Datasets:
davanstrien
/
summary-test

Dataset card Data Studio Files
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JamieWithofs/Deepfake-and-real-images-validation
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Fake '1': Real splits: - name: validation num_bytes: 221394513.687 num_examples: 2041 download_size: 225841664 dataset_size: 221394513.687 configs: - config_name: default data_files: - split: validation path: data/validation-* ---
HydraLM/partitioned_v3_standardized_011
--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_id dtype: string splits: - name: train num_bytes: 11869026.810806446 num_examples: 22073 download_size: 8319441 dataset_size: 11869026.810806446 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v3_standardized_011" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Margaret-mmh/mini-platypus
--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245925 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---
sarpba/common_voice_16.1_hu_texts
--- license: apache-2.0 --- SST with openai whisper large V3 for collect better part of the voices
savaskaplan/sk-review-dataset-sample
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: review dtype: string - name: review_length dtype: int64 splits: - name: train num_bytes: 1336616.5478017514 num_examples: 3600 - name: validation num_bytes: 148512.94975575016 num_examples: 400 download_size: 951377 dataset_size: 1485129.4975575015 --- # Dataset Card for "sk-review-dataset-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Asaf-Yehudai/HelpSteer_prompt_per_row
--- dataset_info: features: - name: prompt dtype: string - name: responses list: - name: response dtype: string - name: scores struct: - name: coherence dtype: int64 - name: complexity dtype: int64 - name: correctness dtype: int64 - name: helpfulness dtype: int64 - name: verbosity dtype: int64 splits: - name: train num_bytes: 44115062 num_examples: 9944 - name: validation num_bytes: 2267028 num_examples: 503 download_size: 25199197 dataset_size: 46382090 --- # Dataset Card for "HelpSteer_prompt_per_row" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JoaoJunior/java-encoded-small
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: rem dtype: string - name: add dtype: string - name: context dtype: string - name: meta dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 2551158 num_examples: 800 - name: test num_bytes: 641178 num_examples: 200 download_size: 391779 dataset_size: 3192336 --- # Dataset Card for "java-encoded-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Charles333/json_lama_chat_1000
--- license: apache-2.0 ---
adamjweintraut/eli5_lfqa_best_slice
--- dataset_info: features: - name: index dtype: int64 - name: q_id dtype: string - name: question dtype: string - name: best_answer dtype: string - name: all_answers sequence: string - name: num_answers dtype: int64 - name: top_answers sequence: string - name: num_top_answers dtype: int64 - name: context dtype: string - name: orig dtype: string - name: target dtype: string splits: - name: train num_bytes: 138199303 num_examples: 10000 - name: test num_bytes: 17022480 num_examples: 1250 - name: validation num_bytes: 17375258 num_examples: 1250 download_size: 103906913 dataset_size: 172597041 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
zolak/twitter_dataset_1712967588
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 1335979 num_examples: 4497 download_size: 692271 dataset_size: 1335979 configs: - config_name: default data_files: - split: train path: data/train-* ---
zamal/SKIPPD
--- license: other --- This is a dataset containing sky images and its corresponding pv panel output data. This is meant to use for Educational and Research purpose only. Commercial use of this will cause legal actions.
