc_norm": 0.7483870967741936, "acc_norm_stderr": 0.024685979286239966 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "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": 0.7515151515151515, "acc_stderr": 0.033744026441394036, "acc_norm": 0.7515151515151515, "acc_norm_stderr": 0.033744026441394036 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7373737373737373, "acc_stderr": 0.03135305009533086, "acc_norm": 0.7373737373737373, "acc_norm_stderr": 0.03135305009533086 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8341968911917098, "acc_stderr": 0.026839845022314415, "acc_norm": 0.8341968911917098, "acc_norm_stderr": 0.026839845022314415 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6128205128205129, "acc_stderr": 0.024697216930878934, "acc_norm": 0.6128205128205129, "acc_norm_stderr": 0.024697216930878934 }, "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": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8055045871559633, "acc_stderr": 0.016970289090458033, "acc_norm": 0.8055045871559633, "acc_norm_stderr": 0.016970289090458033 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5324074074074074, "acc_stderr": 0.03402801581358966, "acc_norm": 0.5324074074074074, "acc_norm_stderr": 0.03402801581358966 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7647058823529411, "acc_stderr": 0.029771775228145628, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.029771775228145628 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7341772151898734, "acc_stderr": 0.028756799629658342, "acc_norm": 0.7341772151898734, "acc_norm_stderr": 0.028756799629658342 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6233183856502242, "acc_stderr": 0.03252113489929188, "acc_norm": 0.6233183856502242, "acc_norm_stderr": 0.03252113489929188 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6641221374045801, "acc_stderr": 0.041423137719966634, "acc_norm": 0.6641221374045801, "acc_norm_stderr": 0.041423137719966634 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7107438016528925, "acc_stderr": 0.041391127276354626, "acc_norm": 0.7107438016528925, "acc_norm_stderr": 0.041391127276354626 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7116564417177914, "acc_stderr": 0.035590395316173425, "acc_norm": 0.7116564417177914, "acc_norm_stderr": 0.035590395316173425 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.39285714285714285, "acc_stderr": 0.04635550135609976, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.04635550135609976 }, "harness|hendrycksTest-management|5": { "acc": 0.7378640776699029, "acc_stderr": 0.04354631077260595, "acc_norm": 0.7378640776699029, "acc_norm_stderr": 0.04354631077260595 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8931623931623932, "acc_stderr": 0.02023714900899093, "acc_norm": 0.8931623931623932, "acc_norm_stderr": 0.02023714900899093 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7867177522349936, "acc_stderr": 0.014648172749593515, "acc_norm": 0.7867177522349936, "acc_norm_stderr": 0.014648172749593515 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6878612716763006, "acc_stderr": 0.02494679222527231, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.02494679222527231 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.32625698324022345, "acc_stderr": 0.015680441518889178, "acc_norm": 0.32625698324022345, "acc_norm_stderr": 0.015680441518889178 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6895424836601307, "acc_stderr": 0.026493033225145898, "acc_norm": 0.6895424836601307, "acc_norm_stderr": 0.026493033225145898 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6881028938906752, "acc_stderr": 0.026311858071854155, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.026311858071854155 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6759259259259259, "acc_stderr": 0.026041766202717156, "acc_norm": 0.6759259259259259, "acc_norm_stderr": 0.026041766202717156 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.02973659252642444, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.02973659252642444 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4178617992177314, "acc_stderr": 0.012596744108998562, "acc_norm": 0.4178617992177314, "acc_norm_stderr": 0.012596744108998562 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.028661996202335303, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.028661996202335303 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6127450980392157, "acc_stderr": 0.019706875804085637, "acc_norm": 0.6127450980392157, "acc_norm_stderr": 0.019706875804085637 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6181818181818182, "acc_stderr": 0.046534298079135075, "acc_norm": 0.6181818181818182, "acc_norm_stderr": 0.046534298079135075 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6489795918367347, "acc_stderr": 0.03055531675557364, "acc_norm": 0.6489795918367347, "acc_norm_stderr": 0.03055531675557364 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7960199004975125, "acc_stderr": 0.02849317624532607, "acc_norm": 0.7960199004975125, "acc_norm_stderr": 0.02849317624532607 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333045, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333045 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8245614035087719, "acc_stderr": 0.02917088550072767, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.02917088550072767 }, "harness|truthfulqa:mc|0": { "mc1": 0.3818849449204406, "mc1_stderr": 0.017008101939163495, "mc2": 0.5376745022515824, "mc2_stderr": 0.01602462184426783 }, "harness|winogrande|5": { "acc": 0.7466456195737964, "acc_stderr": 0.012223754434233623 }, "harness|gsm8k|5": { "acc": 0.12282031842304776, "acc_stderr": 0.009041108602874664 } } ``` ## 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]
open-llm-leaderboard/details_perlthoughts__openchat-3.5-1210-32k-8x7b-MoE
--- pretty_name: Evaluation run of perlthoughts/openchat-3.5-1210-32k-8x7b-MoE dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [perlthoughts/openchat-3.5-1210-32k-8x7b-MoE](https://huggingface.co/perlthoughts/openchat-3.5-1210-32k-8x7b-MoE)\ \ 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_perlthoughts__openchat-3.5-1210-32k-8x7b-MoE\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-05T03:11:16.908454](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__openchat-3.5-1210-32k-8x7b-MoE/blob/main/results_2024-01-05T03-11-16.908454.