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ADHIZ/ghh

ADHIZ

--- dataset_info: features: - name: year dtype: int64 - name: industry_code_ANZSIC dtype: string - name: industry_name_ANZSIC dtype: string - name: rme_size_grp dtype: string - name: variable dtype: string - name: value dtype: string - name: unit dtype: string splits: - name: train num_bytes: 2151896 num_examples: 20124 download_size: 173304 dataset_size: 2151896 configs: - config_name: default data_files: - split: train path: data/train-* ---

ADHIZ/ghh34r5

ADHIZ

--- dataset_info: features: - name: code_language dtype: string - name: code dtype: string - name: answer dtype: string splits: - name: train num_bytes: 202 num_examples: 2 download_size: 2217 dataset_size: 202 configs: - config_name: default data_files: - split: train path: data/train-* ---

dnth/pixmo-cap-qa-images-chunk-0

dnth

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: image dtype: image splits: - name: train num_bytes: 831484746.516 num_examples: 942 download_size: 480669579 dataset_size: 831484746.516 configs: - config_name: default data_files: - split: train path: data/train-* ---

EnKop/primal-chaos

EnKop

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

mathildebindslev/MiniProjectML

mathildebindslev

--- task_categories: - audio-classification tags: - trash - audio --- This dataset is designed for training an audio classification model that identifies the type of trash being thrown into a bucket. The model classifies sounds into the following categories: Metal, Glass, Plastic, Cardboard, and Noise (non-trash-related sounds). The dataset was recorded and organized as part of an Edge Impulse project to create a system that sorts trash based on sound. Link to Edge Impulse: https://studio.edgeimpulse.com/public/556872/live

DT4LM/albertbase_mr_faster-alzantot_differential

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 44372.732447817834 num_examples: 337 download_size: 33583 dataset_size: 44372.732447817834 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3ba_mr_faster-alzantot_differential

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 29331.135135135137 num_examples: 226 download_size: 22921 dataset_size: 29331.135135135137 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3ba_mr_leap_differential

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 52885.45275590551 num_examples: 405 download_size: 37426 dataset_size: 52885.45275590551 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3ba_mr_leap_differential_original

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 52121.69291338583 num_examples: 405 download_size: 36193 dataset_size: 52121.69291338583 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3ba_mr_faster-alzantot_differential_original

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 28956.87469287469 num_examples: 226 download_size: 21990 dataset_size: 28956.87469287469 configs: - config_name: default data_files: - split: train path: data/train-* ---

Ngochuyen2504/jenny-tts-6h-descriptions-v1

Ngochuyen2504

--- dataset_info: features: - name: file_name dtype: string - name: text dtype: string - name: transcription_normalised dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string - name: text_description dtype: string splits: - name: train num_bytes: 2668518 num_examples: 4000 download_size: 1223613 dataset_size: 2668518 configs: - config_name: default data_files: - split: train path: data/train-* ---

davidberenstein1957/daily-papers-docling-full-dataset

davidberenstein1957

--- dataset_info: features: - name: id dtype: string - name: tags sequence: 'null' - name: properties dtype: 'null' - name: error dtype: 'null' - name: raw_response dtype: string - name: version dtype: string - name: mime_type dtype: string - name: label dtype: string - name: filename dtype: string - name: page_no dtype: int64 - name: mimetype dtype: string - name: dpi dtype: int64 - name: width dtype: int64 - name: height dtype: int64 - name: text dtype: string - name: text_length dtype: int64 - name: synced_at dtype: 'null' - name: file_name dtype: image splits: - name: train num_bytes: 744487980.33 num_examples: 14165 download_size: 657766213 dataset_size: 744487980.33 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/albertbasev2_agnews_leap

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 170904 num_examples: 681 download_size: 122381 dataset_size: 170904 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/albertbasev2_agnews_leap_original

DT4LM

--- dataset_info: features: - name: text dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 165950 num_examples: 681 download_size: 115911 dataset_size: 165950 configs: - config_name: default data_files: - split: train path: data/train-* ---

Nash-pAnDiTa/youssef-science-street-peroxide

Nash-pAnDiTa

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 152301488.0 num_examples: 15 download_size: 152136065 dataset_size: 152301488.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

ylacombe/peoples_speech-tags-annotated

ylacombe

--- dataset_info: config_name: clean features: - name: id dtype: string - name: duration_ms dtype: int32 - name: text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string splits: - name: train num_bytes: 1151010413 num_examples: 1501271 - name: validation num_bytes: 9529506 num_examples: 18622 - name: test num_bytes: 17609193 num_examples: 34898 download_size: 490187975 dataset_size: 1178149112 configs: - config_name: clean data_files: - split: train path: clean/train-* - split: validation path: clean/validation-* - split: test path: clean/test-* ---

Tobius/f667011f-cd37-4dd8-9bc7-c5e95dc10170

Tobius

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 108838.4 num_examples: 800 - name: test num_bytes: 27209.6 num_examples: 200 download_size: 11886 dataset_size: 136048.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

kite4869xc/waymo

kite4869xc

--- license: apache-2.0 ---

kite4869xc/waymo_dataset

kite4869xc

--- license: apache-2.0 ---

Nash-pAnDiTa/youssef-Using-ChatGPT-to-learn-programming

Nash-pAnDiTa

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 169594818.0 num_examples: 16 download_size: 154450733 dataset_size: 169594818.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3base_rte_leap

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 58605 num_examples: 190 download_size: 47788 dataset_size: 58605 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3base_rte_leap_original

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 58190 num_examples: 190 download_size: 44535 dataset_size: 58190 configs: - config_name: default data_files: - split: train path: data/train-* ---

Nash-pAnDiTa/youssef-NobleInMedicine2024

Nash-pAnDiTa

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string splits: - name: train num_bytes: 233672373.0 num_examples: 22 download_size: 230946683 dataset_size: 233672373.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Tobius/e2f42034-1079-4a1e-996d-3e81ae5c78f3

Tobius

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 108838.4 num_examples: 800 - name: test num_bytes: 27209.6 num_examples: 200 download_size: 12123 dataset_size: 136048.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

DigiGreen/Kenya_Agri_queries

DigiGreen

--- license: apache-2.0 task_categories: - question-answering language: - en tags: - agriculture - farming - farmerq-a - farmer_queries size_categories: - 100K<n<1M --- This is the dataset of farmer queries (anonymised) from Kenya on version 1 of farmer.chat (Telegram bot). The data is generated through the use of bot over a period of 10 months from September 2023 tillJune 2024.

hugosenet/request_denial_evaluation_of_responses_of_a_restrained_model_test

hugosenet

--- language_creators: - machine-generated language: - en license: apache-2.0 multilinguality: - monolingual size_categories: - 1K1T source_datasets: - extended task_categories: - text-generation pretty_name: Request denial evaluation of responses to illicit queries with a restrained model without primers ---

chaichangkun/so100_grasp_cube

chaichangkun

--- task_categories: - robotics tags: - LeRobot - so100 - grasp_cube --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

rhfeiyang/Art-Free-SAM

rhfeiyang

--- task_categories: - text-to-image tags: - Art-Free --- Our Art-Free-SAM contains the ids from original SA-1B dataset [here](https://ai.meta.com/datasets/segment-anything-downloads/). We used the captions from [SAM-LLaVA-Captions10M](https://huggingface.co/datasets/PixArt-alpha/SAM-LLaVA-Captions10M/tree/main) The folder structure should be like: ``` sam_dataset ├── captions │ ├── 0.txt │ ├── 1.txt │ └── ... ├── images │ ├── sa_000000 │ ├── 0.jpg │ ├── 1.jpg │ └── ... │ ├── sa_000001 │ ├── 0.jpg │ ├── 1.jpg │ └── ... │ ├── ... │ └── sa_000999 └── ``` Download our [id_dict.pickle](https://huggingface.co/datasets/rhfeiyang/Art-Free-SAM/blob/main/id_dict.pickle) and [art-free-sam-loader.py](https://huggingface.co/datasets/rhfeiyang/Art-Free-SAM/blob/main/art-free-sam-loader.py), and [ids_train.pickle](https://huggingface.co/datasets/rhfeiyang/Art-Free-SAM/blob/main/ids_train.pickle), you can load the dataset by: ```python from art_free_sam_loader import SamDataset art_free_sam = SamDataset(image_folder_path=<path-to-sam-images>, caption_folder_path=<path-to-captios>, id_file= <path-to-ids>,id_dict_file=<path-to-id_dict>) ```

DT4LM/debertav3base_rte_clare

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 66539 num_examples: 212 download_size: 51207 dataset_size: 66539 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/debertav3base_rte_clare_original

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 65807 num_examples: 212 download_size: 49805 dataset_size: 65807 configs: - config_name: default data_files: - split: train path: data/train-* ---

oshvartz/so100_test

oshvartz

--- task_categories: - robotics tags: - LeRobot - so100 - tutorial --- This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

ferrazzipietro/tmp_results

ferrazzipietro

--- dataset_info: features: - name: sentence dtype: string - name: entities list: - name: offsets sequence: int64 - name: text dtype: string - name: type dtype: string - name: tokens sequence: string - name: ner_tags sequence: int64 - name: ground_truth_word_level sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: predictions sequence: string - name: ground_truth_labels sequence: string splits: - name: all_validation num_bytes: 172478 num_examples: 94 - name: test num_bytes: 1556215 num_examples: 738 download_size: 308569 dataset_size: 1728693 configs: - config_name: default data_files: - split: all_validation path: data/all_validation-* - split: test path: data/test-* ---

Ngochuyen2504/infore1_25hours-tags-v1

Ngochuyen2504

--- dataset_info: features: - name: text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: stoi dtype: float64 - name: si-sdr dtype: float64 - name: pesq dtype: float64 - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: sdr_noise dtype: string - name: pesq_speech_quality dtype: string splits: - name: train num_bytes: 4740783 num_examples: 14935 download_size: 2013880 dataset_size: 4740783 configs: - config_name: default data_files: - split: train path: data/train-* ---