Seongill/squad_conflict_v2_under_150_with_substitution_chunked
--- dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers struct: - name: answer_start sequence: int64 - name: text sequence: string - name: masked_query dtype: string - name: query_embedding sequence: float64 - name: ent_type dtype: string - name: answer dtype: string - name: random_answer dtype: string - name: similar_answer dtype: string - name: rewritten_context dtype: string - name: has_answer dtype: bool - name: answer_sent dtype: string - name: rewritten_answer_sent dtype: string - name: answer_chunk dtype: string - name: rewritten_answer_chunk dtype: string splits: - name: train num_bytes: 238309199 num_examples: 25866 download_size: 153568266 dataset_size: 238309199 configs: - config_name: default data_files: - split: train path: data/train-* ---
wentingzhao/obqa
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 473240 num_examples: 5957 download_size: 318952 dataset_size: 473240 configs: - config_name: default data_files: - split: train path: data/train-* ---
CholeDYM/full_sh_for_train
--- dataset_info: features: - name: lit_image dtype: image - name: delit_image dtype: image - name: normal_image dtype: image - name: text dtype: string - name: SH_idx sequence: float32 - name: rot_idx sequence: float32 - name: gamm_ang_S sequence: int64 splits: - name: train num_bytes: 766509969.418 num_examples: 5999 download_size: 762466483 dataset_size: 766509969.418 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "full_sh_for_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hsiehpinghan/test
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int64 - name: version_id dtype: int64 splits: - name: train num_bytes: 1508 num_examples: 5 - name: test num_bytes: 956 num_examples: 5 download_size: 0 dataset_size: 2464 --- # Dataset Card for "test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TheSleepyJo/seabream-freshness_v0
--- dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 783931699.0 num_examples: 16 download_size: 51974320 dataset_size: 783931699.0 --- # Dataset Card for "seabream-freshness_v0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jjzha/imdb-dutch-instruct
--- language: - nl license: - apache-2.0 size_categories: - 10K<n<100K dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: template_lang sequence: string - name: template_id dtype: int32 splits: - name: train num_examples: 24992 - name: test num_examples: 24992 --- # Dataset Card for "imdb-dutch-instruct" ## Dataset Description The original IMBD dataset was translated to Dutch with [yhavinga/ul2-large-en-nl](https://huggingface.co/yhavinga/ul2-large-en-nl). Then, the dataset is converted to an instruct-style dataset with the following templates: The instruction templates: "Is deze recensie positief of negatief?", "Wat is het sentiment van de recensie?", "Wat voor toon heeft de volgende recensie?", "Met wat voor sentiment zou je deze recensie beoordelen?" The target templates: "De recensie is", "Gegeven de recensie, mijn antwoord is", "Deze recensie is", "De beoordeling hier is", "Het antwoord is" The template IDs are here: ```[ (0, 'Is deze recensie positief of negatief?', 'De recensie is'), (1, 'Is deze recensie positief of negatief?', 'Gegeven de recensie, mijn antwoord is'), (2, 'Is deze recensie positief of negatief?', 'Deze recensie is'), (3, 'Is deze recensie positief of negatief?', 'De beoordeling hier is'), (4, 'Is deze recensie positief of negatief?', 'Het antwoord is'), (5, 'Wat is het sentiment van de recensie?', 'De recensie is'), (6, 'Wat is het sentiment van de recensie?', 'Gegeven de recensie, mijn antwoord is'), (7, 'Wat is het sentiment van de recensie?', 'Deze recensie is'), (8, 'Wat is het sentiment van de recensie?', 'De beoordeling hier is'), (9, 'Wat is het sentiment van de recensie?', 'Het antwoord is'), (10, 'Wat voor toon heeft de volgende recensie?', 'De recensie is'), (11, 'Wat voor toon heeft de volgende recensie?', 'Gegeven de recensie, mijn antwoord is'), (12, 'Wat voor toon heeft de volgende recensie?', 'Deze recensie is'), (13, 'Wat voor toon heeft de volgende recensie?', 'De beoordeling hier is'), (14, 'Wat voor toon heeft de volgende recensie?', 'Het antwoord is'), (15, 'Met wat voor sentiment zou je deze recensie beoordelen?', 'De recensie is'), (16, 'Met wat voor sentiment zou je deze recensie beoordelen?', 'Gegeven de