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\": 0.6167149824796962,\n\ \ \"acc_stderr\": 0.03270785052087277,\n \"acc_norm\": 0.6202787181505718,\n\ \ \"acc_norm_stderr\": 0.03336449220180264,\n \"mc1\": 0.3292533659730722,\n\ \ \"mc1_stderr\": 0.016451264440068232,\n \"mc2\": 0.4931724783053433,\n\ \ \"mc2_stderr\": 0.015404387399947296\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5972696245733788,\n \"acc_stderr\": 0.01433223630679015,\n\ \ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.013975454122756565\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6394144592710616,\n\ \ \"acc_stderr\": 0.004791890625834195,\n \"acc_norm\": 0.8406691894045011,\n\ \ \"acc_norm_stderr\": 0.0036523632532895825\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.6716981132075471,\n \"acc_stderr\": 0.02890159361241178,\n\ \ \"acc_norm\": 0.6716981132075471,\n \"acc_norm_stderr\": 0.02890159361241178\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n\ \ \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n\ \ \"acc_norm_stderr\": 0.03852084696008534\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\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\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\": 0.6011560693641619,\n\ \ \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.6011560693641619,\n\ \ \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207762,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207762\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467383,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467383\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.02497695405315525,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.02497695405315525\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\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n\ \ \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.7741935483870968,\n\ \ \"acc_norm_stderr\": 0.023785577884181015\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.035107665979592174,\n\ \ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.035107665979592174\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\"\ : 0.61,\n \"acc_norm_stderr\": 0.04902071300001974\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\"\ : 0.7070707070707071,\n \"acc_stderr\": 0.03242497958178815,\n \"\ acc_norm\": 0.7070707070707071,\n \"acc_norm_stderr\": 0.03242497958178815\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \ \ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6134453781512605,\n \"acc_stderr\": 0.03163145807552379,\n \ \ \"acc_norm\": 0.6134453781512605,\n \"acc_norm_stderr\": 0.03163145807552379\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8293577981651377,\n \"acc_stderr\": 0.01612927102509986,\n \"\ acc_norm\": 0.8293577981651377,\n \"acc_norm_stderr\": 0.01612927102509986\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.48148148148148145,\n \"acc_stderr\": 0.03407632093854052,\n \"\ acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.03407632093854052\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.803921568627451,\n \"acc_stderr\": 0.027865942286639325,\n \"\ acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639325\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\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.038808483010823944,\n\ \ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.038808483010823944\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.036401182719909476,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.036401182719909476\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\ \ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n\ \ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\ \ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\ \ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n\ \ \"acc_stderr\": 0.014419123980931894,\n \"acc_norm\": 0.7956577266922095,\n\ \ \"acc_norm_stderr\": 0.014419123980931894\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7138728323699421,\n \"acc_stderr\": 0.02433214677913413,\n\ \ \"acc_norm\": 0.7138728323699421,\n \"acc_norm_stderr\": 0.02433214677913413\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.32737430167597764,\n\ \ \"acc_stderr\": 0.015694238967737386,\n \"acc_norm\": 0.32737430167597764,\n\ \ \"acc_norm_stderr\": 0.015694238967737386\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.026415601914388992,\n\ \ \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.026415601914388992\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6784565916398714,\n\ \ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\ \ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4397163120567376,\n \"acc_stderr\": 0.02960991207559411,\n \ \ \"acc_norm\": 0.4397163120567376,\n \"acc_norm_stderr\": 0.02960991207559411\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45436766623207303,\n\ \ \"acc_stderr\": 0.012716941720734813,\n \"acc_norm\": 0.45436766623207303,\n\ \ \"acc_norm_stderr\": 0.012716941720734813\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335307,\n\ \ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335307\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6290849673202614,\n \"acc_stderr\": 0.01954210156485412,\n \ \ \"acc_norm\": 0.6290849673202614,\n \"acc_norm_stderr\": 0.01954210156485412\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.029393609319879804,\n\ \ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.029393609319879804\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8009950248756219,\n\ \ \"acc_stderr\": 0.028231365092758406,\n \"acc_norm\": 0.8009950248756219,\n\ \ \"acc_norm_stderr\": 0.028231365092758406\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4879518072289157,\n\ \ \"acc_stderr\": 0.0389136449583582,\n \"acc_norm\": 0.4879518072289157,\n\ \ \"acc_norm_stderr\": 0.0389136449583582\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3292533659730722,\n\ \ \"mc1_stderr\": 0.016451264440068232,\n \"mc2\": 0.4931724783053433,\n\ \ \"mc2_stderr\": 0.015404387399947296\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7916337805840569,\n \"acc_stderr\": 0.011414554399987729\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.48142532221379836,\n \ \ \"acc_stderr\": 0.013762977910317583\n }\n}\n```" repo_url: https://huggingface.co/perlthoughts/openchat-3.5-1210-32k-8x7b-MoE 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_05T03_11_16.908454 path: - '**/details_harness|arc:challenge|25_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-05T03-11-16.908454.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|gsm8k|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hellaswag|10_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-11-16.908454.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-11-16.908454.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T03-11-16.908454.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_05T03_11_16.908454 path: - '**/details_harness|winogrande|5_2024-01-05T03-11-16.908454.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-05T03-11-16.908454.parquet' - config_name: results data_files: - split: 2024_01_05T03_11_16.908454 path: - results_2024-01-05T03-11-16.908454.parquet - split: latest path: - results_2024-01-05T03-11-16.908454.parquet --- # Dataset Card for Evaluation run of perlthoughts/openchat-3.5-1210-32k-8x7b-MoE <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [perlthoughts/openchat-3.5-1210-32k-8x7b-MoE](https://huggingface.co/perlthoughts/openchat-3.5-1210-32k-8x7b-MoE) 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_perlthoughts__openchat-3.5-1210-32k-8x7b-MoE", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-05T03:11:16.908454](https://huggingface.co/datasets/open-llm-leaderboard/details_perlthoughts__openchat-3.5-1210-32k-8x7b-MoE/blob/main/results_2024-01-05T03-11-16.908454.