hafizasania/duet_transport_chatbot

hafizasania

--- license: apache-2.0 ---

open-llm-leaderboard/Qwen__QwQ-32B-Preview-details

open-llm-leaderboard

--- pretty_name: Evaluation run of Qwen/QwQ-32B-Preview dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview)\nThe dataset\ \ is composed of 38 configuration(s), each one corresponding 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.\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(\n\t\"open-llm-leaderboard/Qwen__QwQ-32B-Preview-details\"\ ,\n\tname=\"Qwen__QwQ-32B-Preview__leaderboard_bbh_boolean_expressions\",\n\tsplit=\"\ latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results from run\ \ 2024-11-29T18-47-19.440839](https://huggingface.co/datasets/open-llm-leaderboard/Qwen__QwQ-32B-Preview-details/blob/main/Qwen__QwQ-32B-Preview/results_2024-11-29T18-47-19.440839.json)\ \ (note that there 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 \"leaderboard\": {\n\ \ \"acc,none\": 0.5678191489361702,\n \"acc_stderr,none\"\ : 0.004516342962611267,\n \"inst_level_strict_acc,none\": 0.46882494004796166,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"acc_norm,none\"\ : 0.581787521079258,\n \"acc_norm_stderr,none\": 0.004984831150161566,\n\ \ \"inst_level_loose_acc,none\": 0.4880095923261391,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.3567467652495379,\n \"prompt_level_loose_acc_stderr,none\": 0.020614562936479897,\n\ \ \"prompt_level_strict_acc,none\": 0.33826247689463956,\n \ \ \"prompt_level_strict_acc_stderr,none\": 0.020359772138166046,\n \"\ exact_match,none\": 0.22885196374622357,\n \"exact_match_stderr,none\"\ : 0.010715465924617387,\n \"alias\": \"leaderboard\"\n },\n \ \ \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.6663773650407915,\n\ \ \"acc_norm_stderr,none\": 0.005642651971847929,\n \"alias\"\ : \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.92,\n \"acc_norm_stderr,none\": 0.017192507941463025\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.6470588235294118,\n\ \ \"acc_norm_stderr,none\": 0.03504019983419238\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.716,\n \"acc_norm_stderr,none\":\ \ 0.028576958730437443\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.76,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.828,\n\ \ \"acc_norm_stderr,none\": 0.02391551394448624\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.58,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \"\ \ - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\": 0.856,\n \ \ \"acc_norm_stderr,none\": 0.022249407735450245\n },\n \"\ leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\": \" \ \ - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.62,\n \"acc_norm_stderr,none\": 0.030760116042626098\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.884,\n \"acc_norm_stderr,none\": 0.020293429803083823\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.744,\n \"acc_norm_stderr,none\": 0.027657108718204846\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.776,\n \"acc_norm_stderr,none\":\ \ 0.026421361687347884\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.7191780821917808,\n \"acc_norm_stderr,none\": 0.037320694849458984\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.808,\n \"acc_norm_stderr,none\": 0.02496069198917196\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.832,\n \ \ \"acc_norm_stderr,none\": 0.023692813205492536\n },\n \"\ leaderboard_bbh_salient_translation_error_detection\": {\n \"alias\"\ : \" - leaderboard_bbh_salient_translation_error_detection\",\n \"acc_norm,none\"\ : 0.664,\n \"acc_norm_stderr,none\": 0.029933259094191533\n },\n\ \ \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.8370786516853933,\n \"acc_norm_stderr,none\"\ : 0.02775782910660744\n },\n \"leaderboard_bbh_sports_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \ \ \"acc_norm,none\": 0.748,\n \"acc_norm_stderr,none\": 0.027513851933031318\n\ \ },\n \"leaderboard_bbh_temporal_sequences\": {\n \"alias\"\ : \" - leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.836,\n\ \ \"acc_norm_stderr,none\": 0.023465261002076715\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.256,\n \"acc_norm_stderr,none\": 0.027657108718204846\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.192,\n \"acc_norm_stderr,none\":\ \ 0.024960691989171963\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.32,\n \"acc_norm_stderr,none\": 0.029561724955240978\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\":\ \ \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\": 0.556,\n\ \ \"acc_norm_stderr,none\": 0.03148684942554571\n },\n \ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2818791946308725,\n\ \ \"acc_norm_stderr,none\": 0.013046291338577345,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2777777777777778,\n \"acc_norm_stderr,none\": 0.03191178226713548\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.2893772893772894,\n\ \ \"acc_norm_stderr,none\": 0.019424663872261782\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.27455357142857145,\n \"acc_norm_stderr,none\"\ : 0.021108747290633768\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.33826247689463956,\n \"prompt_level_strict_acc_stderr,none\": 0.020359772138166046,\n\ \ \"inst_level_strict_acc,none\": 0.46882494004796166,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.3567467652495379,\n \"prompt_level_loose_acc_stderr,none\": 0.020614562936479897,\n\ \ \"inst_level_loose_acc,none\": 0.4880095923261391,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.22885196374622357,\n \"exact_match_stderr,none\"\ : 0.010715465924617387,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.4006514657980456,\n\ \ \"exact_match_stderr,none\": 0.028013177848580824\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.23577235772357724,\n \"exact_match_stderr,none\": 0.03843066495214836\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.09090909090909091,\n\ \ \"exact_match_stderr,none\": 0.0251172256361608\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\": \"\ \ - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.05357142857142857,\n \"exact_match_stderr,none\": 0.01348057551341636\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.21428571428571427,\n\ \ \"exact_match_stderr,none\": 0.03317288314377314\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.44041450777202074,\n \"exact_match_stderr,none\"\ : 0.035827245300360966\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.044444444444444446,\n \"exact_match_stderr,none\"\ : 0.01780263602032457\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.5678191489361702,\n\ \ \"acc_stderr,none\": 0.004516342962611267\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.41005291005291006,\n \"acc_norm_stderr,none\"\ : 0.017653759371565242,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.488,\n\ \ \"acc_norm_stderr,none\": 0.03167708558254714\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.29296875,\n \"acc_norm_stderr,none\"\ : 0.028500984607927556\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.452,\n \"acc_norm_stderr,none\": 0.03153986449255664\n\ \ }\n },\n \"leaderboard\": {\n \"acc,none\": 0.5678191489361702,\n\ \ \"acc_stderr,none\": 0.004516342962611267,\n \"inst_level_strict_acc,none\"\ : 0.46882494004796166,\n \"inst_level_strict_acc_stderr,none\": \"N/A\",\n\ \ \"acc_norm,none\": 0.581787521079258,\n \"acc_norm_stderr,none\"\ : 0.004984831150161566,\n \"inst_level_loose_acc,none\": 0.4880095923261391,\n\ \ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.3567467652495379,\n \"prompt_level_loose_acc_stderr,none\": 0.020614562936479897,\n\ \ \"prompt_level_strict_acc,none\": 0.33826247689463956,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.020359772138166046,\n \"exact_match,none\": 0.22885196374622357,\n \ \ \"exact_match_stderr,none\": 0.010715465924617387,\n \"alias\": \"\ leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\": 0.6663773650407915,\n\ \ \"acc_norm_stderr,none\": 0.005642651971847929,\n \"alias\": \"\ \ - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\": {\n\ \ \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\"\ : 0.92,\n \"acc_norm_stderr,none\": 0.017192507941463025\n },\n \"\ leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6470588235294118,\n \"acc_norm_stderr,none\"\ : 0.03504019983419238\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.716,\n \"acc_norm_stderr,none\": 0.028576958730437443\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.76,\n \"acc_norm_stderr,none\": 0.027065293652238982\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.828,\n \"acc_norm_stderr,none\": 0.02391551394448624\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.58,\n \"acc_norm_stderr,none\": 0.03127799950463661\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.856,\n \"acc_norm_stderr,none\": 0.022249407735450245\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.62,\n \"acc_norm_stderr,none\": 0.030760116042626098\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.496,\n \"acc_norm_stderr,none\": 0.0316851985511992\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.884,\n \"acc_norm_stderr,none\": 0.020293429803083823\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.744,\n \"acc_norm_stderr,none\": 0.027657108718204846\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.776,\n \"acc_norm_stderr,none\": 0.026421361687347884\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.7191780821917808,\n\ \ \"acc_norm_stderr,none\": 0.037320694849458984\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.808,\n \"acc_norm_stderr,none\": 0.02496069198917196\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.832,\n \"acc_norm_stderr,none\": 0.023692813205492536\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.664,\n \"acc_norm_stderr,none\": 0.029933259094191533\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.8370786516853933,\n \"acc_norm_stderr,none\"\ : 0.02775782910660744\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.748,\n \"acc_norm_stderr,none\": 0.027513851933031318\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.836,\n \"acc_norm_stderr,none\": 0.023465261002076715\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.256,\n \"acc_norm_stderr,none\": 0.027657108718204846\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.192,\n \"acc_norm_stderr,none\": 0.024960691989171963\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.32,\n \"acc_norm_stderr,none\": 0.029561724955240978\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.556,\n \"acc_norm_stderr,none\": 0.03148684942554571\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.2818791946308725,\n\ \ \"acc_norm_stderr,none\": 0.013046291338577345,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2777777777777778,\n\ \ \"acc_norm_stderr,none\": 0.03191178226713548\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.2893772893772894,\n \"acc_norm_stderr,none\": 0.019424663872261782\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.27455357142857145,\n \"acc_norm_stderr,none\"\ : 0.021108747290633768\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.33826247689463956,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.020359772138166046,\n \ \ \"inst_level_strict_acc,none\": 0.46882494004796166,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.3567467652495379,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.020614562936479897,\n \"inst_level_loose_acc,none\"\ : 0.4880095923261391,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.22885196374622357,\n\ \ \"exact_match_stderr,none\": 0.010715465924617387,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.4006514657980456,\n \"exact_match_stderr,none\": 0.028013177848580824\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.23577235772357724,\n \"exact_match_stderr,none\": 0.03843066495214836\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.09090909090909091,\n \"exact_match_stderr,none\"\ : 0.0251172256361608\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.05357142857142857,\n \"exact_match_stderr,none\"\ : 0.01348057551341636\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.21428571428571427,\n \"exact_match_stderr,none\": 0.03317288314377314\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.44041450777202074,\n \"exact_match_stderr,none\"\ : 0.035827245300360966\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.044444444444444446,\n \"exact_match_stderr,none\": 0.01780263602032457\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.5678191489361702,\n \"acc_stderr,none\": 0.004516342962611267\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.41005291005291006,\n\ \ \"acc_norm_stderr,none\": 0.017653759371565242,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.29296875,\n \"acc_norm_stderr,none\": 0.028500984607927556\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.452,\n \"acc_norm_stderr,none\": 0.03153986449255664\n\ \ }\n}\n```" repo_url: https://huggingface.co/Qwen/QwQ-32B-Preview leaderboard_url: '' point_of_contact: '' configs: - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_date_understanding data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_navigate data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_navigate_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_object_counting data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_ruin_names data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_snarks data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_snarks_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_gpqa_diamond data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_gpqa_extended data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_gpqa_extended_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_gpqa_main data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_gpqa_main_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_ifeval data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_ifeval_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_math_algebra_hard data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_math_geometry_hard data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_math_num_theory_hard data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_math_precalculus_hard data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_mmlu_pro data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_mmlu_pro_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_musr_object_placements data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_musr_object_placements_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-29T18-47-19.440839.jsonl' - config_name: Qwen__QwQ-32B-Preview__leaderboard_musr_team_allocation data_files: - split: 2024_11_29T18_47_19.440839 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-29T18-47-19.440839.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-29T18-47-19.440839.jsonl' --- # Dataset Card for Evaluation run of Qwen/QwQ-32B-Preview <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) The dataset is composed of 38 configuration(s), each one corresponding 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. 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/Qwen__QwQ-32B-Preview-details", name="Qwen__QwQ-32B-Preview__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-29T18-47-19.440839](https://huggingface.co/datasets/open-llm-leaderboard/Qwen__QwQ-32B-Preview-details/blob/main/Qwen__QwQ-32B-Preview/results_2024-11-29T18-47-19.440839.json) (note that there 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": { "leaderboard": { "acc,none": 0.5678191489361702, "acc_stderr,none": 0.004516342962611267, "inst_level_strict_acc,none": 0.46882494004796166, "inst_level_strict_acc_stderr,none": "N/A", "acc_norm,none": 0.581787521079258, "acc_norm_stderr,none": 0.004984831150161566, "inst_level_loose_acc,none": 0.4880095923261391, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.3567467652495379, "prompt_level_loose_acc_stderr,none": 0.020614562936479897, "prompt_level_strict_acc,none": 0.33826247689463956, "prompt_level_strict_acc_stderr,none": 0.020359772138166046, "exact_match,none": 0.22885196374622357, "exact_match_stderr,none": 0.010715465924617387, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.6663773650407915, "acc_norm_stderr,none": 0.005642651971847929, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.92, "acc_norm_stderr,none": 0.017192507941463025 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6470588235294118, "acc_norm_stderr,none": 0.03504019983419238 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.76, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.828, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.58, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.856, "acc_norm_stderr,none": 0.022249407735450245 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.62, "acc_norm_stderr,none": 0.030760116042626098 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.884, "acc_norm_stderr,none": 0.020293429803083823 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.744, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.776, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.7191780821917808, "acc_norm_stderr,none": 0.037320694849458984 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.808, "acc_norm_stderr,none": 0.02496069198917196 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.832, "acc_norm_stderr,none": 0.023692813205492536 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.664, "acc_norm_stderr,none": 0.029933259094191533 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.8370786516853933, "acc_norm_stderr,none": 0.02775782910660744 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.748, "acc_norm_stderr,none": 0.027513851933031318 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.836, "acc_norm_stderr,none": 0.023465261002076715 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.256, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.192, "acc_norm_stderr,none": 0.024960691989171963 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.32, "acc_norm_stderr,none": 0.029561724955240978 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.556, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_gpqa": { "acc_norm,none": 0.2818791946308725, "acc_norm_stderr,none": 0.013046291338577345, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2777777777777778, "acc_norm_stderr,none": 0.03191178226713548 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2893772893772894, "acc_norm_stderr,none": 0.019424663872261782 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.27455357142857145, "acc_norm_stderr,none": 0.021108747290633768 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.33826247689463956, "prompt_level_strict_acc_stderr,none": 0.020359772138166046, "inst_level_strict_acc,none": 0.46882494004796166, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.3567467652495379, "prompt_level_loose_acc_stderr,none": 0.020614562936479897, "inst_level_loose_acc,none": 0.4880095923261391, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.22885196374622357, "exact_match_stderr,none": 0.010715465924617387, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.4006514657980456, "exact_match_stderr,none": 0.028013177848580824 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.23577235772357724, "exact_match_stderr,none": 0.03843066495214836 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.09090909090909091, "exact_match_stderr,none": 0.0251172256361608 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.05357142857142857, "exact_match_stderr,none": 0.01348057551341636 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.21428571428571427, "exact_match_stderr,none": 0.03317288314377314 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.44041450777202074, "exact_match_stderr,none": 0.035827245300360966 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.044444444444444446, "exact_match_stderr,none": 0.01780263602032457 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.5678191489361702, "acc_stderr,none": 0.004516342962611267 }, "leaderboard_musr": { "acc_norm,none": 0.41005291005291006, "acc_norm_stderr,none": 0.017653759371565242, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.29296875, "acc_norm_stderr,none": 0.028500984607927556 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.452, "acc_norm_stderr,none": 0.03153986449255664 } }, "leaderboard": { "acc,none": 0.5678191489361702, "acc_stderr,none": 0.004516342962611267, "inst_level_strict_acc,none": 0.46882494004796166, "inst_level_strict_acc_stderr,none": "N/A", "acc_norm,none": 0.581787521079258, "acc_norm_stderr,none": 0.004984831150161566, "inst_level_loose_acc,none": 0.4880095923261391, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.3567467652495379, "prompt_level_loose_acc_stderr,none": 0.020614562936479897, "prompt_level_strict_acc,none": 0.33826247689463956, "prompt_level_strict_acc_stderr,none": 0.020359772138166046, "exact_match,none": 0.22885196374622357, "exact_match_stderr,none": 0.010715465924617387, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.6663773650407915, "acc_norm_stderr,none": 0.005642651971847929, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.92, "acc_norm_stderr,none": 0.017192507941463025 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6470588235294118, "acc_norm_stderr,none": 0.03504019983419238 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.716, "acc_norm_stderr,none": 0.028576958730437443 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.76, "acc_norm_stderr,none": 0.027065293652238982 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.828, "acc_norm_stderr,none": 0.02391551394448624 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.58, "acc_norm_stderr,none": 0.03127799950463661 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.856, "acc_norm_stderr,none": 0.022249407735450245 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.62, "acc_norm_stderr,none": 0.030760116042626098 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.496, "acc_norm_stderr,none": 0.0316851985511992 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.884, "acc_norm_stderr,none": 0.020293429803083823 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.744, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.776, "acc_norm_stderr,none": 0.026421361687347884 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.7191780821917808, "acc_norm_stderr,none": 0.037320694849458984 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.808, "acc_norm_stderr,none": 0.02496069198917196 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.832, "acc_norm_stderr,none": 0.023692813205492536 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.664, "acc_norm_stderr,none": 0.029933259094191533 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.8370786516853933, "acc_norm_stderr,none": 0.02775782910660744 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.748, "acc_norm_stderr,none": 0.027513851933031318 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.836, "acc_norm_stderr,none": 0.023465261002076715 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.256, "acc_norm_stderr,none": 0.027657108718204846 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.192, "acc_norm_stderr,none": 0.024960691989171963 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.32, "acc_norm_stderr,none": 0.029561724955240978 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.556, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_gpqa": { "acc_norm,none": 0.2818791946308725, "acc_norm_stderr,none": 0.013046291338577345, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2777777777777778, "acc_norm_stderr,none": 0.03191178226713548 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2893772893772894, "acc_norm_stderr,none": 0.019424663872261782 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.27455357142857145, "acc_norm_stderr,none": 0.021108747290633768 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.33826247689463956, "prompt_level_strict_acc_stderr,none": 0.020359772138166046, "inst_level_strict_acc,none": 0.46882494004796166, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.3567467652495379, "prompt_level_loose_acc_stderr,none": 0.020614562936479897, "inst_level_loose_acc,none": 0.4880095923261391, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.22885196374622357, "exact_match_stderr,none": 0.010715465924617387, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.4006514657980456, "exact_match_stderr,none": 0.028013177848580824 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.23577235772357724, "exact_match_stderr,none": 0.03843066495214836 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.09090909090909091, "exact_match_stderr,none": 0.0251172256361608 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.05357142857142857, "exact_match_stderr,none": 0.01348057551341636 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.21428571428571427, "exact_match_stderr,none": 0.03317288314377314 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.44041450777202074, "exact_match_stderr,none": 0.035827245300360966 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.044444444444444446, "exact_match_stderr,none": 0.01780263602032457 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.5678191489361702, "acc_stderr,none": 0.004516342962611267 }, "leaderboard_musr": { "acc_norm,none": 0.41005291005291006, "acc_norm_stderr,none": 0.017653759371565242, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.29296875, "acc_norm_stderr,none": 0.028500984607927556 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.452, "acc_norm_stderr,none": 0.03153986449255664 } } ``` ## 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. 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anastasiafrosted/endpoint0_300