recensie, mijn antwoord is'), (17, 'Met wat voor sentiment zou je deze recensie beoordelen?', 'Deze recensie is'), (18, 'Met wat voor sentiment zou je deze recensie beoordelen?', 'De beoordeling hier is'), (19, 'Met wat voor sentiment zou je deze recensie beoordelen?', 'Het antwoord is') ]``` ### Dataset Summary Large Movie Review Dataset translated to Dutch converted to instruct style. This is a dataset for sentiment classification containing substantially more data than previous benchmark datasets. ### Languages and Example This dataset contains Dutch data. An example of 'train' looks as follows. ``` { "inputs": "Is deze recensie positief of negatief?\n\nIk heb alle vier de films in deze serie gezien. Elke film wijkt steeds verder af van de boeken. Deze is de ergste tot nu toe. Mijn probleem is dat hij op geen enkele manier het boek volgt waar hij naar genoemd is! De regisseurs en producenten hadden hem een andere naam moeten geven dan 'Love's Abiding Joy'. Het enige aan deze film dat ook maar in de verte op het boek lijkt, zijn de namen van sommige personages (Willie, Missie, Henry, Clark, Scottie en Cookie). De namen/ouders/verzorgers van de kinderen kloppen niet. De hele verhaallijn staat nergens in het boek. '<br />Ik vind het een grote belediging voor Janette Oke, haar boeken en haar fans om een film onder haar titel te produceren die in geen enkel opzicht correct is. De muziek is te hard. De acteurs zijn niet overtuigend <0xE2><0x80><0x93> ze missen emoties.<br />Als je een goede familiefilm wilt, is dit misschien goed. Het is schoon. Maar kijk er niet naar, als je hoopt op een verkorte versie van het boek. Ik hoop dat dit de laatste film uit deze serie zal zijn, maar ik betwijfel het. Als er meer films worden gemaakt, zou ik willen dat Michael Landon jr. en anderen dichter bij de oorspronkelijke plot en verhaallijn zouden blijven. De boeken zijn uitstekend en als je ze goed leest, zijn het uitstekende films!", "targets": "Het antwoord is negatief."} ``` ### Data Fields The data fields are the same among all splits. #### plain_text - `inputs`: a `string` feature, starting with a question whether the review is positive or negative. - `targets`: a `string` feature, with a template prefix and the final label. - `template_lang`: a `string` feature, which indicates which language the sentence is in. - `template_id`: an `int` feature, which indicates which template has been used ### Data Splits | name |train|test | |----------|----:|----:| |plain_text|24992|24992| ### Official Citation Information The original data is from here: https://huggingface.co/datasets/yhavinga/imdb_dutch ``` @InProceedings{maas-EtAl:2011:ACL-HLT2011, author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher}, title = {Learning Word Vectors for Sentiment Analysis}, booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, month = {June}, year = {2011}, address = {Portland, Oregon, USA}, publisher = {Association for Computational Linguistics}, pages = {142--150}, url = {http://www.aclweb.org/anthology/P11-1015} } ``` Created by [Mike Zhang](https://jjzha.github.io/)
irds/msmarco-document_trec-dl-hard_fold2
--- pretty_name: '`msmarco-document/trec-dl-hard/fold2`' viewer: false source_datasets: ['irds/msmarco-document'] task_categories: - text-retrieval --- # Dataset Card for `msmarco-document/trec-dl-hard/fold2` The `msmarco-document/trec-dl-hard/fold2` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/msmarco-document#msmarco-document/trec-dl-hard/fold2). # Data This dataset provides: - `queries` (i.e., topics); count=10 - `qrels`: (relevance assessments); count=1,345 - For `docs`, use [`irds/msmarco-document`](https://huggingface.co/datasets/irds/msmarco-document) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/msmarco-document_trec-dl-hard_fold2', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/msmarco-document_trec-dl-hard_fold2', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Mackie2021DlHard, title={How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset}, author={Iain Mackie and Jeffrey Dalton and Andrew Yates}, journal={ArXiv}, year={2021}, volume={abs/2105.07975} } @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} } ```
yzhuang/autotree_automl_10000_default-of-credit-card-clients_sgosdt_l256_dim10_d3_sd0