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": 0.6167149824796962, "acc_stderr": 0.03270785052087277, "acc_norm": 0.6202787181505718, "acc_norm_stderr": 0.03336449220180264, "mc1": 0.3292533659730722, "mc1_stderr": 0.016451264440068232, "mc2": 0.4931724783053433, "mc2_stderr": 0.015404387399947296 }, "harness|arc:challenge|25": { "acc": 0.5972696245733788, "acc_stderr": 0.01433223630679015, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.013975454122756565 }, "harness|hellaswag|10": { "acc": 0.6394144592710616, "acc_stderr": 0.004791890625834195, "acc_norm": 0.8406691894045011, "acc_norm_stderr": 0.0036523632532895825 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6, "acc_stderr": 0.04232073695151589, "acc_norm": 0.6, "acc_norm_stderr": 0.04232073695151589 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6716981132075471, "acc_stderr": 0.02890159361241178, "acc_norm": 0.6716981132075471, "acc_norm_stderr": 0.02890159361241178 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6944444444444444, "acc_stderr": 0.03852084696008534, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.03852084696008534 }, "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": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "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": 0.6011560693641619, "acc_stderr": 0.037336266553835096, "acc_norm": 0.6011560693641619, "acc_norm_stderr": 0.037336266553835096 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207762, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207762 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467383, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467383 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5793103448275863, "acc_stderr": 0.0411391498118926, "acc_norm": 0.5793103448275863, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3783068783068783, "acc_stderr": 0.02497695405315525, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.02497695405315525 }, "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": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7741935483870968, "acc_stderr": 0.023785577884181015, "acc_norm": 0.7741935483870968, "acc_norm_stderr": 0.023785577884181015 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.46798029556650245, "acc_stderr": 0.035107665979592174, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.035107665979592174 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001974, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001974 }, "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": 0.7070707070707071, "acc_stderr": 0.03242497958178815, "acc_norm": 0.7070707070707071, "acc_norm_stderr": 0.03242497958178815 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758733, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758733 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.28888888888888886, "acc_stderr": 0.027634907264178544, "acc_norm": 0.28888888888888886, "acc_norm_stderr": 0.027634907264178544 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6134453781512605, "acc_stderr": 0.03163145807552379, "acc_norm": 0.6134453781512605, "acc_norm_stderr": 0.03163145807552379 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763743, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763743 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8293577981651377, "acc_stderr": 0.01612927102509986, "acc_norm": 0.8293577981651377, "acc_norm_stderr": 0.01612927102509986 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.48148148148148145, "acc_stderr": 0.03407632093854052, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.03407632093854052 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.803921568627451, "acc_stderr": 0.027865942286639325, "acc_norm": 0.803921568627451, "acc_norm_stderr": 0.027865942286639325 }, "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": 0.6771300448430493, "acc_stderr": 0.03138147637575499, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575499 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.732824427480916, "acc_stderr": 0.038808483010823944, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.038808483010823944 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.036401182719909476, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.036401182719909476 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7129629629629629, "acc_stderr": 0.043733130409147614, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.039891398595317706, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.039891398595317706 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7956577266922095, "acc_stderr": 0.014419123980931894, "acc_norm": 0.7956577266922095, "acc_norm_stderr": 0.014419123980931894 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7138728323699421, "acc_stderr": 0.02433214677913413, "acc_norm": 0.7138728323699421, "acc_norm_stderr": 0.02433214677913413 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.32737430167597764, "acc_stderr": 0.015694238967737386, "acc_norm": 0.32737430167597764, "acc_norm_stderr": 0.015694238967737386 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6928104575163399, "acc_stderr": 0.026415601914388992, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.026415601914388992 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6784565916398714, "acc_stderr": 0.026527724079528872, "acc_norm": 0.6784565916398714, "acc_norm_stderr": 0.026527724079528872 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7222222222222222, "acc_stderr": 0.024922001168886335, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.024922001168886335 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4397163120567376, "acc_stderr": 0.02960991207559411, "acc_norm": 0.4397163120567376, "acc_norm_stderr": 0.02960991207559411 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.45436766623207303, "acc_stderr": 0.012716941720734813, "acc_norm": 0.45436766623207303, "acc_norm_stderr": 0.012716941720734813 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6654411764705882, "acc_stderr": 0.028661996202335307, "acc_norm": 0.6654411764705882, "acc_norm_stderr": 0.028661996202335307 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6290849673202614, "acc_stderr": 0.01954210156485412, "acc_norm": 0.6290849673202614, "acc_norm_stderr": 0.01954210156485412 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6979591836734694, "acc_stderr": 0.029393609319879804, "acc_norm": 0.6979591836734694, "acc_norm_stderr": 0.029393609319879804 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8009950248756219, "acc_stderr": 0.028231365092758406, "acc_norm": 0.8009950248756219, "acc_norm_stderr": 0.028231365092758406 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-virology|5": { "acc": 0.4879518072289157, "acc_stderr": 0.0389136449583582, "acc_norm": 0.4879518072289157, "acc_norm_stderr": 0.0389136449583582 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.02991312723236804, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.02991312723236804 }, "harness|truthfulqa:mc|0": { "mc1": 0.3292533659730722, "mc1_stderr": 0.016451264440068232, "mc2": 0.4931724783053433, "mc2_stderr": 0.015404387399947296 }, "harness|winogrande|5": { "acc": 0.7916337805840569, "acc_stderr": 0.011414554399987729 }, "harness|gsm8k|5": { "acc": 0.48142532221379836, "acc_stderr": 0.013762977910317583 } } ``` ## 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]
dhruvabansal/llama-training-ablation
--- license: apache-2.0 task_categories: - text-classification language: - en pretty_name: training-ablation size_categories: - 1K<n<10K --- Each dataset has exactly three columns: instruction,input,output. Everything is clean and can be processed to create few shot training examples.