anastasiafrosted

--- dataset_info: features: - name: n_invocations dtype: int64 - name: avg_loc dtype: float64 - name: avg_cyc_complexity dtype: float64 - name: avg_num_of_imports dtype: float64 - name: avg_argument_size dtype: float64 - name: e_type_LSFProvider dtype: int64 - name: e_type_CobaltProvider dtype: int64 - name: e_type_PBSProProvider dtype: int64 - name: e_type_LocalProvider dtype: int64 - name: e_type_KubernetesProvider dtype: int64 - name: e_type_SlurmProvider dtype: int64 - name: timestamp dtype: timestamp[us] splits: - name: train num_bytes: 2457600 num_examples: 25600 download_size: 596720 dataset_size: 2457600 configs: - config_name: default data_files: - split: train path: data/train-* ---

anastasiafrosted/endpoint2_30

anastasiafrosted

--- dataset_info: features: - name: n_invocations dtype: int64 - name: avg_loc dtype: float64 - name: avg_cyc_complexity dtype: float64 - name: avg_num_of_imports dtype: float64 - name: avg_argument_size dtype: float64 - name: e_type_LSFProvider dtype: int64 - name: e_type_CobaltProvider dtype: int64 - name: e_type_PBSProProvider dtype: int64 - name: e_type_LocalProvider dtype: int64 - name: e_type_KubernetesProvider dtype: int64 - name: e_type_SlurmProvider dtype: int64 - name: timestamp dtype: timestamp[us] splits: - name: train num_bytes: 3324384 num_examples: 34629 download_size: 392307 dataset_size: 3324384 configs: - config_name: default data_files: - split: train path: data/train-* ---

marcov/docred_promptsource

marcov

--- dataset_info: features: - name: title dtype: string - name: sents sequence: sequence: string - name: vertexSet list: list: - name: name dtype: string - name: sent_id dtype: int32 - name: pos sequence: int32 - name: type dtype: string - name: labels sequence: - name: head dtype: int32 - name: tail dtype: int32 - name: relation_id dtype: string - name: relation_text dtype: string - name: evidence sequence: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: validation num_bytes: 50528660.3252505 num_examples: 8941 - name: test num_bytes: 34870267.540125 num_examples: 7047 - name: train_annotated num_bytes: 153818832.47173274 num_examples: 27398 - name: train_distant num_bytes: 5037034967.125294 num_examples: 897390 download_size: 1755032299 dataset_size: 5276252727.462402 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* - split: train_annotated path: data/train_annotated-* - split: train_distant path: data/train_distant-* ---

marcov/web_questions_promptsource

marcov

--- dataset_info: features: - name: url dtype: string - name: question dtype: string - name: answers sequence: string - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 6322517.0 num_examples: 18890 - name: test num_bytes: 3423767.0 num_examples: 10160 download_size: 3256824 dataset_size: 9746284.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

marcov/glue_mrpc_promptsource

marcov

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': not_equivalent '1': equivalent - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 16113182.701978501 num_examples: 23288 - name: validation num_bytes: 1810423.9411764706 num_examples: 2598 - name: test num_bytes: 7530577.036604555 num_examples: 10919 download_size: 11786564 dataset_size: 25454183.679759525 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_qqp_promptsource

marcov

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: class_label: names: '0': not_duplicate '1': duplicate - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 1122875522.0 num_examples: 2183076 - name: validation num_bytes: 124744941.0 num_examples: 242580 - name: test num_bytes: 1212532028.0 num_examples: 2345790 download_size: 861173708 dataset_size: 2460152491.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_mnli_mismatched_promptsource

marcov

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: validation num_bytes: 91111255.0 num_examples: 147480 - name: test num_bytes: 91034428.0 num_examples: 147705 download_size: 74363557 dataset_size: 182145683.0 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_qnli_promptsource

marcov

--- dataset_info: features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 388467238.0 num_examples: 523715 - name: validation num_bytes: 20585393.0 num_examples: 27315 - name: test num_bytes: 20619773.0 num_examples: 27315 download_size: 191077076 dataset_size: 429672404.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_wnli_promptsource

marcov

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': not_entailment '1': entailment - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 1800360.0 num_examples: 3175 - name: validation num_bytes: 203141.0 num_examples: 355 - name: test num_bytes: 546936.0 num_examples: 730 download_size: 468652 dataset_size: 2550437.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_stsb_promptsource

marcov

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float32 - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 18511253.0 num_examples: 28745 - name: validation num_bytes: 5021140.0 num_examples: 7500 - name: test num_bytes: 4336388.0 num_examples: 6895 download_size: 7459539 dataset_size: 27868781.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_ax_promptsource

marcov

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: test num_bytes: 4630204.0 num_examples: 5520 download_size: 698656 dataset_size: 4630204.0 configs: - config_name: default data_files: - split: test path: data/test-* ---

marcov/glue_sst2_promptsource

marcov

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': negative '1': positive - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 121349382.0 num_examples: 336745 - name: validation num_bytes: 2027412.0 num_examples: 4360 - name: test num_bytes: 4185889.0 num_examples: 9105 download_size: 34100881 dataset_size: 127562683.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_cola_promptsource

marcov

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: class_label: names: '0': unacceptable '1': acceptable - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 17174176.0 num_examples: 42755 - name: validation num_bytes: 2106582.0 num_examples: 5215 - name: test num_bytes: 2137976.0 num_examples: 5315 download_size: 3422798 dataset_size: 21418734.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_rte_promptsource

marcov

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: class_label: names: '0': entailment '1': not_entailment - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 11829720.0 num_examples: 12450 - name: validation num_bytes: 1280676.0 num_examples: 1385 - name: test num_bytes: 13784530.0 num_examples: 15000 download_size: 12326416 dataset_size: 26894926.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

marcov/glue_mnli_promptsource

marcov

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: idx dtype: int32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 3542330327.0 num_examples: 5890530 - name: validation_matched num_bytes: 87592314.0 num_examples: 147225 - name: validation_mismatched num_bytes: 91111255.0 num_examples: 147480 - name: test_matched num_bytes: 87805424.0 num_examples: 146940 - name: test_mismatched num_bytes: 91034428.0 num_examples: 147705 download_size: 1658407677 dataset_size: 3899873748.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation_matched path: data/validation_matched-* - split: validation_mismatched path: data/validation_mismatched-* - split: test_matched path: data/test_matched-* - split: test_mismatched path: data/test_mismatched-* ---

FrancophonIA/Budget_Belgium

FrancophonIA

--- language: - fr - nl multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19222 ## Description This resource contains a translation memory (TMX) of the budget of Belgium in Dutch and French. It is based on the report of the 2018 budget and contains very relevant terminology. The donated resource contains 6 files: (1) begroting.tmx (= the aligned TMX) (2) Publicatie van de Algemene Uitgavenbegroting aangepaste 2018.xlsx (= the original file, also available online through Belgium's open data portal) (3) + (4) begroting_omschrijving_NL.txt and begroting_omschrijving_FR.txt (= the short descriptions of budget items in Dutch and French, with the same items per line) (5) + (6) begroting_lange_omschrijving_NL.txt and begroting_lange_omschrijving_FR.txt (= same as 3 & 4, but longer descriptions for the same items, also aligned). ## Citation ``` Budget Belgium (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19222 ```

FrancophonIA/Coalition_Agreement_Belgium_2014

FrancophonIA

--- language: - fr - nl multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19269 ## Description This resource is a Dutch-French translation memory (TMX) created from Belgium's 2014 coalition agreement and also includes the original (aligned) files as txts. ## Citation ``` Coalition Agreement Belgium 2014 (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19269 ```

saurabhy27-outcomes/finetune_speech_corpus_1111

saurabhy27-outcomes

--- dataset_info: - config_name: en features: - name: term dtype: string - name: text dtype: string - name: voice dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 1154959.2 num_examples: 8 - name: test num_bytes: 288739.8 num_examples: 2 download_size: 1410263 dataset_size: 1443699.0 - config_name: zn features: - name: term dtype: string - name: text dtype: string - name: voice dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 1154959.2 num_examples: 8 - name: test num_bytes: 288739.8 num_examples: 2 download_size: 1420991 dataset_size: 1443699.0 configs: - config_name: en data_files: - split: train path: en/train-* - split: test path: en/test-* - config_name: zn data_files: - split: train path: zn/train-* - split: test path: zn/test-* ---

FrancophonIA/Constituicao_da_Republica_Portuguesa

FrancophonIA

--- language: - en - fr - pt multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19368 ## Citation ``` Constituição da República Portuguesa (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19368 ```

marcov/swag_regular_promptsource

marcov

--- dataset_info: features: - name: video-id dtype: string - name: fold-ind dtype: string - name: startphrase dtype: string - name: sent1 dtype: string - name: sent2 dtype: string - name: gold-source dtype: string - name: ending0 dtype: string - name: ending1 dtype: string - name: ending2 dtype: string - name: ending3 dtype: string - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 461680074.0 num_examples: 514822 - name: validation num_bytes: 127975226.0 num_examples: 140042 - name: test num_bytes: 127584122.0 num_examples: 140035 download_size: 254052350 dataset_size: 717239422.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

FrancophonIA/Praias_2007

FrancophonIA

--- language: - de - es - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19401 ## Citation ``` Praias 2007 (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19401 ```

marcov/biosses_promptsource

marcov

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float32 - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 1055334.0 num_examples: 1100 download_size: 213914 dataset_size: 1055334.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

FrancophonIA/Museus_2007

FrancophonIA

--- language: - de - es - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19402 ## Citation ``` Museus 2007 (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19402 ```

FrancophonIA/Artigos_visitportugal_2007

FrancophonIA

--- language: - de - es - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19412 ## Citation ``` Artigos visitportugal 2007 (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19412 ```

FrancophonIA/Localidades_2007

FrancophonIA

--- language: - de - es - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19413 ## Citation ``` Localidades 2007 (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19413 ```

halltape/output

halltape

--- license: mit ---

FrancophonIA/Parques_e_reservas_2007

FrancophonIA

--- language: - de - es - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19414 ## Citation ``` Parques e reservas 2007 (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19414 ```

FrancophonIA/localidades_alentejo

FrancophonIA

--- language: - pt - it - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19417 ## Citation ``` localidades alentejo (2022). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/19417 ```

FrancophonIA/Taxa_municipal_turistica_faro

FrancophonIA

--- language: - es - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/21403 ## Citation ``` TAXA MUNICIPAL TURÍSTICA FARO (2023). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/21403 ```

marcov/hans_promptsource

marcov

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: class_label: names: '0': entailment '1': non-entailment - name: parse_premise dtype: string - name: parse_hypothesis dtype: string - name: binary_parse_premise dtype: string - name: binary_parse_hypothesis dtype: string - name: heuristic dtype: string - name: subcase dtype: string - name: template dtype: string - name: template_name dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 240998620.0 num_examples: 300000 - name: validation num_bytes: 240715490.0 num_examples: 300000 download_size: 80584756 dataset_size: 481714110.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

FrancophonIA/Lei_da_Paridade

FrancophonIA

--- language: - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/21417 ## Citation ``` Lei da Paridade nos Órgãos Colegiais Representativos do Poder Político (2023). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/21417 ```

marcov/craigslist_bargains_promptsource

marcov

--- dataset_info: features: - name: agent_info sequence: - name: Bottomline dtype: string - name: Role dtype: string - name: Target dtype: float32 - name: agent_turn sequence: int32 - name: dialogue_acts sequence: - name: intent dtype: string - name: price dtype: float32 - name: utterance sequence: string - name: items sequence: - name: Category dtype: string - name: Images dtype: string - name: Price dtype: float32 - name: Description dtype: string - name: Title dtype: string - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 100587712.0 num_examples: 31482 - name: test num_bytes: 16028723.0 num_examples: 5028 - name: validation num_bytes: 11428215.0 num_examples: 3582 download_size: 30986149 dataset_size: 128044650.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

FrancophonIA/Codigo_de_Conduta_dos_Deputados

FrancophonIA

--- language: - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19418 ## Citation ``` Código de Conduta dos Deputados à Assembleia da República (2023). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/21418 ```

FrancophonIA/Regime_Juridico_Inqueritos_Parlamentares

FrancophonIA

--- language: - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19419 ## Citation ``` Regime Jurídico dos Inquéritos Parlamentares (2023). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/21419 ```

marcov/acronym_identification_promptsource

marcov

--- dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: labels sequence: class_label: names: '0': B-long '1': B-short '2': I-long '3': I-short '4': O - name: template_name dtype: string - name: template dtype: string - name: rendered_input dtype: string - name: rendered_output dtype: string splits: - name: train num_bytes: 221583682.52955875 num_examples: 80383 - name: validation num_bytes: 27188873.57212192 num_examples: 9868 - name: test num_bytes: 16108512.0 num_examples: 7000 download_size: 27551938 dataset_size: 264881068.10168067 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