--- dataset_info: features: - name: id dtype: int64 - name: input_x sequence: sequence: float32 - name: input_y sequence: sequence: float32 - name: input_y_clean sequence: sequence: float32 - name: rtg sequence: float64 - name: status sequence: sequence: float32 - name: split_threshold sequence: sequence: float32 - name: split_dimension sequence: int64 splits: - name: train num_bytes: 236440000 num_examples: 10000 - name: validation num_bytes: 236440000 num_examples: 10000 download_size: 122258450 dataset_size: 472880000 --- # Dataset Card for "autotree_automl_10000_default-of-credit-card-clients_sgosdt_l256_dim10_d3_sd0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Stevross/mmlu
--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - mit multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - question-answering task_ids: - multiple-choice-qa paperswithcode_id: mmlu pretty_name: Measuring Massive Multitask Language Understanding language_bcp47: - en-US dataset_info: - config_name: abstract_algebra features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 19328 num_examples: 100 - name: validation num_bytes: 2024 num_examples: 11 - name: dev num_bytes: 830 num_examples: 5 download_size: 166184960 dataset_size: 160623559 - config_name: anatomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33121 num_examples: 135 - name: validation num_bytes: 3140 num_examples: 14 - name: dev num_bytes: 967 num_examples: 5 download_size: 166184960 dataset_size: 160638605 - config_name: astronomy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46771 num_examples: 152 - name: validation num_bytes: 5027 num_examples: 16 - name: dev num_bytes: 2076 num_examples: 5 download_size: 166184960 dataset_size: 160655251 - config_name: business_ethics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33252 num_examples: 100 - name: validation num_bytes: 3038 num_examples: 11 - name: dev num_bytes: 2190 num_examples: 5 download_size: 166184960 dataset_size: 160639857 - config_name: clinical_knowledge features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 62754 num_examples: 265 - name: validation num_bytes: 6664 num_examples: 29 - name: dev num_bytes: 1210 num_examples: 5 download_size: 166184960 dataset_size: 160672005 - config_name: college_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 48797 num_examples: 144 - name: validation num_bytes: 4819 num_examples: 16 - name: dev num_bytes: 1532 num_examples: 5 download_size: 166184960 dataset_size: 160656525 - config_name: college_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24708 num_examples: 100 - name: validation num_bytes: 2328 num_examples: 8 - name: dev num_bytes: 1331 num_examples: 5 download_size: 166184960 dataset_size: 160629744 - config_name: college_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42641 num_examples: 100 - name: validation num_bytes: 4663 num_examples: 11 - name: dev num_bytes: 2765 num_examples: 5 download_size: 166184960 dataset_size: 160651446 - config_name: college_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 24711 num_examples: 100 - name: validation num_bytes: 2668 num_examples: 11 - name: dev num_bytes: 1493 num_examples: 5 download_size: 166184960 dataset_size: 160630249 - config_name: college_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 82397 num_examples: 173 - name: validation num_bytes: 7909 num_examples: 22 - name: dev num_bytes: 1670 num_examples: 5 download_size: 166184960 dataset_size: 160693353 - config_name: college_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 30181 num_examples: 102 - name: validation num_bytes: 3490 num_examples: 11 - name: dev num_bytes: 1412 num_examples: 5 download_size: 166184960 dataset_size: 160636460 - config_name: computer_security features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 27124 num_examples: 100 - name: validation num_bytes: 4549 num_examples: 11 - name: dev num_bytes: 1101 num_examples: 5 download_size: 166184960 dataset_size: 160634151 - config_name: conceptual_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 40709 num_examples: 235 - name: validation num_bytes: 4474 num_examples: 26 - name: dev num_bytes: 934 num_examples: 5 download_size: 166184960 dataset_size: 160647494 - config_name: econometrics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46547 num_examples: 114 - name: validation num_bytes: 4967 num_examples: 12 - name: dev num_bytes: 1644 num_examples: 5 download_size: 166184960 dataset_size: 