sam1120/terrain-jackal-morning-344-v1.0
--- dataset_info: features: - name: name dtype: string - name: pixel_values dtype: image - name: labels dtype: image splits: - name: train num_bytes: 955653437.0 num_examples: 344 download_size: 276803569 dataset_size: 955653437.0 --- # Dataset Card for "terrain-jackal-morning-344-v1.0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sminpark/ds-alpha-small-dataset-v1.3
--- license: gpl ---
Denissilva88/JJS
--- license: openrail ---
CyberHarem/iris_arknights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of iris/アイリス/爱丽丝 (Arknights) This is the dataset of iris/アイリス/爱丽丝 (Arknights), containing 44 images and their tags. The core tags of this character are `long_hair, animal_ears, blue_eyes, hair_ornament, cat_ears, bow, hair_bow, hair_flower, very_long_hair, blonde_hair, parted_bangs, animal_ear_fluff, black_bow, brown_hair, drill_hair`, 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 | 44 | 86.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 44 | 71.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 108 | 132.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iris_arknights/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/iris_arknights', 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 | 15 | ![](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, long_sleeves, solo, black_jacket, looking_at_viewer, closed_mouth, white_shirt, blue_rose, open_jacket, blue_skirt, frills, blue_nails, holding_fan, folding_fan, staff | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | black_jacket | looking_at_viewer | closed_mouth | white_shirt | blue_rose | open_jacket | blue_skirt | frills | blue_nails | holding_fan | folding_fan | staff | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:---------------|:--------------------|:---------------|:--------------|:------------|:--------------|:-------------|:---------|:-------------|:--------------|:--------------|:--------| | 0 | 15 | ![](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 |
CyberHarem/katori_kantaicollection
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of katori/香取/香取 (Kantai Collection) This is the dataset of katori/香取/香取 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `glasses, green_eyes, folded_ponytail, breasts, large_breasts, brown_hair, bangs`, 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 | 423.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katori_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 304.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katori_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1097 | 598.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katori_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 397.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katori_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1097 | 738.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katori_kantaicollection/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/katori_kantaicollection', 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 | 17 | ![](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, epaulettes, military_uniform, necktie, solo, white_gloves, collared_shirt, double-breasted, jacket, black_pantyhose, miniskirt, smile, looking_at_viewer, parted_bangs, light_brown_hair, pencil_skirt, simple_background, white_background, grey_skirt, riding_crop, long_sleeves | | 1 | 15 | ![](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, collared_shirt, double-breasted, epaulettes, looking_at_viewer, military_uniform, solo, upper_body, parted_bangs, simple_background, smile, white_gloves, white_background, jacket, long_sleeves, light_brown_hair, black_necktie, grey_shirt | | 2 | 6 | ![](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) | 1girl, epaulettes, military_uniform, miniskirt, necktie, pantyhose, riding_crop, solo, white_gloves, smile | | 3 | 5 | ![](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, epaulettes, military_uniform, necktie, panties_under_pantyhose, solo, white_gloves, black_pantyhose, sitting, smile, looking_at_viewer, miniskirt, feet | | 4 | 5 | ![](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) | 1boy, 1girl, epaulettes, hetero, military_uniform, solo_focus, blush, necktie, penis, white_gloves, smile, bar_censor, heart, huge_breasts, looking_at_viewer, nipples, paizuri | | 5 | 8 | ![](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, light_brown_hair, looking_at_viewer, solo, blush, cleavage, parted_bangs, rimless_eyewear, simple_background, long_hair, side-tie_bikini_bottom, white_background, cowboy_shot, navel, white_bikini, front-tie_top | | 6 | 5 | ![](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, looking_at_viewer, smile, solo, bikini, blush, navel, cleavage, pointer, twitter_username | | 7 | 7 | ![](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, competition_swimsuit, cowboy_shot, solo, parted_bangs, collarbone, highleg_swimsuit, looking_at_viewer, simple_background, blue_one-piece_swimsuit, dated, jacket, twitter_username, white_background, white_one-piece_swimsuit | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | epaulettes | military_uniform | necktie | solo | white_gloves | collared_shirt | double-breasted | jacket | black_pantyhose | miniskirt | smile | looking_at_viewer | parted_bangs | light_brown_hair | pencil_skirt | simple_background | white_background | grey_skirt | riding_crop | long_sleeves | upper_body | black_necktie | grey_shirt | pantyhose | panties_under_pantyhose | sitting | feet | 1boy | hetero | solo_focus | blush | penis | bar_censor | heart | huge_breasts | nipples | paizuri | cleavage | rimless_eyewear | long_hair | side-tie_bikini_bottom | cowboy_shot | navel | white_bikini | front-tie_top | bikini | pointer | twitter_username | competition_swimsuit | collarbone | highleg_swimsuit | blue_one-piece_swimsuit | dated | white_one-piece_swimsuit | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:-------------------|:----------|:-------|:---------------|:-----------------|:------------------|:---------|:------------------|:------------|:--------|:--------------------|:---------------|:-------------------|:---------------|:--------------------|:-------------------|:-------------|:--------------|:---------------|:-------------|:----------------|:-------------|:------------|:--------------------------|:----------|:-------|:-------|:---------|:-------------|:--------|:--------|:-------------|:--------|:---------------|:----------|:----------|:-----------|:------------------|:------------|:-------------------------|:--------------|:--------|:---------------|:----------------|:---------|:----------|:-------------------|:-----------------------|:-------------|:-------------------|:--------------------------|:--------|:---------------------------| | 0 | 17 | ![](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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 15 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | 5 | 8 | ![](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 | | | | | | | | | | | 6 | 5 | ![](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 | | | | | | | | 7 | 7 | ![](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 |
itzzdeep/mrbeast-thumbnails
--- dataset_info: features: - name: image dtype: image - name: caption dtype: string splits: - name: train num_bytes: 20560694.0 num_examples: 150 download_size: 20535577 dataset_size: 20560694.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
yzhuang/autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2
--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float64 - name: input_y sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float64 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 10863200000 num_examples: 100000 - name: validation num_bytes: 1086320000 num_examples: 10000 download_size: 2039406876 dataset_size: 11949520000 --- # Dataset Card for "autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
universeTBD/arxiv-qa-astro-ph
--- dataset_info: features: - name: index dtype: int64 - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 4108026 num_examples: 10356 download_size: 2402562 dataset_size: 4108026 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "arxiv-qa-astro-ph" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-college_biology-neg-prepend-verbal
--- configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: neg_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string splits: - name: dev num_bytes: 8495 num_examples: 5 - name: test num_bytes: 1406615 num_examples: 144 download_size: 196092 dataset_size: 1415110 --- # Dataset Card for "mmlu-college_biology-neg-prepend-verbal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Corianas__256_5epoch
--- pretty_name: Evaluation run of Corianas/256_5epoch dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Corianas/256_5epoch](https://huggingface.co/Corianas/256_5epoch) on the [Open\ \ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_Corianas__256_5epoch\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T17:10:44.545164](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__256_5epoch/blob/main/results_2023-09-17T17-10-44.545164.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\": 0.006082214765100671,\n\ \ \"em_stderr\": 0.0007962432393028846,\n \"f1\": 0.04929320469798652,\n\ \ \"f1_stderr\": 0.0015028533751229739,\n \"acc\": 0.26475206337105733,\n\ \ \"acc_stderr\": 0.0076718947223475545\n },\n \"harness|drop|3\":\ \ {\n \"em\": 0.006082214765100671,\n \"em_stderr\": 0.0007962432393028846,\n\ \ \"f1\": 0.04929320469798652,\n \"f1_stderr\": 0.0015028533751229739\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.002274450341167551,\n \ \ \"acc_stderr\": 0.0013121578148674133\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5272296764009471,\n \"acc_stderr\": 0.014031631629827696\n\ \ }\n}\n```" repo_url: https://huggingface.co/Corianas/256_5epoch leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_09_17T17_10_44.545164 path: - '**/details_harness|drop|3_2023-09-17T17-10-44.545164.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T17-10-44.545164.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T17_10_44.545164 path: - '**/details_harness|gsm8k|5_2023-09-17T17-10-44.545164.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T17-10-44.545164.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T17_10_44.545164 path: - '**/details_harness|winogrande|5_2023-09-17T17-10-44.545164.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T17-10-44.545164.parquet' - config_name: results data_files: - split: 2023_09_17T17_10_44.545164 path: - results_2023-09-17T17-10-44.545164.parquet - split: latest path: - results_2023-09-17T17-10-44.545164.parquet --- # Dataset Card for Evaluation run of Corianas/256_5epoch ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Corianas/256_5epoch - **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 [Corianas/256_5epoch](https://huggingface.co/Corianas/256_5epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_Corianas__256_5epoch", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T17:10:44.545164](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__256_5epoch/blob/main/results_2023-09-17T17-10-44.545164.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": 0.006082214765100671, "em_stderr": 0.0007962432393028846, "f1": 0.04929320469798652, "f1_stderr": 0.0015028533751229739, "acc": 0.26475206337105733, "acc_stderr": 0.0076718947223475545 }, "harness|drop|3": { "em": 0.006082214765100671, "em_stderr": 0.0007962432393028846, "f1": 0.04929320469798652, "f1_stderr": 0.0015028533751229739 }, "harness|gsm8k|5": { "acc": 0.002274450341167551, "acc_stderr": 0.0013121578148674133 }, "harness|winogrande|5": { "acc": 0.5272296764009471, "acc_stderr": 0.014031631629827696 } } ``` ### 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]
HuggingFaceM4/AdVQA_modif-Sample
Invalid username or password.