FrancophonIA/Estatuto_dos_Funcionarios_Parlamentares

FrancophonIA

--- language: - pt - en - fr multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19420 ## Citation ``` Estatuto dos Funcionários Parlamentares (2023). Version unspecified. [Dataset (Text corpus)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/corpus/21420 ```

user9000/CLEVR-HOPE

user9000

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

ziyu3141/rich_feedback_test_new_all

ziyu3141

--- dataset_info: features: - name: Filename dtype: string - name: Aesthetics score dtype: float64 - name: Artifact score dtype: float64 - name: Misalignment score dtype: float64 - name: Overall score dtype: float64 - name: Artifact heatmap sequence: sequence: sequence: int64 - name: Misalignment heatmap sequence: sequence: sequence: int64 - name: Misalignment token label dtype: string splits: - name: train num_bytes: 99534427480 num_examples: 15810 download_size: 181470749 dataset_size: 99534427480 configs: - config_name: default data_files: - split: train path: data/train-* ---

FrancophonIA/Esterm_2

FrancophonIA

--- language: - de - fr - en - ru - fi - et - la multilingulality: - multilingual task_categories: - translation viewer: false --- > [!NOTE] > Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/19865 ## Description Esterm 2 is a multilingual terminology database of the Estonian Language Institute, which combines terminology from different fields. It contains information on both the term projects of the EIT and the terms researched in the course of responding to the EIT's terminology queries. ## Citation ``` EKI ühendterminibaas Esterm 2 (2022). Version unspecified. [Dataset (Lexical/Conceptual Resource)]. Source: European Language Grid. https://live.european-language-grid.eu/catalogue/lcr/19865 ```

open-llm-leaderboard/DreadPoor__WIP-Acacia-8B-Model_Stock-details

open-llm-leaderboard

--- pretty_name: Evaluation run of DreadPoor/WIP-Acacia-8B-Model_Stock dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [DreadPoor/WIP-Acacia-8B-Model_Stock](https://huggingface.co/DreadPoor/WIP-Acacia-8B-Model_Stock)\n\ The dataset is composed of 38 configuration(s), each one corresponding 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.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/DreadPoor__WIP-Acacia-8B-Model_Stock-details\"\ ,\n\tname=\"DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_boolean_expressions\"\ ,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\ \ from run 2024-11-29T21-25-50.060860](https://huggingface.co/datasets/open-llm-leaderboard/DreadPoor__WIP-Acacia-8B-Model_Stock-details/blob/main/DreadPoor__WIP-Acacia-8B-Model_Stock/results_2024-11-29T21-25-50.060860.json)\ \ (note that there 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 \"leaderboard\": {\n\ \ \"prompt_level_loose_acc,none\": 0.6284658040665434,\n \"\ prompt_level_loose_acc_stderr,none\": 0.020794253888707582,\n \"inst_level_loose_acc,none\"\ : 0.7194244604316546,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\ ,\n \"exact_match,none\": 0.15709969788519634,\n \"exact_match_stderr,none\"\ : 0.00946496305892503,\n \"acc_norm,none\": 0.4750291866649371,\n \ \ \"acc_norm_stderr,none\": 0.005373063781032417,\n \"acc,none\"\ : 0.37367021276595747,\n \"acc_stderr,none\": 0.004410571933521376,\n\ \ \"inst_level_strict_acc,none\": 0.6762589928057554,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_strict_acc,none\"\ : 0.5730129390018485,\n \"prompt_level_strict_acc_stderr,none\": 0.021285933050061243,\n\ \ \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.5169241451136956,\n \"acc_norm_stderr,none\"\ : 0.0062288773189484396,\n \"alias\": \" - leaderboard_bbh\"\n \ \ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"alias\"\ : \" - leaderboard_bbh_boolean_expressions\",\n \"acc_norm,none\": 0.816,\n\ \ \"acc_norm_stderr,none\": 0.02455581299422255\n },\n \ \ \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6149732620320856,\n \"acc_norm_stderr,none\"\ : 0.03567936280544673\n },\n \"leaderboard_bbh_date_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_date_understanding\",\n \ \ \"acc_norm,none\": 0.48,\n \"acc_norm_stderr,none\": 0.03166085340849512\n\ \ },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"alias\"\ : \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\": 0.588,\n\ \ \"acc_norm_stderr,none\": 0.031191596026022818\n },\n \ \ \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\"\ : \" - leaderboard_bbh_geometric_shapes\",\n \"acc_norm,none\": 0.444,\n\ \ \"acc_norm_stderr,none\": 0.03148684942554571\n },\n \ \ \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.692,\n \"acc_norm_stderr,none\":\ \ 0.02925692860650181\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\ ,\n \"acc_norm,none\": 0.392,\n \"acc_norm_stderr,none\":\ \ 0.030938207620401222\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.408,\n \"acc_norm_stderr,none\":\ \ 0.031145209846548512\n },\n \"leaderboard_bbh_logical_deduction_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\ ,\n \"acc_norm,none\": 0.596,\n \"acc_norm_stderr,none\":\ \ 0.03109668818482536\n },\n \"leaderboard_bbh_movie_recommendation\"\ : {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\",\n \ \ \"acc_norm,none\": 0.66,\n \"acc_norm_stderr,none\": 0.030020073605457876\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \"\ \ - leaderboard_bbh_navigate\",\n \"acc_norm,none\": 0.584,\n \ \ \"acc_norm_stderr,none\": 0.031235856237014505\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.5342465753424658,\n \"acc_norm_stderr,none\": 0.04142522736934774\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.636,\n \"acc_norm_stderr,none\": 0.030491555220405475\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.704,\n \ \ \"acc_norm_stderr,none\": 0.028928939388379697\n },\n \"\ leaderboard_bbh_salient_translation_error_detection\": {\n \"alias\"\ : \" - leaderboard_bbh_salient_translation_error_detection\",\n \"acc_norm,none\"\ : 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n },\n\ \ \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6179775280898876,\n \"acc_norm_stderr,none\"\ : 0.03652112637307604\n },\n \"leaderboard_bbh_sports_understanding\"\ : {\n \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \ \ \"acc_norm,none\": 0.784,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_temporal_sequences\": {\n \"alias\"\ : \" - leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.268,\n\ \ \"acc_norm_stderr,none\": 0.02806876238252672\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.216,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.204,\n \"acc_norm_stderr,none\":\ \ 0.025537121574548162\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.312,\n \"acc_norm_stderr,none\":\ \ 0.02936106757521985\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.492,\n \"acc_norm_stderr,none\": 0.03168215643141386\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3062080536912752,\n\ \ \"acc_norm_stderr,none\": 0.013363479514082741,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.3181818181818182,\n \"acc_norm_stderr,none\": 0.0331847733384533\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.2948717948717949,\n\ \ \"acc_norm_stderr,none\": 0.01953225605335253\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.31473214285714285,\n \"acc_norm_stderr,none\"\ : 0.021965797142222607\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.5730129390018485,\n \"prompt_level_strict_acc_stderr,none\": 0.021285933050061243,\n\ \ \"inst_level_strict_acc,none\": 0.6762589928057554,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.6284658040665434,\n \"prompt_level_loose_acc_stderr,none\": 0.020794253888707582,\n\ \ \"inst_level_loose_acc,none\": 0.7194244604316546,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.15709969788519634,\n \"exact_match_stderr,none\"\ : 0.00946496305892503,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.31596091205211724,\n\ \ \"exact_match_stderr,none\": 0.026576416772305225\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.11382113821138211,\n \"exact_match_stderr,none\": 0.02875360087323741\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.09848484848484848,\n\ \ \"exact_match_stderr,none\": 0.026033680930226354\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.03214285714285714,\n \"exact_match_stderr,none\": 0.01055955866175321\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.1038961038961039,\n\ \ \"exact_match_stderr,none\": 0.02466795220435413\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.29015544041450775,\n \"exact_match_stderr,none\"\ : 0.032752644677915166\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.022222222222222223,\n \"exact_match_stderr,none\"\ : 0.01273389971505968\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.37367021276595747,\n\ \ \"acc_stderr,none\": 0.004410571933521376\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.421957671957672,\n \"acc_norm_stderr,none\"\ : 0.01746179776757259,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\": \"\ \ - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.592,\n\ \ \"acc_norm_stderr,none\": 0.03114520984654851\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.34375,\n \"acc_norm_stderr,none\"\ : 0.029743078779677763\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ }\n },\n \"leaderboard\": {\n \"prompt_level_loose_acc,none\"\ : 0.6284658040665434,\n \"prompt_level_loose_acc_stderr,none\": 0.020794253888707582,\n\ \ \"inst_level_loose_acc,none\": 0.7194244604316546,\n \"inst_level_loose_acc_stderr,none\"\ : \"N/A\",\n \"exact_match,none\": 0.15709969788519634,\n \"exact_match_stderr,none\"\ : 0.00946496305892503,\n \"acc_norm,none\": 0.4750291866649371,\n \ \ \"acc_norm_stderr,none\": 0.005373063781032417,\n \"acc,none\": 0.37367021276595747,\n\ \ \"acc_stderr,none\": 0.004410571933521376,\n \"inst_level_strict_acc,none\"\ : 0.6762589928057554,\n \"inst_level_strict_acc_stderr,none\": \"N/A\",\n\ \ \"prompt_level_strict_acc,none\": 0.5730129390018485,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.021285933050061243,\n \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\ : {\n \"acc_norm,none\": 0.5169241451136956,\n \"acc_norm_stderr,none\"\ : 0.0062288773189484396,\n \"alias\": \" - leaderboard_bbh\"\n },\n \ \ \"leaderboard_bbh_boolean_expressions\": {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\"\ ,\n \"acc_norm,none\": 0.816,\n \"acc_norm_stderr,none\": 0.02455581299422255\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6149732620320856,\n \"acc_norm_stderr,none\"\ : 0.03567936280544673\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.48,\n \"acc_norm_stderr,none\": 0.03166085340849512\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \"acc_norm,none\"\ : 0.588,\n \"acc_norm_stderr,none\": 0.031191596026022818\n },\n \"\ leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.5,\n \"acc_norm_stderr,none\": 0.031686212526223896\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.444,\n \"acc_norm_stderr,none\": 0.03148684942554571\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.692,\n \"acc_norm_stderr,none\": 0.02925692860650181\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.392,\n \"acc_norm_stderr,none\": 0.030938207620401222\n },\n \"\ leaderboard_bbh_logical_deduction_seven_objects\": {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.408,\n \"acc_norm_stderr,none\": 0.031145209846548512\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.596,\n \"acc_norm_stderr,none\": 0.03109668818482536\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.66,\n \"acc_norm_stderr,none\": 0.030020073605457876\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.584,\n \"acc_norm_stderr,none\": 0.031235856237014505\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.46,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.5342465753424658,\n\ \ \"acc_norm_stderr,none\": 0.04142522736934774\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.636,\n \"acc_norm_stderr,none\": 0.030491555220405475\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.704,\n \"acc_norm_stderr,none\": 0.028928939388379697\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.6179775280898876,\n \"acc_norm_stderr,none\"\ : 0.03652112637307604\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.784,\n \"acc_norm_stderr,none\": 0.02607865766373279\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.268,\n \"acc_norm_stderr,none\": 0.02806876238252672\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.216,\n \"acc_norm_stderr,none\": 0.02607865766373279\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.204,\n \"acc_norm_stderr,none\": 0.025537121574548162\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.312,\n \"acc_norm_stderr,none\": 0.02936106757521985\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.492,\n \"acc_norm_stderr,none\": 0.03168215643141386\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.3062080536912752,\n\ \ \"acc_norm_stderr,none\": 0.013363479514082741,\n \"alias\": \"\ \ - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"\ alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.3181818181818182,\n\ \ \"acc_norm_stderr,none\": 0.0331847733384533\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.2948717948717949,\n \"acc_norm_stderr,none\": 0.01953225605335253\n \ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.31473214285714285,\n \"acc_norm_stderr,none\"\ : 0.021965797142222607\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.5730129390018485,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.021285933050061243,\n \ \ \"inst_level_strict_acc,none\": 0.6762589928057554,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.6284658040665434,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.020794253888707582,\n \"inst_level_loose_acc,none\"\ : 0.7194244604316546,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.15709969788519634,\n\ \ \"exact_match_stderr,none\": 0.00946496305892503,\n \"alias\": \"\ \ - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.31596091205211724,\n \"exact_match_stderr,none\": 0.026576416772305225\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.11382113821138211,\n \"exact_match_stderr,none\": 0.02875360087323741\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.09848484848484848,\n \"exact_match_stderr,none\"\ : 0.026033680930226354\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.03214285714285714,\n \"exact_match_stderr,none\"\ : 0.01055955866175321\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.1038961038961039,\n \"exact_match_stderr,none\": 0.02466795220435413\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.29015544041450775,\n \"exact_match_stderr,none\"\ : 0.032752644677915166\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.022222222222222223,\n \"exact_match_stderr,none\": 0.01273389971505968\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.37367021276595747,\n \"acc_stderr,none\": 0.004410571933521376\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.421957671957672,\n\ \ \"acc_norm_stderr,none\": 0.01746179776757259,\n \"alias\": \" -\ \ leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.592,\n \"acc_norm_stderr,none\": 0.03114520984654851\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.34375,\n \"acc_norm_stderr,none\": 0.029743078779677763\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465857\n\ \ }\n}\n```" repo_url: https://huggingface.co/DreadPoor/WIP-Acacia-8B-Model_Stock leaderboard_url: '' point_of_contact: '' configs: - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_date_understanding data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_navigate data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_navigate_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_object_counting data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_ruin_names data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_snarks data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_snarks_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_gpqa_diamond data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_gpqa_extended data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_gpqa_extended_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_gpqa_main data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_gpqa_main_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_ifeval data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_ifeval_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_math_algebra_hard data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_math_geometry_hard data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_math_num_theory_hard data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_math_precalculus_hard data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_mmlu_pro data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_mmlu_pro_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_musr_object_placements data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_musr_object_placements_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-29T21-25-50.060860.jsonl' - config_name: DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_musr_team_allocation data_files: - split: 2024_11_29T21_25_50.060860 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-29T21-25-50.060860.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-29T21-25-50.060860.jsonl' --- # Dataset Card for Evaluation run of DreadPoor/WIP-Acacia-8B-Model_Stock <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [DreadPoor/WIP-Acacia-8B-Model_Stock](https://huggingface.co/DreadPoor/WIP-Acacia-8B-Model_Stock) The dataset is composed of 38 configuration(s), each one corresponding 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. 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/DreadPoor__WIP-Acacia-8B-Model_Stock-details", name="DreadPoor__WIP-Acacia-8B-Model_Stock__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-29T21-25-50.060860](https://huggingface.co/datasets/open-llm-leaderboard/DreadPoor__WIP-Acacia-8B-Model_Stock-details/blob/main/DreadPoor__WIP-Acacia-8B-Model_Stock/results_2024-11-29T21-25-50.060860.json) (note that there 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": { "leaderboard": { "prompt_level_loose_acc,none": 0.6284658040665434, "prompt_level_loose_acc_stderr,none": 0.020794253888707582, "inst_level_loose_acc,none": 0.7194244604316546, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.15709969788519634, "exact_match_stderr,none": 0.00946496305892503, "acc_norm,none": 0.4750291866649371, "acc_norm_stderr,none": 0.005373063781032417, "acc,none": 0.37367021276595747, "acc_stderr,none": 0.004410571933521376, "inst_level_strict_acc,none": 0.6762589928057554, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.5730129390018485, "prompt_level_strict_acc_stderr,none": 0.021285933050061243, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.5169241451136956, "acc_norm_stderr,none": 0.0062288773189484396, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.816, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6149732620320856, "acc_norm_stderr,none": 0.03567936280544673 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.48, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.588, "acc_norm_stderr,none": 0.031191596026022818 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.444, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.692, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.392, "acc_norm_stderr,none": 0.030938207620401222 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.596, "acc_norm_stderr,none": 0.03109668818482536 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.66, "acc_norm_stderr,none": 0.030020073605457876 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.5342465753424658, "acc_norm_stderr,none": 0.04142522736934774 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.636, "acc_norm_stderr,none": 0.030491555220405475 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.704, "acc_norm_stderr,none": 0.028928939388379697 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6179775280898876, "acc_norm_stderr,none": 0.03652112637307604 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.784, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.216, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.204, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.312, "acc_norm_stderr,none": 0.02936106757521985 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.492, "acc_norm_stderr,none": 0.03168215643141386 }, "leaderboard_gpqa": { "acc_norm,none": 0.3062080536912752, "acc_norm_stderr,none": 0.013363479514082741, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.3181818181818182, "acc_norm_stderr,none": 0.0331847733384533 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2948717948717949, "acc_norm_stderr,none": 0.01953225605335253 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.31473214285714285, "acc_norm_stderr,none": 0.021965797142222607 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.5730129390018485, "prompt_level_strict_acc_stderr,none": 0.021285933050061243, "inst_level_strict_acc,none": 0.6762589928057554, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.6284658040665434, "prompt_level_loose_acc_stderr,none": 0.020794253888707582, "inst_level_loose_acc,none": 0.7194244604316546, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.15709969788519634, "exact_match_stderr,none": 0.00946496305892503, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.31596091205211724, "exact_match_stderr,none": 0.026576416772305225 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.11382113821138211, "exact_match_stderr,none": 0.02875360087323741 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.09848484848484848, "exact_match_stderr,none": 0.026033680930226354 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.03214285714285714, "exact_match_stderr,none": 0.01055955866175321 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.1038961038961039, "exact_match_stderr,none": 0.02466795220435413 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.29015544041450775, "exact_match_stderr,none": 0.032752644677915166 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.022222222222222223, "exact_match_stderr,none": 0.01273389971505968 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.37367021276595747, "acc_stderr,none": 0.004410571933521376 }, "leaderboard_musr": { "acc_norm,none": 0.421957671957672, "acc_norm_stderr,none": 0.01746179776757259, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.34375, "acc_norm_stderr,none": 0.029743078779677763 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 } }, "leaderboard": { "prompt_level_loose_acc,none": 0.6284658040665434, "prompt_level_loose_acc_stderr,none": 0.020794253888707582, "inst_level_loose_acc,none": 0.7194244604316546, "inst_level_loose_acc_stderr,none": "N/A", "exact_match,none": 0.15709969788519634, "exact_match_stderr,none": 0.00946496305892503, "acc_norm,none": 0.4750291866649371, "acc_norm_stderr,none": 0.005373063781032417, "acc,none": 0.37367021276595747, "acc_stderr,none": 0.004410571933521376, "inst_level_strict_acc,none": 0.6762589928057554, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.5730129390018485, "prompt_level_strict_acc_stderr,none": 0.021285933050061243, "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.5169241451136956, "acc_norm_stderr,none": 0.0062288773189484396, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.816, "acc_norm_stderr,none": 0.02455581299422255 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6149732620320856, "acc_norm_stderr,none": 0.03567936280544673 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.48, "acc_norm_stderr,none": 0.03166085340849512 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.588, "acc_norm_stderr,none": 0.031191596026022818 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.5, "acc_norm_stderr,none": 0.031686212526223896 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.444, "acc_norm_stderr,none": 0.03148684942554571 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.692, "acc_norm_stderr,none": 0.02925692860650181 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.392, "acc_norm_stderr,none": 0.030938207620401222 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.408, "acc_norm_stderr,none": 0.031145209846548512 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.596, "acc_norm_stderr,none": 0.03109668818482536 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.66, "acc_norm_stderr,none": 0.030020073605457876 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.584, "acc_norm_stderr,none": 0.031235856237014505 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.46, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.5342465753424658, "acc_norm_stderr,none": 0.04142522736934774 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.636, "acc_norm_stderr,none": 0.030491555220405475 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.704, "acc_norm_stderr,none": 0.028928939388379697 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.6179775280898876, "acc_norm_stderr,none": 0.03652112637307604 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.784, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.268, "acc_norm_stderr,none": 0.02806876238252672 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.216, "acc_norm_stderr,none": 0.02607865766373279 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.204, "acc_norm_stderr,none": 0.025537121574548162 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.312, "acc_norm_stderr,none": 0.02936106757521985 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.492, "acc_norm_stderr,none": 0.03168215643141386 }, "leaderboard_gpqa": { "acc_norm,none": 0.3062080536912752, "acc_norm_stderr,none": 0.013363479514082741, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.3181818181818182, "acc_norm_stderr,none": 0.0331847733384533 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2948717948717949, "acc_norm_stderr,none": 0.01953225605335253 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.31473214285714285, "acc_norm_stderr,none": 0.021965797142222607 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.5730129390018485, "prompt_level_strict_acc_stderr,none": 0.021285933050061243, "inst_level_strict_acc,none": 0.6762589928057554, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.6284658040665434, "prompt_level_loose_acc_stderr,none": 0.020794253888707582, "inst_level_loose_acc,none": 0.7194244604316546, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.15709969788519634, "exact_match_stderr,none": 0.00946496305892503, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.31596091205211724, "exact_match_stderr,none": 0.026576416772305225 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.11382113821138211, "exact_match_stderr,none": 0.02875360087323741 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.09848484848484848, "exact_match_stderr,none": 0.026033680930226354 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.03214285714285714, "exact_match_stderr,none": 0.01055955866175321 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.1038961038961039, "exact_match_stderr,none": 0.02466795220435413 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.29015544041450775, "exact_match_stderr,none": 0.032752644677915166 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.022222222222222223, "exact_match_stderr,none": 0.01273389971505968 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.37367021276595747, "acc_stderr,none": 0.004410571933521376 }, "leaderboard_musr": { "acc_norm,none": 0.421957671957672, "acc_norm_stderr,none": 0.01746179776757259, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.592, "acc_norm_stderr,none": 0.03114520984654851 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.34375, "acc_norm_stderr,none": 0.029743078779677763 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.332, "acc_norm_stderr,none": 0.029844039047465857 } } ``` ## 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. 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FrancophonIA/ANNODIS