Bieubr/CarlosGemer
--- license: openrail ---
Talelaw/soningtidsberegninger
--- license: eupl-1.1 ---
Fece228/latin-literature-dataset-170M
--- language: - la tags: - text - linguistics - NLP - Latin - literature size_categories: - 100M<n<1B --- This is a dataset collected from all the texts available at Corpus Corporum, which includes probably all the literary works ever written in Latin. The dataset is split in two parts: preprocessed with basic cltk tools, ready for work, and raw text data. It must be noted, however, that the latter contains text in Greek, Hebrew, and other languages, with references and contractions
udkai/klexikon_dpo
--- license: cc-by-sa-4.0 dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 7156517 num_examples: 2893 download_size: 4334446 dataset_size: 7156517 configs: - config_name: default data_files: - split: train path: data/train-* language: - de pretty_name: Kinder Lexikon Direct Preference Optimization Dataset tags: - simple-german - dpo - language simplification --- Version of https://huggingface.co/datasets/dennlinger/klexikon which can be useful for Direct Preference Optimization of large language models generating sentences in simple german.
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo2_100_kl_0.1_prm_160m_thr_1.0_seed_1
--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43630616 num_examples: 18929 - name: epoch_1 num_bytes: 43868088 num_examples: 18929 - name: epoch_2 num_bytes: 43827793 num_examples: 18929 - name: epoch_3 num_bytes: 43780418 num_examples: 18929 - name: epoch_4 num_bytes: 43767895 num_examples: 18929 - name: epoch_5 num_bytes: 43748008 num_examples: 18929 - name: epoch_6 num_bytes: 43740763 num_examples: 18929 - name: epoch_7 num_bytes: 43732082 num_examples: 18929 - name: epoch_8 num_bytes: 43726319 num_examples: 18929 - name: epoch_9 num_bytes: 43727460 num_examples: 18929 download_size: 232404489 dataset_size: 437549442 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-* ---
liuyanchen1015/MULTI_VALUE_mnli_double_determiners
--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 719 num_examples: 5 - name: dev_mismatched num_bytes: 1459 num_examples: 6 - name: test_matched num_bytes: 1145 num_examples: 8 - name: test_mismatched num_bytes: 439 num_examples: 3 - name: train num_bytes: 54368 num_examples: 261 download_size: 37140 dataset_size: 58130 --- # Dataset Card for "MULTI_VALUE_mnli_double_determiners" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shaowinw/seismic_inversion
--- license: cc-by-4.0 --- # How to download this ```$ git lfs install``` ```$ git clone https://huggingface.co/shaowinw/seismic_inversion```
arieg/cluster02_large_10
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '000140' '1': 001259 '2': '004507' '3': 005940 '4': '006443' '5': 007483 '6': 007487 '7': 007872 '8': '011237' '9': 012986 '10': '014541' '11': '014576' '12': '014661' '13': 018037 '14': 018038 '15': '022477' '16': '024367' '17': 025668 '18': 028241 '19': 028266 '20': '030056' '21': '032333' '22': '032337' '23': 032339 '24': '035543' '25': 036999 '26': 039259 '27': 039658 '28': '040657' '29': '042020' '30': '042023' '31': '042025' '32': '042030' '33': '042046' '34': '042372' '35': '043030' '36': 043598 '37': '043761' '38': 043965 '39': 044794 '40': 046839 '41': 047197 '42': 047835 '43': 049394 '44': 049478 '45': '051655' '46': 051659 '47': '052120' '48': '052122' '49': '052123' '50': '052125' '51': '053154' '52': '054153' '53': 055826 '54': 055830 '55': 055831 '56': '057371' '57': '057640' '58': '057665' '59': 057691 '60': 059678 '61': '060170' '62': '061160' '63': '061736' '64': 061820 '65': 061821 '66': 062592 '67': '064364' '68': 064629 '69': '066405' '70': '067366' '71': '067367' '72': '070426' '73': 072149 '74': 072788 '75': 073309 '76': '073467' '77': 075428 '78': 075784 '79': 075862 '80': '076074' '81': 076079 '82': 079593 '83': 080518 '84': 085966 '85': 086140 '86': 091443 '87': 094449 '88': 094628 '89': 095908 '90': 096168 '91': 096696 '92': 097374 '93': 099095 '94': '101111' '95': '101112' '96': '107432' '97': '107567' '98': '108012' '99': '108529' '100': '109445' '101': '109449' '102': '109450' '103': '110263' '104': '111392' '105': '112197' '106': '113018' '107': '113360' '108': '114036' '109': '114041' '110': '116239' '111': '116735' '112': '117170' '113': '119592' '114': '120196' '115': '121273' '116': '122077' '117': '122082' '118': '122201' '119': '122247' '120': '125190' '121': '126017' '122': '126300' '123': '126411' '124': '126718' '125': '128469' '126': '129887' '127': '129972' '128': '130129' '129': '130709' '130': '130711' '131': '131624' '132': '131787' '133': '134643' '134': '134934' '135': '135028' '136': '135043' '137': '135336' '138': '137898' '139': '139330' '140': '139804' '141': '140421' '142': '141903' '143': '144171' '144': '144551' '145': '144935' '146': '145749' '147': '145780' '148': '146639' '149': '148303' '150': '148518' '151': '148608' '152': '149623' '153': '149953' splits: - name: train num_bytes: 82713531.4 num_examples: 1540 download_size: 82253270 dataset_size: 82713531.4 configs: - config_name: default data_files: - split: train path: data/train-* ---
one-sec-cv12/chunk_124
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 28649869776.125 num_examples: 298287 download_size: 26744267899 dataset_size: 28649869776.125 --- # Dataset Card for "chunk_124" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
feynman-integrals-nn/t331ZZZM-s12_24
--- license: cc-by-4.0 --- # t331ZZZM * [data](https://huggingface.co/datasets/feynman-integrals-nn/t331ZZZM-s12_24) * [model](https://huggingface.co/feynman-integrals-nn/t331ZZZM-dimensionless) * [source](https://gitlab.com/feynman-integrals-nn/feynman-integrals-nn/-/tree/main/t331ZZZM) Warning: deprecated dataset
persiannlp/parsinlu_reading_comprehension