FrancophonIA

--- language: - fr viewer: false license: cc-by-nc-sa-3.0 --- > [!NOTE] > Dataset origin: http://redac.univ-tlse2.fr/corpus/annodis/ ## Presentation La ressource ANNODIS est un ensemble diversifié de textes en français enrichis manuellement d'annotations de structures discursives. Elle est le résultat du projet ANNODIS (ANNOtation DIScursive), projet financé par l'ANR. Ses caractéristiques principales : deux annotations (correspondant à deux approches distinctes de l'organisation discursive) L'annotation en relations rhétoriques comprend la délimitation de 3188 Unités Élémentaires de Discours (EDU) et 1395 Unités Complexes de Discours (CDU) reliées par 3355 relations de discours typées (e.g. contraste, élaboration, résultat, attribution, etc.) L'annotation en structures multi-échelles qui fournit 991 structures énumératives, 588 chaînes topicales et l'ensemble des indices qui leur sont associés (e.g. 3456 expressions topicales) des textes (total 687 000 mots) issus de quatre sources : Est Républicain (39 articles, 10 000 mots) Wikipédia (30 articles + 30 extraits, 242 000 mots) Actes du Congrès Mondial de Linguistique Française 2008 (25 articles, 169 000 mots) Rapports de l'Institut Français de Relations Internationales (32 rapports, 266 000 mots) Les corpus ont été annotés avec Glozz, plate-forme développée dans le cadre d'ANNODIS ## Citation ``` Muller P., Vergez-Couret M., Prévot L., Asher N., Benamara F., Bras M., Le Draoulec A., Vieu L. (2012). Manuel d'annotation en relations de discours du projet ANNODIS. Carnets de Grammaire 21, 34p. [ PDF : http://w3.erss.univ-tlse2.fr/textes/publications/CarnetsGrammaire/carnGram21.pdf] ``` ``` Colléter M., Fabre C., Ho-Dac L.-M., Péry-Woodley M.-P., Rebeyrolle J., Tanguy L. (2012). La ressource ANNODIS multi-échelle : guide d'annotation et "bonus" Carnets de Grammaire 20, 63p. [ PDF : http://w3.erss.univ-tlse2.fr/textes/publications/CarnetsGrammaire/carnGram20.pdf ] ```

FrancophonIA/ClaimsKG

FrancophonIA

--- language: - fr - en multilinguality: - multilingual viewer: false --- > [!NOTE] > Dataset origin: https://lium.univ-lemans.fr/frnewslink/ ## Description ClaimsKG is a knowledge graph of metadata information for fact-checked claims scraped from popular fact-checking sites. In addition to providing a single dataset of claims and associated metadata, truth ratings are harmonized and additional information is provided for each claim, e.g., about mentioned entities. Please see (https://data.gesis.org/claimskg/) for further details about the data model, query examples and statistics. The dataset facilitates structured queries about claims, their truth values, involved entities, authors, dates, and other kinds of metadata. ClaimsKG is generated through a (semi-)automated pipeline, which harvests claim-related data from popular fact-checking web sites, annotates them with related entities from DBpedia/Wikipedia, and lifts all data to RDF using established vocabularies (such as schema.org). The latest release of ClaimsKG covers 74066 claims and 72127 Claim Reviews. This is the fourth release of the dataset where data was scraped till Jan 31, 2023 containing claims published between 1996 and 2023 from 13 fact-checking websites. The websites are Fullfact, Politifact, TruthOrFiction, Checkyourfact, Vishvanews, AFP (French), AFP, Polygraph, EU factcheck, Factograph, Fatabyyano, Snopes and Africacheck. The claim-review (fact-checking) period for claims ranges between the year 1996 to 2023. Similar to the previous release, the Entity fishing python client (https://github.com/hirmeos/entity-fishing-client-python) has been used for entity linking and disambiguation in this release. Improvements have been made in the web scraping and data preprocessing pipeline to extract more entities from both claims and claims reviews. Currently, ClaimsKG contains 3408386 entities detected and referenced with DBpedia. This latest release of ClaimsKG supersedes the previous versions as it contained all the claims from the previous versions together in addition to the additional new claims as well as improved entity annotation resulting in a higher number of entities. ## Citation ``` @misc{SDN-10.7802-2620, author = "Gangopadhyay, Susmita and Schellhammer, Sebastian and Boland, Katarina and Sch{\"u}ller, Sascha and Todorov, Konstantin and Tchechmedjiev, Andon and Zapilko, Benjamin and Fafalios, Pavlos and Jabeen, Hajira and Dietze, Stefan", title = "ClaimsKG - A Knowledge Graph of Fact-Checked Claims (January, 2023)", year = "2023", howpublished = "GESIS, Cologne. Data File Version 2.0.0, https://doi.org/10.7802/2620", doi = "10.7802/2620", } ```

DT4LM/albertbasev2_rte_pair_faster-alzantot

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 47528 num_examples: 147 download_size: 39911 dataset_size: 47528 configs: - config_name: default data_files: - split: train path: data/train-* ---

DT4LM/albertbasev2_rte_pair_faster-alzantot_original

DT4LM

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: int32 splits: - name: train num_bytes: 47205 num_examples: 147 download_size: 39680 dataset_size: 47205 configs: - config_name: default data_files: - split: train path: data/train-* ---

amuvarma/qa_large_0_4_speechq

amuvarma

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 11822414204.0 num_examples: 80000 download_size: 10988942555 dataset_size: 11822414204.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

sufianstek/phc_vital_signs

sufianstek

--- license: mit ---

oakwood/demo_curtain

oakwood

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

ashercn97/reasoning-data-v2-2

ashercn97

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 24418126 num_examples: 4000 download_size: 12107288 dataset_size: 24418126 configs: - config_name: default data_files: - split: train path: data/train-* ---

WANGYJ0325/crag

WANGYJ0325

--- license: apache-2.0 ---

oakwood/test_20241130

oakwood

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

mpanda27/common_voice_16_0_ro_pseudo_labelled

mpanda27

--- dataset_info: config_name: ro features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: condition_on_prev sequence: int64 - name: whisper_transcript dtype: string splits: - name: train num_bytes: 650572662.0 num_examples: 734 - name: validation num_bytes: 485173030.0 num_examples: 546 - name: test num_bytes: 528897106.0 num_examples: 597 download_size: 1513163928 dataset_size: 1664642798.0 configs: - config_name: ro data_files: - split: train path: ro/train-* - split: validation path: ro/validation-* - split: test path: ro/test-* ---

ashercn97/test-distiset-1

ashercn97

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': illogical '1': logical splits: - name: train num_bytes: 27171 num_examples: 100 download_size: 17095 dataset_size: 27171 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif - datacraft --- <p align="left"> <a href="https://github.com/argilla-io/distilabel"> <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/> </a> </p> # Dataset Card for test-distiset-1 This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/ashercn97/test-distiset-1/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/ashercn97/test-distiset-1/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "label": 1, "text": "Just because 7 out of 10 people prefer pizza over burgers does not necessarily mean that 7/10 people prefer pizza, because the sample may not be representative of the entire population and we are rounding the result which is an approximation." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("ashercn97/test-distiset-1", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("ashercn97/test-distiset-1") ``` </details>

EdsonKanou/sql_training

EdsonKanou

--- license: mit dataset_info: features: - name: role dtype: string - name: content dtype: string splits: - name: train num_bytes: 80840 num_examples: 50 download_size: 12074 dataset_size: 80840 configs: - config_name: default data_files: - split: train path: data/train-* ---

ashercn97/reasoning-data-v3-1

ashercn97

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 8123629 num_examples: 1000 download_size: 3773971 dataset_size: 8123629 configs: - config_name: default data_files: - split: train path: data/train-* ---

ashercn97/reasoning-data-v3-2

ashercn97

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 45229809 num_examples: 4000 download_size: 20190882 dataset_size: 45229809 configs: - config_name: default data_files: - split: train path: data/train-* ---

ashercn97/reasoning-data-v4-1

ashercn97

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 39095272 num_examples: 2000 download_size: 16191447 dataset_size: 39095272 configs: - config_name: default data_files: - split: train path: data/train-* ---

Taylor658/fluoroscopy_techniques

Taylor658

--- size_categories: n<1K dataset_info: features: - name: text dtype: string - name: labels sequence: class_label: names: '0': digital-fluoroscopy '1': mobile-fluoroscopy '2': conventional-fluoroscopy splits: - name: train num_bytes: 61388 num_examples: 250 download_size: 24571 dataset_size: 61388 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif --- # Dataset Card for fluoroscopy_techniques This dataset has been created with [distilabel](https://distilabel.argilla.io/). ## Dataset Summary This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI: ```console distilabel pipeline run --config "https://huggingface.co/datasets/Taylor658/fluoroscopy_techniques/raw/main/pipeline.yaml" ``` or explore the configuration: ```console distilabel pipeline info --config "https://huggingface.co/datasets/Taylor658/fluoroscopy_techniques/raw/main/pipeline.yaml" ``` ## Dataset structure The examples have the following structure per configuration: <details><summary> Configuration: default </summary><hr> ```json { "labels": [ 0 ], "text": "This new imaging technology uses a flat-panel detector to provide continuous X-ray images in real-time, allowing for dynamic viewing of moving structures without the need for sequential exposures." } ``` This subset can be loaded as: ```python from datasets import load_dataset ds = load_dataset("Taylor658/fluoroscopy_techniques", "default") ``` Or simply as it follows, since there's only one configuration and is named `default`: ```python from datasets import load_dataset ds = load_dataset("Taylor658/fluoroscopy_techniques") ``` </details>

ashwiniai/anatomy-corpus-test

ashwiniai

--- dataset_info: features: - name: text dtype: string - name: page_idx dtype: int64 - name: document_name dtype: string - name: file_path dtype: string - name: file_url dtype: string - name: loader_name dtype: string splits: - name: pdfplumbertextloader num_bytes: 23313 num_examples: 6 - name: pypdf2textloader num_bytes: 23554 num_examples: 6 - name: pymupdf4llmtextloader num_bytes: 22607 num_examples: 6 download_size: 51369 dataset_size: 69474 configs: - config_name: default data_files: - split: pdfplumbertextloader path: data/pdfplumbertextloader-* - split: pypdf2textloader path: data/pypdf2textloader-* - split: pymupdf4llmtextloader path: data/pymupdf4llmtextloader-* ---

maanasharma5/arabic_sft_data

maanasharma5

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: refusal dtype: string splits: - name: train num_bytes: 24078936 num_examples: 15000 download_size: 9974963 dataset_size: 24078936 configs: - config_name: default data_files: - split: train path: data/train-* ---

rathore11/snoopy

rathore11

--- license: apache-2.0 ---

amuvarma/luna-full-conversations-250

amuvarma

--- dataset_info: features: - name: messsages sequence: string splits: - name: train num_bytes: 207067.0 num_examples: 250 download_size: 133978 dataset_size: 207067.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

hexuan21/math-sft-mix-full-w4-sub-1

hexuan21

--- license: apache-2.0 ---

JsZe/distributed-computing-complex

JsZe

--- annotations_creators: - no-annotation language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 100<n<1K source_datasets: - original task_categories: - question-answering - text-generation task_ids: - open-domain-qa --- # Distributed Systems Q&A Dataset This dataset is collection of question-and-answer pairs related to distributed systems, compiled from a list of commonly asked questions in a college-level class. This dataset is designed to assist educators, researchers, and developers working on tuning AI models, chatbots, or educational tools in the field of distributed systems. ### Key Features: - **Questions**: A variety of questions covering fundamental distributed systems concepts. - **Answers**: Detailed, accurate, and explanatory answers. - **Shuffled Order**: Entries are shuffled for non-sequential learning. --- ## Dataset Structure The dataset is provided in CSV format, with the following columns: | Column | Description | |----------|-------------------------------------------------| | Question | A question about distributed systems. | | Answer | A corresponding answer explaining the concept. | ### Sample Entries: | Question | Answer | |-----------------------------------------------|-----------------------------------------------------------------------------------------| | What are the main properties of a distributed transaction? | The main properties of a distributed transaction are atomicity, consistency, isolation, and durability (ACID). Atomicity ensures all operations are completed or none at all. Consistency ensures the system remains in a valid state. Isolation ensures transactions do not interfere with each other. Durability ensures results are permanent. | | How do distributed systems handle 'Deadlock Detection'? | Distributed systems handle deadlock detection by monitoring resource allocation and communication patterns. Algorithms like wait-for graphs and probe-based methods identify cycles or unresolved dependencies, allowing the system to detect and resolve deadlocks promptly. | --- ## Licensing This dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ## Citation If you use this dataset in your research or applications, please cite it as follows: ``` Author(s): Jeffrey Zhou, K. Mani Chandy, Sachin Adlakha Title: Distributed Systems Q&A Dataset URL: https://huggingface.co/datasets/JsZe/distributed-computing-complex License: MIT License Date: [2024-07-14] ```