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - fa license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|wikipedia|google task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for PersiNLU (Reading Comprehension) ## Table of Contents - [Dataset Card for PersiNLU (Reading Comprehension)](#dataset-card-for-persi_nlu_reading_comprehension) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [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/persiannlp/parsinlu/) - **Repository:** [Github](https://github.com/persiannlp/parsinlu/) - **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154) - **Leaderboard:** - **Point of Contact:** d.khashabi@gmail.com ### Dataset Summary A Persian reading comprehenion task (generating an answer, given a question and a context paragraph). The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The text dataset is in Persian (`fa`). ## Dataset Structure ### Data Instances Here is an example from the dataset: ``` { 'question': 'پیامبر در چه سالی به پیامبری رسید؟', 'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF', 'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.', 'answers': [ {'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'} ] } ``` ### Data Fields - `question`: the question, mined using Google auto-complete. - `passage`: the passage that contains the answer. - `url`: the url from which the passage was mined. - `answers`: a list of answers, containing the string and the index of the answer. ### Data Splits The train/test split contains 600/575 samples. ## Dataset Creation ### Curation Rationale The question were collected via Google auto-complete. The answers were annotated by native speakers. For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154). ### 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 CC BY-NC-SA 4.0 License ### Citation Information ```bibtex @article{huggingface:dataset, title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian}, authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others}, year={2020} journal = {arXiv e-prints}, eprint = {2012.06154}, } ``` ### Contributions Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
tomekkorbak/shp_with_features_20k
--- dataset_info: features: - name: post_id dtype: string - name: domain dtype: string - name: upvote_ratio dtype: float64 - name: history dtype: string - name: c_root_id_A dtype: string - name: c_root_id_B dtype: string - name: created_at_utc_A dtype: int64 - name: created_at_utc_B dtype: int64 - name: score_A dtype: int64 - name: score_B dtype: int64 - name: human_ref_A dtype: string - name: human_ref_B dtype: string - name: labels dtype: int64 - name: seconds_difference dtype: float64 - name: score_ratio dtype: float64 - name: helpfulness_A dtype: float64 - name: helpfulness_B dtype: float64 - name: specificity_A dtype: float64 - name: specificity_B dtype: float64 - name: intent_A dtype: float64 - name: intent_B dtype: float64 - name: factuality_A dtype: float64 - name: factuality_B dtype: float64 - name: easy-to-understand_A dtype: float64 - name: easy-to-understand_B dtype: float64 - name: relevance_A dtype: float64 - name: relevance_B dtype: float64 - name: readability_A dtype: float64 - name: readability_B dtype: float64 - name: enough-detail_A dtype: float64 - name: enough-detail_B dtype: float64 - name: biased:_A dtype: float64 - name: biased:_B dtype: float64 - name: fail-to-consider-individual-preferences_A dtype: float64 - name: fail-to-consider-individual-preferences_B dtype: float64 - name: repetetive_A dtype: float64 - name: repetetive_B dtype: float64 - name: fail-to-consider-context_A dtype: float64 - name: fail-to-consider-context_B dtype: float64 - name: too-long_A dtype: float64 - name: too-long_B dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 20532157.0 num_examples: 9459 - name: test num_bytes: 20532157.0 num_examples: 9459 download_size: 23638147 dataset_size: 41064314.0 --- # Dataset Card for "shp_with_features_20k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dylanmontoya22/biomedical-ner
--- dataset_info: features: - name: text dtype: string - name: tokens sequence: string - name: prediction sequence: 'null' - name: prediction_agent dtype: string - name: annotation list: - name: end dtype: int64 - name: label dtype: string - name: start dtype: int64 - name: annotation_agent dtype: string - name: vectors dtype: 'null' - name: id dtype: string - name: metadata dtype: 'null' - name: status dtype: string - name: event_timestamp dtype: timestamp[us] - name: metrics struct: - name: annotated struct: - name: mentions list: - name: capitalness dtype: string - name: chars_length dtype: int64 - name: density dtype: float64 - name: label dtype: string - name: score dtype: float64 - name: tokens_length dtype: int64 - name: value dtype: string - name: tags list: - name: tag dtype: string - name: value dtype: string - name: predicted struct: - name: mentions sequence: 'null' - name: tags list: - name: tag dtype: string - name: value dtype: string - name: text_length dtype: int64 - name: tokens list: - name: capitalness dtype: string - name: char_end dtype: int64 - name: char_start dtype: int64 - name: custom dtype: 'null' - name: idx dtype: int64 - name: length dtype: int64 - name: score dtype: 'null' - name: tag dtype: string - name: value dtype: string - name: tokens_length dtype: int64 splits: - name: train num_bytes: 1055927 num_examples: 1000 download_size: 184089 dataset_size: 1055927 --- # Dataset Card for "biomedical-ner" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Laethitia/Gaarabr
--- license: openrail ---
open-llm-leaderboard/details_Linly-AI__Chinese-LLaMA-2-7B-hf
--- pretty_name: Evaluation run of Linly-AI/Chinese-LLaMA-2-7B-hf dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Linly-AI/Chinese-LLaMA-2-7B-hf](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-hf)\ \ 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_Linly-AI__Chinese-LLaMA-2-7B-hf\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T10:13:08.335270](https://huggingface.co/datasets/open-llm-leaderboard/details_Linly-AI__Chinese-LLaMA-2-7B-hf/blob/main/results_2023-10-29T10-13-08.335270.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\": 0.20123741610738255,\n\ \ \"em_stderr\": 0.004105848061320724,\n \"f1\": 0.24458682885905994,\n\ \ \"f1_stderr\": 0.00409620440356687,\n \"acc\": 0.38191288394439116,\n\ \ \"acc_stderr\": 0.009754960327281063\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.20123741610738255,\n \"em_stderr\": 0.004105848061320724,\n\ \ \"f1\": 0.24458682885905994,\n \"f1_stderr\": 0.00409620440356687\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0621683093252464,\n \ \ \"acc_stderr\": 0.006651035644531703\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7016574585635359,\n \"acc_stderr\": 0.012858885010030425\n\ \ }\n}\n```" repo_url: https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-hf 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_01T14_35_23.324449 path: - '**/details_harness|arc:challenge|25_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-10-01T14-35-23.324449.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T10_13_08.335270 path: - '**/details_harness|drop|3_2023-10-29T10-13-08.335270.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T10-13-08.335270.