open-llm-leaderboard/mkxu__llama-3-8b-po1-details

open-llm-leaderboard

--- pretty_name: Evaluation run of mkxu/llama-3-8b-po1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mkxu/llama-3-8b-po1](https://huggingface.co/mkxu/llama-3-8b-po1)\nThe dataset\ \ is composed of 38 configuration(s), each one corresponding 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.\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(\n\t\"open-llm-leaderboard/mkxu__llama-3-8b-po1-details\"\ ,\n\tname=\"mkxu__llama-3-8b-po1__leaderboard_bbh_boolean_expressions\",\n\tsplit=\"\ latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results from run\ \ 2024-11-30T06-55-29.252571](https://huggingface.co/datasets/open-llm-leaderboard/mkxu__llama-3-8b-po1-details/blob/main/mkxu__llama-3-8b-po1/results_2024-11-30T06-55-29.252571.json)\ \ (note that there 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 \"leaderboard\": {\n\ \ \"inst_level_loose_acc,none\": 0.5623501199040767,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\",\n \"prompt_level_strict_acc,none\"\ : 0.3438077634011091,\n \"prompt_level_strict_acc_stderr,none\": 0.020439793487859976,\n\ \ \"exact_match,none\": 0.0702416918429003,\n \"exact_match_stderr,none\"\ : 0.006863454031669159,\n \"acc,none\": 0.3562167553191489,\n \ \ \"acc_stderr,none\": 0.004365923714430882,\n \"prompt_level_loose_acc,none\"\ : 0.4491682070240296,\n \"prompt_level_loose_acc_stderr,none\": 0.021405093233588298,\n\ \ \"acc_norm,none\": 0.4525878842910883,\n \"acc_norm_stderr,none\"\ : 0.005350400462318365,\n \"inst_level_strict_acc,none\": 0.4724220623501199,\n\ \ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"alias\"\ : \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\"\ : 0.4943586182954348,\n \"acc_norm_stderr,none\": 0.006221085972017242,\n\ \ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \ \ \"acc_norm,none\": 0.74,\n \"acc_norm_stderr,none\": 0.027797315752644335\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\"\ : \" - leaderboard_bbh_causal_judgement\",\n \"acc_norm,none\": 0.6737967914438503,\n\ \ \"acc_norm_stderr,none\": 0.03437574439341202\n },\n \ \ \"leaderboard_bbh_date_understanding\": {\n \"alias\": \" - leaderboard_bbh_date_understanding\"\ ,\n \"acc_norm,none\": 0.484,\n \"acc_norm_stderr,none\":\ \ 0.03166998503010743\n },\n \"leaderboard_bbh_disambiguation_qa\"\ : {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\",\n \ \ \"acc_norm,none\": 0.644,\n \"acc_norm_stderr,none\": 0.0303436806571532\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\"\ : \" - leaderboard_bbh_formal_fallacies\",\n \"acc_norm,none\": 0.552,\n\ \ \"acc_norm_stderr,none\": 0.03151438761115348\n },\n \ \ \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.472,\n \"acc_norm_stderr,none\":\ \ 0.031636489531544396\n },\n \"leaderboard_bbh_hyperbaton\": {\n\ \ \"alias\": \" - leaderboard_bbh_hyperbaton\",\n \"acc_norm,none\"\ : 0.676,\n \"acc_norm_stderr,none\": 0.029658294924545567\n },\n\ \ \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.356,\n \"acc_norm_stderr,none\": 0.0303436806571532\n },\n\ \ \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\",\n \"\ acc_norm,none\": 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n\ \ \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n\ \ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\ alias\": \" - leaderboard_bbh_movie_recommendation\",\n \"acc_norm,none\"\ : 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n },\n\ \ \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.652,\n \"acc_norm_stderr,none\":\ \ 0.030186568464511673\n },\n \"leaderboard_bbh_object_counting\"\ : {\n \"alias\": \" - leaderboard_bbh_object_counting\",\n \ \ \"acc_norm,none\": 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\ alias\": \" - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\"\ : 0.5342465753424658,\n \"acc_norm_stderr,none\": 0.04142522736934774\n\ \ },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\",\n\ \ \"acc_norm,none\": 0.54,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \"\ \ - leaderboard_bbh_ruin_names\",\n \"acc_norm,none\": 0.7,\n \ \ \"acc_norm_stderr,none\": 0.029040893477575786\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\ : {\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\":\ \ 0.03160397514522374\n },\n \"leaderboard_bbh_snarks\": {\n \ \ \"alias\": \" - leaderboard_bbh_snarks\",\n \"acc_norm,none\"\ : 0.550561797752809,\n \"acc_norm_stderr,none\": 0.037389649660569645\n\ \ },\n \"leaderboard_bbh_sports_understanding\": {\n \"\ alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.788,\n \"acc_norm_stderr,none\": 0.025901884690541117\n },\n\ \ \"leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" -\ \ leaderboard_bbh_temporal_sequences\",\n \"acc_norm,none\": 0.152,\n\ \ \"acc_norm_stderr,none\": 0.022752024491765464\n },\n \ \ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \"\ alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\",\n \ \ \"acc_norm,none\": 0.208,\n \"acc_norm_stderr,none\": 0.02572139890141637\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.232,\n \"acc_norm_stderr,none\":\ \ 0.026750070374865202\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.352,\n \"acc_norm_stderr,none\":\ \ 0.030266288057359866\n },\n \"leaderboard_bbh_web_of_lies\": {\n\ \ \"alias\": \" - leaderboard_bbh_web_of_lies\",\n \"acc_norm,none\"\ : 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n },\n\ \ \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.29697986577181207,\n\ \ \"acc_norm_stderr,none\": 0.0132451965442603,\n \"alias\"\ : \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n\ \ \"alias\": \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\"\ : 0.2878787878787879,\n \"acc_norm_stderr,none\": 0.03225883512300998\n\ \ },\n \"leaderboard_gpqa_extended\": {\n \"alias\": \"\ \ - leaderboard_gpqa_extended\",\n \"acc_norm,none\": 0.2857142857142857,\n\ \ \"acc_norm_stderr,none\": 0.019351013185102753\n },\n \ \ \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.31473214285714285,\n \"acc_norm_stderr,none\"\ : 0.021965797142222607\n },\n \"leaderboard_ifeval\": {\n \ \ \"alias\": \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\"\ : 0.3438077634011091,\n \"prompt_level_strict_acc_stderr,none\": 0.020439793487859976,\n\ \ \"inst_level_strict_acc,none\": 0.4724220623501199,\n \"\ inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\ : 0.4491682070240296,\n \"prompt_level_loose_acc_stderr,none\": 0.021405093233588298,\n\ \ \"inst_level_loose_acc,none\": 0.5623501199040767,\n \"\ inst_level_loose_acc_stderr,none\": \"N/A\"\n },\n \"leaderboard_math_hard\"\ : {\n \"exact_match,none\": 0.0702416918429003,\n \"exact_match_stderr,none\"\ : 0.006863454031669159,\n \"alias\": \" - leaderboard_math_hard\"\n \ \ },\n \"leaderboard_math_algebra_hard\": {\n \"alias\"\ : \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\": 0.13029315960912052,\n\ \ \"exact_match_stderr,none\": 0.019243609597826783\n },\n \ \ \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\": \"\ \ - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.032520325203252036,\n \"exact_match_stderr,none\": 0.016058998205879745\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\"\ : \" - leaderboard_math_geometry_hard\",\n \"exact_match,none\": 0.007575757575757576,\n\ \ \"exact_match_stderr,none\": 0.007575757575757577\n },\n \ \ \"leaderboard_math_intermediate_algebra_hard\": {\n \"alias\":\ \ \" - leaderboard_math_intermediate_algebra_hard\",\n \"exact_match,none\"\ : 0.0035714285714285713,\n \"exact_match_stderr,none\": 0.0035714285714285713\n\ \ },\n \"leaderboard_math_num_theory_hard\": {\n \"alias\"\ : \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\": 0.07142857142857142,\n\ \ \"exact_match_stderr,none\": 0.020820824576076338\n },\n \ \ \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.15025906735751296,\n \"exact_match_stderr,none\"\ : 0.025787723180723855\n },\n \"leaderboard_math_precalculus_hard\"\ : {\n \"alias\": \" - leaderboard_math_precalculus_hard\",\n \ \ \"exact_match,none\": 0.05185185185185185,\n \"exact_match_stderr,none\"\ : 0.019154368449050496\n },\n \"leaderboard_mmlu_pro\": {\n \ \ \"alias\": \" - leaderboard_mmlu_pro\",\n \"acc,none\": 0.3562167553191489,\n\ \ \"acc_stderr,none\": 0.004365923714430882\n },\n \"leaderboard_musr\"\ : {\n \"acc_norm,none\": 0.37962962962962965,\n \"acc_norm_stderr,none\"\ : 0.017120448240540476,\n \"alias\": \" - leaderboard_musr\"\n \ \ },\n \"leaderboard_musr_murder_mysteries\": {\n \"alias\":\ \ \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\": 0.552,\n\ \ \"acc_norm_stderr,none\": 0.03151438761115348\n },\n \ \ \"leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.28125,\n \"acc_norm_stderr,none\"\ : 0.028155620586096754\n },\n \"leaderboard_musr_team_allocation\"\ : {\n \"alias\": \" - leaderboard_musr_team_allocation\",\n \ \ \"acc_norm,none\": 0.308,\n \"acc_norm_stderr,none\": 0.02925692860650181\n\ \ }\n },\n \"leaderboard\": {\n \"inst_level_loose_acc,none\"\ : 0.5623501199040767,\n \"inst_level_loose_acc_stderr,none\": \"N/A\",\n\ \ \"prompt_level_strict_acc,none\": 0.3438077634011091,\n \"prompt_level_strict_acc_stderr,none\"\ : 0.020439793487859976,\n \"exact_match,none\": 0.0702416918429003,\n \ \ \"exact_match_stderr,none\": 0.006863454031669159,\n \"acc,none\":\ \ 0.3562167553191489,\n \"acc_stderr,none\": 0.004365923714430882,\n \ \ \"prompt_level_loose_acc,none\": 0.4491682070240296,\n \"prompt_level_loose_acc_stderr,none\"\ : 0.021405093233588298,\n \"acc_norm,none\": 0.4525878842910883,\n \ \ \"acc_norm_stderr,none\": 0.005350400462318365,\n \"inst_level_strict_acc,none\"\ : 0.4724220623501199,\n \"inst_level_strict_acc_stderr,none\": \"N/A\",\n\ \ \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \ \ \"acc_norm,none\": 0.4943586182954348,\n \"acc_norm_stderr,none\": 0.006221085972017242,\n\ \ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\ : {\n \"alias\": \" - leaderboard_bbh_boolean_expressions\",\n \"\ acc_norm,none\": 0.74,\n \"acc_norm_stderr,none\": 0.027797315752644335\n\ \ },\n \"leaderboard_bbh_causal_judgement\": {\n \"alias\": \" - leaderboard_bbh_causal_judgement\"\ ,\n \"acc_norm,none\": 0.6737967914438503,\n \"acc_norm_stderr,none\"\ : 0.03437574439341202\n },\n \"leaderboard_bbh_date_understanding\": {\n \ \ \"alias\": \" - leaderboard_bbh_date_understanding\",\n \"acc_norm,none\"\ : 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n },\n \"\ leaderboard_bbh_disambiguation_qa\": {\n \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\ ,\n \"acc_norm,none\": 0.644,\n \"acc_norm_stderr,none\": 0.0303436806571532\n\ \ },\n \"leaderboard_bbh_formal_fallacies\": {\n \"alias\": \" - leaderboard_bbh_formal_fallacies\"\ ,\n \"acc_norm,none\": 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n\ \ },\n \"leaderboard_bbh_geometric_shapes\": {\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\ ,\n \"acc_norm,none\": 0.472,\n \"acc_norm_stderr,none\": 0.031636489531544396\n\ \ },\n \"leaderboard_bbh_hyperbaton\": {\n \"alias\": \" - leaderboard_bbh_hyperbaton\"\ ,\n \"acc_norm,none\": 0.676,\n \"acc_norm_stderr,none\": 0.029658294924545567\n\ \ },\n \"leaderboard_bbh_logical_deduction_five_objects\": {\n \"alias\"\ : \" - leaderboard_bbh_logical_deduction_five_objects\",\n \"acc_norm,none\"\ : 0.356,\n \"acc_norm_stderr,none\": 0.0303436806571532\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\ ,\n \"acc_norm,none\": 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n\ \ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \"\ alias\": \" - leaderboard_bbh_logical_deduction_three_objects\",\n \"acc_norm,none\"\ : 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254714\n },\n \"\ leaderboard_bbh_movie_recommendation\": {\n \"alias\": \" - leaderboard_bbh_movie_recommendation\"\ ,\n \"acc_norm,none\": 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010743\n\ \ },\n \"leaderboard_bbh_navigate\": {\n \"alias\": \" - leaderboard_bbh_navigate\"\ ,\n \"acc_norm,none\": 0.652,\n \"acc_norm_stderr,none\": 0.030186568464511673\n\ \ },\n \"leaderboard_bbh_object_counting\": {\n \"alias\": \" - leaderboard_bbh_object_counting\"\ ,\n \"acc_norm,none\": 0.368,\n \"acc_norm_stderr,none\": 0.03056207062099311\n\ \ },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"alias\": \" \ \ - leaderboard_bbh_penguins_in_a_table\",\n \"acc_norm,none\": 0.5342465753424658,\n\ \ \"acc_norm_stderr,none\": 0.04142522736934774\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\"\ : {\n \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\ ,\n \"acc_norm,none\": 0.54,\n \"acc_norm_stderr,none\": 0.031584653891499004\n\ \ },\n \"leaderboard_bbh_ruin_names\": {\n \"alias\": \" - leaderboard_bbh_ruin_names\"\ ,\n \"acc_norm,none\": 0.7,\n \"acc_norm_stderr,none\": 0.029040893477575786\n\ \ },\n \"leaderboard_bbh_salient_translation_error_detection\": {\n \ \ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\",\n \ \ \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_bbh_snarks\": {\n \"alias\": \" - leaderboard_bbh_snarks\"\ ,\n \"acc_norm,none\": 0.550561797752809,\n \"acc_norm_stderr,none\"\ : 0.037389649660569645\n },\n \"leaderboard_bbh_sports_understanding\": {\n\ \ \"alias\": \" - leaderboard_bbh_sports_understanding\",\n \"acc_norm,none\"\ : 0.788,\n \"acc_norm_stderr,none\": 0.025901884690541117\n },\n \"\ leaderboard_bbh_temporal_sequences\": {\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\ ,\n \"acc_norm,none\": 0.152,\n \"acc_norm_stderr,none\": 0.022752024491765464\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\ ,\n \"acc_norm,none\": 0.208,\n \"acc_norm_stderr,none\": 0.02572139890141637\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\ ,\n \"acc_norm,none\": 0.232,\n \"acc_norm_stderr,none\": 0.026750070374865202\n\ \ },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \ \ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\ ,\n \"acc_norm,none\": 0.352,\n \"acc_norm_stderr,none\": 0.030266288057359866\n\ \ },\n \"leaderboard_bbh_web_of_lies\": {\n \"alias\": \" - leaderboard_bbh_web_of_lies\"\ ,\n \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522374\n\ \ },\n \"leaderboard_gpqa\": {\n \"acc_norm,none\": 0.29697986577181207,\n\ \ \"acc_norm_stderr,none\": 0.0132451965442603,\n \"alias\": \" -\ \ leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\": {\n \"alias\"\ : \" - leaderboard_gpqa_diamond\",\n \"acc_norm,none\": 0.2878787878787879,\n\ \ \"acc_norm_stderr,none\": 0.03225883512300998\n },\n \"leaderboard_gpqa_extended\"\ : {\n \"alias\": \" - leaderboard_gpqa_extended\",\n \"acc_norm,none\"\ : 0.2857142857142857,\n \"acc_norm_stderr,none\": 0.019351013185102753\n\ \ },\n \"leaderboard_gpqa_main\": {\n \"alias\": \" - leaderboard_gpqa_main\"\ ,\n \"acc_norm,none\": 0.31473214285714285,\n \"acc_norm_stderr,none\"\ : 0.021965797142222607\n },\n \"leaderboard_ifeval\": {\n \"alias\"\ : \" - leaderboard_ifeval\",\n \"prompt_level_strict_acc,none\": 0.3438077634011091,\n\ \ \"prompt_level_strict_acc_stderr,none\": 0.020439793487859976,\n \ \ \"inst_level_strict_acc,none\": 0.4724220623501199,\n \"inst_level_strict_acc_stderr,none\"\ : \"N/A\",\n \"prompt_level_loose_acc,none\": 0.4491682070240296,\n \ \ \"prompt_level_loose_acc_stderr,none\": 0.021405093233588298,\n \"inst_level_loose_acc,none\"\ : 0.5623501199040767,\n \"inst_level_loose_acc_stderr,none\": \"N/A\"\n \ \ },\n \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.0702416918429003,\n\ \ \"exact_match_stderr,none\": 0.006863454031669159,\n \"alias\":\ \ \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\": {\n\ \ \"alias\": \" - leaderboard_math_algebra_hard\",\n \"exact_match,none\"\ : 0.13029315960912052,\n \"exact_match_stderr,none\": 0.019243609597826783\n\ \ },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"alias\":\ \ \" - leaderboard_math_counting_and_prob_hard\",\n \"exact_match,none\"\ : 0.032520325203252036,\n \"exact_match_stderr,none\": 0.016058998205879745\n\ \ },\n \"leaderboard_math_geometry_hard\": {\n \"alias\": \" - leaderboard_math_geometry_hard\"\ ,\n \"exact_match,none\": 0.007575757575757576,\n \"exact_match_stderr,none\"\ : 0.007575757575757577\n },\n \"leaderboard_math_intermediate_algebra_hard\"\ : {\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\",\n \ \ \"exact_match,none\": 0.0035714285714285713,\n \"exact_match_stderr,none\"\ : 0.0035714285714285713\n },\n \"leaderboard_math_num_theory_hard\": {\n \ \ \"alias\": \" - leaderboard_math_num_theory_hard\",\n \"exact_match,none\"\ : 0.07142857142857142,\n \"exact_match_stderr,none\": 0.020820824576076338\n\ \ },\n \"leaderboard_math_prealgebra_hard\": {\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\ ,\n \"exact_match,none\": 0.15025906735751296,\n \"exact_match_stderr,none\"\ : 0.025787723180723855\n },\n \"leaderboard_math_precalculus_hard\": {\n \ \ \"alias\": \" - leaderboard_math_precalculus_hard\",\n \"exact_match,none\"\ : 0.05185185185185185,\n \"exact_match_stderr,none\": 0.019154368449050496\n\ \ },\n \"leaderboard_mmlu_pro\": {\n \"alias\": \" - leaderboard_mmlu_pro\"\ ,\n \"acc,none\": 0.3562167553191489,\n \"acc_stderr,none\": 0.004365923714430882\n\ \ },\n \"leaderboard_musr\": {\n \"acc_norm,none\": 0.37962962962962965,\n\ \ \"acc_norm_stderr,none\": 0.017120448240540476,\n \"alias\": \"\ \ - leaderboard_musr\"\n },\n \"leaderboard_musr_murder_mysteries\": {\n \ \ \"alias\": \" - leaderboard_musr_murder_mysteries\",\n \"acc_norm,none\"\ : 0.552,\n \"acc_norm_stderr,none\": 0.03151438761115348\n },\n \"\ leaderboard_musr_object_placements\": {\n \"alias\": \" - leaderboard_musr_object_placements\"\ ,\n \"acc_norm,none\": 0.28125,\n \"acc_norm_stderr,none\": 0.028155620586096754\n\ \ },\n \"leaderboard_musr_team_allocation\": {\n \"alias\": \" - leaderboard_musr_team_allocation\"\ ,\n \"acc_norm,none\": 0.308,\n \"acc_norm_stderr,none\": 0.02925692860650181\n\ \ }\n}\n```" repo_url: https://huggingface.co/mkxu/llama-3-8b-po1 leaderboard_url: '' point_of_contact: '' configs: - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_boolean_expressions data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_boolean_expressions_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_causal_judgement data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_causal_judgement_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_date_understanding data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_date_understanding_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_disambiguation_qa data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_disambiguation_qa_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_formal_fallacies data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_formal_fallacies_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_geometric_shapes data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_geometric_shapes_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_hyperbaton data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_hyperbaton_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_logical_deduction_five_objects data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_logical_deduction_seven_objects data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_logical_deduction_three_objects data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_movie_recommendation data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_movie_recommendation_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_navigate data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_navigate_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_navigate_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_object_counting data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_object_counting_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_object_counting_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_penguins_in_a_table data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_penguins_in_a_table_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_reasoning_about_colored_objects data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_ruin_names data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_ruin_names_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_salient_translation_error_detection data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_snarks data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_snarks_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_snarks_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_sports_understanding data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_sports_understanding_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_temporal_sequences data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_temporal_sequences_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_tracking_shuffled_objects_five_objects data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_tracking_shuffled_objects_seven_objects data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_tracking_shuffled_objects_three_objects data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_bbh_web_of_lies data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_bbh_web_of_lies_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_gpqa_diamond data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_gpqa_diamond_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_diamond_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_gpqa_extended data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_gpqa_extended_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_extended_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_gpqa_main data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_gpqa_main_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_gpqa_main_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_ifeval data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_ifeval_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_ifeval_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_math_algebra_hard data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_math_algebra_hard_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_math_algebra_hard_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_math_counting_and_prob_hard data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_math_counting_and_prob_hard_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_math_geometry_hard data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_math_geometry_hard_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_math_geometry_hard_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_math_intermediate_algebra_hard data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_math_intermediate_algebra_hard_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_math_num_theory_hard data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_math_num_theory_hard_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_math_prealgebra_hard data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_math_prealgebra_hard_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_math_precalculus_hard data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_math_precalculus_hard_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_mmlu_pro data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_mmlu_pro_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_mmlu_pro_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_musr_murder_mysteries data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_musr_murder_mysteries_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_musr_object_placements data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_musr_object_placements_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_musr_object_placements_2024-11-30T06-55-29.252571.jsonl' - config_name: mkxu__llama-3-8b-po1__leaderboard_musr_team_allocation data_files: - split: 2024_11_30T06_55_29.252571 path: - '**/samples_leaderboard_musr_team_allocation_2024-11-30T06-55-29.252571.jsonl' - split: latest path: - '**/samples_leaderboard_musr_team_allocation_2024-11-30T06-55-29.252571.jsonl' --- # Dataset Card for Evaluation run of mkxu/llama-3-8b-po1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mkxu/llama-3-8b-po1](https://huggingface.co/mkxu/llama-3-8b-po1) The dataset is composed of 38 configuration(s), each one corresponding 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. 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/mkxu__llama-3-8b-po1-details", name="mkxu__llama-3-8b-po1__leaderboard_bbh_boolean_expressions", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-11-30T06-55-29.252571](https://huggingface.co/datasets/open-llm-leaderboard/mkxu__llama-3-8b-po1-details/blob/main/mkxu__llama-3-8b-po1/results_2024-11-30T06-55-29.252571.json) (note that there 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": { "leaderboard": { "inst_level_loose_acc,none": 0.5623501199040767, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.3438077634011091, "prompt_level_strict_acc_stderr,none": 0.020439793487859976, "exact_match,none": 0.0702416918429003, "exact_match_stderr,none": 0.006863454031669159, "acc,none": 0.3562167553191489, "acc_stderr,none": 0.004365923714430882, "prompt_level_loose_acc,none": 0.4491682070240296, "prompt_level_loose_acc_stderr,none": 0.021405093233588298, "acc_norm,none": 0.4525878842910883, "acc_norm_stderr,none": 0.005350400462318365, "inst_level_strict_acc,none": 0.4724220623501199, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4943586182954348, "acc_norm_stderr,none": 0.006221085972017242, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.74, "acc_norm_stderr,none": 0.027797315752644335 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6737967914438503, "acc_norm_stderr,none": 0.03437574439341202 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.644, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.472, "acc_norm_stderr,none": 0.031636489531544396 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.676, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.356, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.652, "acc_norm_stderr,none": 0.030186568464511673 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.5342465753424658, "acc_norm_stderr,none": 0.04142522736934774 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.54, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.7, "acc_norm_stderr,none": 0.029040893477575786 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.550561797752809, "acc_norm_stderr,none": 0.037389649660569645 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.788, "acc_norm_stderr,none": 0.025901884690541117 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.152, "acc_norm_stderr,none": 0.022752024491765464 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.208, "acc_norm_stderr,none": 0.02572139890141637 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.232, "acc_norm_stderr,none": 0.026750070374865202 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.352, "acc_norm_stderr,none": 0.030266288057359866 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_gpqa": { "acc_norm,none": 0.29697986577181207, "acc_norm_stderr,none": 0.0132451965442603, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2878787878787879, "acc_norm_stderr,none": 0.03225883512300998 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2857142857142857, "acc_norm_stderr,none": 0.019351013185102753 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.31473214285714285, "acc_norm_stderr,none": 0.021965797142222607 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.3438077634011091, "prompt_level_strict_acc_stderr,none": 0.020439793487859976, "inst_level_strict_acc,none": 0.4724220623501199, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.4491682070240296, "prompt_level_loose_acc_stderr,none": 0.021405093233588298, "inst_level_loose_acc,none": 0.5623501199040767, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.0702416918429003, "exact_match_stderr,none": 0.006863454031669159, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.13029315960912052, "exact_match_stderr,none": 0.019243609597826783 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.032520325203252036, "exact_match_stderr,none": 0.016058998205879745 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.007575757575757576, "exact_match_stderr,none": 0.007575757575757577 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0035714285714285713, "exact_match_stderr,none": 0.0035714285714285713 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.07142857142857142, "exact_match_stderr,none": 0.020820824576076338 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.15025906735751296, "exact_match_stderr,none": 0.025787723180723855 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05185185185185185, "exact_match_stderr,none": 0.019154368449050496 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3562167553191489, "acc_stderr,none": 0.004365923714430882 }, "leaderboard_musr": { "acc_norm,none": 0.37962962962962965, "acc_norm_stderr,none": 0.017120448240540476, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.28125, "acc_norm_stderr,none": 0.028155620586096754 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.308, "acc_norm_stderr,none": 0.02925692860650181 } }, "leaderboard": { "inst_level_loose_acc,none": 0.5623501199040767, "inst_level_loose_acc_stderr,none": "N/A", "prompt_level_strict_acc,none": 0.3438077634011091, "prompt_level_strict_acc_stderr,none": 0.020439793487859976, "exact_match,none": 0.0702416918429003, "exact_match_stderr,none": 0.006863454031669159, "acc,none": 0.3562167553191489, "acc_stderr,none": 0.004365923714430882, "prompt_level_loose_acc,none": 0.4491682070240296, "prompt_level_loose_acc_stderr,none": 0.021405093233588298, "acc_norm,none": 0.4525878842910883, "acc_norm_stderr,none": 0.005350400462318365, "inst_level_strict_acc,none": 0.4724220623501199, "inst_level_strict_acc_stderr,none": "N/A", "alias": "leaderboard" }, "leaderboard_bbh": { "acc_norm,none": 0.4943586182954348, "acc_norm_stderr,none": 0.006221085972017242, "alias": " - leaderboard_bbh" }, "leaderboard_bbh_boolean_expressions": { "alias": " - leaderboard_bbh_boolean_expressions", "acc_norm,none": 0.74, "acc_norm_stderr,none": 0.027797315752644335 }, "leaderboard_bbh_causal_judgement": { "alias": " - leaderboard_bbh_causal_judgement", "acc_norm,none": 0.6737967914438503, "acc_norm_stderr,none": 0.03437574439341202 }, "leaderboard_bbh_date_understanding": { "alias": " - leaderboard_bbh_date_understanding", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_disambiguation_qa": { "alias": " - leaderboard_bbh_disambiguation_qa", "acc_norm,none": 0.644, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_formal_fallacies": { "alias": " - leaderboard_bbh_formal_fallacies", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_bbh_geometric_shapes": { "alias": " - leaderboard_bbh_geometric_shapes", "acc_norm,none": 0.472, "acc_norm_stderr,none": 0.031636489531544396 }, "leaderboard_bbh_hyperbaton": { "alias": " - leaderboard_bbh_hyperbaton", "acc_norm,none": 0.676, "acc_norm_stderr,none": 0.029658294924545567 }, "leaderboard_bbh_logical_deduction_five_objects": { "alias": " - leaderboard_bbh_logical_deduction_five_objects", "acc_norm,none": 0.356, "acc_norm_stderr,none": 0.0303436806571532 }, "leaderboard_bbh_logical_deduction_seven_objects": { "alias": " - leaderboard_bbh_logical_deduction_seven_objects", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_logical_deduction_three_objects": { "alias": " - leaderboard_bbh_logical_deduction_three_objects", "acc_norm,none": 0.488, "acc_norm_stderr,none": 0.03167708558254714 }, "leaderboard_bbh_movie_recommendation": { "alias": " - leaderboard_bbh_movie_recommendation", "acc_norm,none": 0.484, "acc_norm_stderr,none": 0.03166998503010743 }, "leaderboard_bbh_navigate": { "alias": " - leaderboard_bbh_navigate", "acc_norm,none": 0.652, "acc_norm_stderr,none": 0.030186568464511673 }, "leaderboard_bbh_object_counting": { "alias": " - leaderboard_bbh_object_counting", "acc_norm,none": 0.368, "acc_norm_stderr,none": 0.03056207062099311 }, "leaderboard_bbh_penguins_in_a_table": { "alias": " - leaderboard_bbh_penguins_in_a_table", "acc_norm,none": 0.5342465753424658, "acc_norm_stderr,none": 0.04142522736934774 }, "leaderboard_bbh_reasoning_about_colored_objects": { "alias": " - leaderboard_bbh_reasoning_about_colored_objects", "acc_norm,none": 0.54, "acc_norm_stderr,none": 0.031584653891499004 }, "leaderboard_bbh_ruin_names": { "alias": " - leaderboard_bbh_ruin_names", "acc_norm,none": 0.7, "acc_norm_stderr,none": 0.029040893477575786 }, "leaderboard_bbh_salient_translation_error_detection": { "alias": " - leaderboard_bbh_salient_translation_error_detection", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_bbh_snarks": { "alias": " - leaderboard_bbh_snarks", "acc_norm,none": 0.550561797752809, "acc_norm_stderr,none": 0.037389649660569645 }, "leaderboard_bbh_sports_understanding": { "alias": " - leaderboard_bbh_sports_understanding", "acc_norm,none": 0.788, "acc_norm_stderr,none": 0.025901884690541117 }, "leaderboard_bbh_temporal_sequences": { "alias": " - leaderboard_bbh_temporal_sequences", "acc_norm,none": 0.152, "acc_norm_stderr,none": 0.022752024491765464 }, "leaderboard_bbh_tracking_shuffled_objects_five_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects", "acc_norm,none": 0.208, "acc_norm_stderr,none": 0.02572139890141637 }, "leaderboard_bbh_tracking_shuffled_objects_seven_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects", "acc_norm,none": 0.232, "acc_norm_stderr,none": 0.026750070374865202 }, "leaderboard_bbh_tracking_shuffled_objects_three_objects": { "alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects", "acc_norm,none": 0.352, "acc_norm_stderr,none": 0.030266288057359866 }, "leaderboard_bbh_web_of_lies": { "alias": " - leaderboard_bbh_web_of_lies", "acc_norm,none": 0.464, "acc_norm_stderr,none": 0.03160397514522374 }, "leaderboard_gpqa": { "acc_norm,none": 0.29697986577181207, "acc_norm_stderr,none": 0.0132451965442603, "alias": " - leaderboard_gpqa" }, "leaderboard_gpqa_diamond": { "alias": " - leaderboard_gpqa_diamond", "acc_norm,none": 0.2878787878787879, "acc_norm_stderr,none": 0.03225883512300998 }, "leaderboard_gpqa_extended": { "alias": " - leaderboard_gpqa_extended", "acc_norm,none": 0.2857142857142857, "acc_norm_stderr,none": 0.019351013185102753 }, "leaderboard_gpqa_main": { "alias": " - leaderboard_gpqa_main", "acc_norm,none": 0.31473214285714285, "acc_norm_stderr,none": 0.021965797142222607 }, "leaderboard_ifeval": { "alias": " - leaderboard_ifeval", "prompt_level_strict_acc,none": 0.3438077634011091, "prompt_level_strict_acc_stderr,none": 0.020439793487859976, "inst_level_strict_acc,none": 0.4724220623501199, "inst_level_strict_acc_stderr,none": "N/A", "prompt_level_loose_acc,none": 0.4491682070240296, "prompt_level_loose_acc_stderr,none": 0.021405093233588298, "inst_level_loose_acc,none": 0.5623501199040767, "inst_level_loose_acc_stderr,none": "N/A" }, "leaderboard_math_hard": { "exact_match,none": 0.0702416918429003, "exact_match_stderr,none": 0.006863454031669159, "alias": " - leaderboard_math_hard" }, "leaderboard_math_algebra_hard": { "alias": " - leaderboard_math_algebra_hard", "exact_match,none": 0.13029315960912052, "exact_match_stderr,none": 0.019243609597826783 }, "leaderboard_math_counting_and_prob_hard": { "alias": " - leaderboard_math_counting_and_prob_hard", "exact_match,none": 0.032520325203252036, "exact_match_stderr,none": 0.016058998205879745 }, "leaderboard_math_geometry_hard": { "alias": " - leaderboard_math_geometry_hard", "exact_match,none": 0.007575757575757576, "exact_match_stderr,none": 0.007575757575757577 }, "leaderboard_math_intermediate_algebra_hard": { "alias": " - leaderboard_math_intermediate_algebra_hard", "exact_match,none": 0.0035714285714285713, "exact_match_stderr,none": 0.0035714285714285713 }, "leaderboard_math_num_theory_hard": { "alias": " - leaderboard_math_num_theory_hard", "exact_match,none": 0.07142857142857142, "exact_match_stderr,none": 0.020820824576076338 }, "leaderboard_math_prealgebra_hard": { "alias": " - leaderboard_math_prealgebra_hard", "exact_match,none": 0.15025906735751296, "exact_match_stderr,none": 0.025787723180723855 }, "leaderboard_math_precalculus_hard": { "alias": " - leaderboard_math_precalculus_hard", "exact_match,none": 0.05185185185185185, "exact_match_stderr,none": 0.019154368449050496 }, "leaderboard_mmlu_pro": { "alias": " - leaderboard_mmlu_pro", "acc,none": 0.3562167553191489, "acc_stderr,none": 0.004365923714430882 }, "leaderboard_musr": { "acc_norm,none": 0.37962962962962965, "acc_norm_stderr,none": 0.017120448240540476, "alias": " - leaderboard_musr" }, "leaderboard_musr_murder_mysteries": { "alias": " - leaderboard_musr_murder_mysteries", "acc_norm,none": 0.552, "acc_norm_stderr,none": 0.03151438761115348 }, "leaderboard_musr_object_placements": { "alias": " - leaderboard_musr_object_placements", "acc_norm,none": 0.28125, "acc_norm_stderr,none": 0.028155620586096754 }, "leaderboard_musr_team_allocation": { "alias": " - leaderboard_musr_team_allocation", "acc_norm,none": 0.308, "acc_norm_stderr,none": 0.02925692860650181 } } ``` ## 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. 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Yuyang-z/CamVid-30K

Yuyang-z

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