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T10_13_08.335270 path: - '**/details_harness|gsm8k|5_2023-10-29T10-13-08.335270.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T10-13-08.335270.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hellaswag|10_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-23.324449.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-35-23.324449.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_10_01T14_35_23.324449 path: - '**/details_harness|truthfulqa:mc|0_2023-10-01T14-35-23.324449.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-10-01T14-35-23.324449.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T10_13_08.335270 path: - '**/details_harness|winogrande|5_2023-10-29T10-13-08.335270.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T10-13-08.335270.parquet' - config_name: results data_files: - split: 2023_10_01T14_35_23.324449 path: - results_2023-10-01T14-35-23.324449.parquet - split: 2023_10_29T10_13_08.335270 path: - results_2023-10-29T10-13-08.335270.parquet - split: latest path: - results_2023-10-29T10-13-08.335270.parquet --- # Dataset Card for Evaluation run of Linly-AI/Chinese-LLaMA-2-7B-hf ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-hf - **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 [Linly-AI/Chinese-LLaMA-2-7B-hf](https://huggingface.co/Linly-AI/Chinese-LLaMA-2-7B-hf) 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_Linly-AI__Chinese-LLaMA-2-7B-hf", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T10:13:08.335270](https://huggingface.co/datasets/open-llm-leaderboard/details_Linly-AI__Chinese-LLaMA-2-7B-hf/blob/main/results_2023-10-29T10-13-08.335270.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": 0.20123741610738255, "em_stderr": 0.004105848061320724, "f1": 0.24458682885905994, "f1_stderr": 0.00409620440356687, "acc": 0.38191288394439116, "acc_stderr": 0.009754960327281063 }, "harness|drop|3": { "em": 0.20123741610738255, "em_stderr": 0.004105848061320724, "f1": 0.24458682885905994, "f1_stderr": 0.00409620440356687 }, "harness|gsm8k|5": { "acc": 0.0621683093252464, "acc_stderr": 0.006651035644531703 }, "harness|winogrande|5": { "acc": 0.7016574585635359, "acc_stderr": 0.012858885010030425 } } ``` ### 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]
johnnyclee/chats
--- configs: - config_name: default data_files: - split: train path: '**/*.jsonl' --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
unaidedelf87777/SlimOrca-with-ids
--- dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: weight dtype: float64 splits: - name: train num_bytes: 930346157 num_examples: 517982 download_size: 469683669 dataset_size: 930346157 configs: - config_name: default data_files: - split: train path: data/train-* ---
DEplain/DEplain-APA-sent
--- annotations_creators: - expert-generated language: - de language_creators: - expert-generated license: - other multilinguality: - translation - monolingual pretty_name: DEplain-APA-sent size_categories: - 10K<n<100K source_datasets: - extended|DEplain-APA-doc tags: - sentence simplification - web-text - plain language - easy-to-read language task_categories: - text2text-generation task_ids: - text-simplification --- # DEplain-APA-sent: A corpus for German Sentence Simplification DEplain-APA-sent is a subcorpus of DEplain [Stodden et al., 2023]((https://arxiv.org/abs/2305.18939)) for evaluation of sentence simplification. The corpus consists of 13,122 manual-aligned sentence pairs of 483 parallel documents of the Austrian Press Agency (APA) in German written for people with CEFR level B1 (plain language) and for people with CEFR level A2 (plain language). The data of APA (Austrian Press Agency) is restricted for non-commercial research purposes. To get access to DEplain-APA please request the access via zenodo (https://zenodo.org/record/7674560). Human annotators sentence-wise aligned the 483 documents to build a corpus for sentence simplification. For the document-level version of this corpus, please see [https://huggingface.co/datasets/DEplain/DEplain-APA-doc](https://huggingface.co/datasets/DEplain/DEplain-APA-doc). ## Dataset Card for DEplain-APA-sent ### 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 - **Repository:** [DEplain-APA zenodo repository](https://zenodo.org/record/7674560) - **Paper:** ["DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification."](https://arxiv.org/abs/2305.18939) - **Point of Contact:** [Regina Stodden](regina.stodden@hhu.de) #### Dataset Summary DEplain-APA [(Stodden et al., 2023)](https://arxiv.org/abs/2305.18939) is a dataset for the training and evaluation of sentence and document simplification in German. All texts of this dataset are provided by the Austrian Press Agency. The simple-complex sentence pairs are manually aligned. #### Supported Tasks and Leaderboards The dataset supports the training and evaluation of `text-simplification` systems. Success in this task is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf). #### Languages The text in this dataset is in Austrian German (`de-at`). #### Domains All texts in this dataset are news data. ## Dataset Structure #### Data Access - The dataset is licensed with restricted access for only academic purposes. To download the dataset, please request access on [zenodo](https://zenodo.org/record/7674560). #### Data Instances - `document-simplification` configuration: an instance consists of an original document and one reference simplification (in plain-text format). - `sentence-simplification` configuration: an instance consists of original sentence(s) and one manually aligned reference simplification (inclusing one or more sentences). #### Data Fields | data field | data field description | |-------------------------------------------------|-------------------------------------------------------------------------------------------------------| | `original` | an original text from the source dataset | | `simplification` | a simplified text from the source dataset | | `pair_id` | document pair id | | `complex_document_id ` (on doc-level) | id of complex document (-1) | | `simple_document_id ` (on doc-level) | id of simple document (-0) | | `original_id ` (on sent-level) | id of sentence(s) of the original text | | `simplification_id ` (on sent-level) | id of sentence(s) of the simplified text | | `domain ` | text domain of the document pair | | `corpus ` | subcorpus name | | `simple_url ` | origin URL of the simplified document | | `complex_url ` | origin URL of the simplified document | | `simple_level ` or `language_level_simple ` | required CEFR language level to understand the simplified document | | `complex_level ` or `language_level_original ` | required CEFR language level to understand the original document | | `simple_location_html ` | location on hard disk where the HTML file of the simple document is stored | | `complex_location_html ` | location on hard disk where the HTML file of the original document is stored | | `simple_location_txt ` | location on hard disk where the content extracted from the HTML file of the simple document is stored | | `complex_location_txt ` | location on hard disk where the content extracted from the HTML file of the simple document is stored | | `alignment_location ` | location on hard disk where the alignment is stored | | `simple_author ` | author (or copyright owner) of the simplified document | | `complex_author ` | author (or copyright owner) of the original document | | `simple_title ` | title of the simplified document | | `complex_title ` | title of the original document | | `license ` | license of the data | | `last_access ` or `access_date` | data origin data or data when the HTML files were downloaded | | `rater` | id of the rater who annotated the sentence pair | | `alignment` | type of alignment, e.g., 1:1, 1:n, n:1 or n:m | #### Data Splits DEplain-APA is randomly split into a training, development and test set. The training set of the sentence-simplification configuration contains only texts of documents which are part of the training set of document-simplification configuration and the same for dev and test sets. The statistics are given below. | | Train | Dev | Test | Total | | ----- | ------ | ------ | ---- | ----- | | Document Pairs | 387 | 48 | 48 |483 | | Sentence Pairs | 10660 | 1231 | 1231 | 13122| Inter-Annotator-Agreement: 0.7497 (moderate). Here, more information on simplification operations will follow soon. ### Dataset Creation #### Curation Rationale DEplain-APA was created to improve the training and evaluation of German document and sentence simplification. The data is provided by the same data provided as for the APA-LHA data. In comparison to APA-LHA (automatic-aligned), the sentence pairs of DEplain-APA are all manually aligned. Further, DEplain-APA aligns the texts in language level B1 with the texts in A2, which result in mostly mild simplifications. Further DEplain-APA, contains parallel documents as well as parallel sentence pairs. #### Source Data ##### Initial Data Collection and Normalization The original news texts (in CEFR level B2) were manually simplified by professional translators, i.e. capito – CFS GmbH, and provided to us by the Austrian Press Agency. All documents date back to 2019 to 2021. Two German native speakers have manually aligned the sentence pairs by using the text simplification annotation tool TS-ANNO. The data was split into sentences using a German model of SpaCy. ##### Who are the source language producers? The original news texts (in CEFR level B2) were manually simplified by professional translators, i.e. capito – CFS GmbH. No other demographic or compensation information is known. #### Annotations ##### Annotation process The instructions given to the annotators are available [here](https://github.com/rstodden/TS_annotation_tool/tree/master/annotation_schema). ##### Who are the annotators? The annotators are two German native speakers, who are trained in linguistics. Both were at least compensated with the minimum wage of their country of residence. They are not part of any target group of text simplification. #### Personal and Sensitive Information No sensitive data. ### Considerations for Using the Data #### Social Impact of Dataset Many people do not understand texts due to their complexity. With automatic text simplification methods, the texts can be simplified for them. Our new training data can benefit in training a TS model. #### Discussion of Biases No bias is known. #### Other Known Limitations The dataset is provided for research purposes only. Please check the dataset license for additional information. ### Additional Information #### Dataset Curators Researchers at the Heinrich-Heine-University Düsseldorf, Germany, developed DEplain-APA. This research is part of the PhD-program `Online Participation` supported by the North Rhine-Westphalian (German) funding scheme `Forschungskolleg`. #### Licensing Information The dataset (DEplain-APA) is provided for research purposes only. Please request access using the following form: [https://zenodo.org/record/7674560](https://zenodo.org/record/7674560). #### Citation Information If you use part of this work, please cite our paper: ``` @inproceedings{stodden-etal-2023-deplain, title = "{DE}-plain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification", author = "Stodden, Regina and Momen, Omar and Kallmeyer, Laura", booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", notes = "preprint: https://arxiv.org/abs/2305.18939", } ``` This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r) and [Yacine Jernite](https://github.com/yjernite).
lonestar108/naughty-chat
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: input dtype: string splits: - name: train num_bytes: 80492 num_examples: 266 download_size: 21186 dataset_size: 80492 --- # Dataset Card for "naughty-chat" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saibo/bookcorpus_compact_1024_shard9_of_10
--- dataset_info: features: - name: text dtype: string - name: concept_with_offset dtype: string splits: - name: train num_bytes: 754029555 num_examples: 61605 download_size: 379859859 dataset_size: 754029555 --- # Dataset Card for "bookcorpus_compact_1024_shard9_of_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_mnli_possessives_for_pre
--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev_matched num_bytes: 773174 num_examples: 3253 - name: dev_mismatched num_bytes: 934287 num_examples: 3840 - name: test_matched num_bytes: 783981 num_examples: 3286 - name: test_mismatched num_bytes: 947271 num_examples: 3877 - name: train num_bytes: 31700435 num_examples: 132019 download_size: 22887147 dataset_size: 35139148 --- # Dataset Card for "MULTI_VALUE_mnli_possessives_for_pre" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mihaien/ads_dataset
--- dataset_info: features: - name: image dtype: image - name: metaphor dtype: string - name: visual_elaboration dtype: string splits: - name: train num_bytes: 5821963.0 num_examples: 243 download_size: 5813008 dataset_size: 5821963.0 configs: - config_name: default data_files: - split: train path: data/train-* ---