160654535 - config_name: electrical_engineering features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25142 num_examples: 145 - name: validation num_bytes: 2903 num_examples: 16 - name: dev num_bytes: 972 num_examples: 5 download_size: 166184960 dataset_size: 160630394 - config_name: elementary_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 70108 num_examples: 378 - name: validation num_bytes: 8988 num_examples: 41 - name: dev num_bytes: 1440 num_examples: 5 download_size: 166184960 dataset_size: 160681913 - config_name: formal_logic features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 49785 num_examples: 126 - name: validation num_bytes: 6252 num_examples: 14 - name: dev num_bytes: 1757 num_examples: 5 download_size: 166184960 dataset_size: 160659171 - config_name: global_facts features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 18403 num_examples: 100 - name: validation num_bytes: 1865 num_examples: 10 - name: dev num_bytes: 1229 num_examples: 5 download_size: 166184960 dataset_size: 160622874 - config_name: high_school_biology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 109732 num_examples: 310 - name: validation num_bytes: 11022 num_examples: 32 - name: dev num_bytes: 1673 num_examples: 5 download_size: 166184960 dataset_size: 160723804 - config_name: high_school_chemistry features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 58464 num_examples: 203 - name: validation num_bytes: 7092 num_examples: 22 - name: dev num_bytes: 1220 num_examples: 5 download_size: 166184960 dataset_size: 160668153 - config_name: high_school_computer_science features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 44476 num_examples: 100 - name: validation num_bytes: 3343 num_examples: 9 - name: dev num_bytes: 2918 num_examples: 5 download_size: 166184960 dataset_size: 160652114 - config_name: high_school_european_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 270300 num_examples: 165 - name: validation num_bytes: 29632 num_examples: 18 - name: dev num_bytes: 11564 num_examples: 5 download_size: 166184960 dataset_size: 160912873 - config_name: high_school_geography features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 42034 num_examples: 198 - name: validation num_bytes: 4332 num_examples: 22 - name: dev num_bytes: 1403 num_examples: 5 download_size: 166184960 dataset_size: 160649146 - config_name: high_school_government_and_politics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66074 num_examples: 193 - name: validation num_bytes: 7063 num_examples: 21 - name: dev num_bytes: 1779 num_examples: 5 download_size: 166184960 dataset_size: 160676293 - config_name: high_school_macroeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 117687 num_examples: 390 - name: validation num_bytes: 13020 num_examples: 43 - name: dev num_bytes: 1328 num_examples: 5 download_size: 166184960 dataset_size: 160733412 - config_name: high_school_mathematics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 54854 num_examples: 270 - name: validation num_bytes: 5765 num_examples: 29 - name: dev num_bytes: 1297 num_examples: 5 download_size: 166184960 dataset_size: 160663293 - config_name: high_school_microeconomics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 75703 num_examples: 238 - name: validation num_bytes: 7553 num_examples: 26 - name: dev num_bytes: 1298 num_examples: 5 download_size: 166184960 dataset_size: 160685931 - config_name: high_school_physics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 59538 num_examples: 151 - name: validation num_bytes: 6771 num_examples: 17 - name: dev num_bytes: 1489 num_examples: 5 download_size: 166184960 dataset_size: 160669175 - config_name: high_school_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 159407 num_examples: 545 - name: validation num_bytes: 17269 num_examples: 60 - name: dev num_bytes: 1905 num_examples: 5 download_size: 166184960 dataset_size: 160779958 - config_name: high_school_statistics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 110702 num_examples: 216 - name: validation num_bytes: 9997 num_examples: 23 - name: dev num_bytes: 2528 num_examples: 5 download_size: 166184960 dataset_size: 160724604 - config_name: high_school_us_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 296734 num_examples: 204 - name: validation num_bytes: 31706 num_examples: 22 - name: dev num_bytes: 8864 num_examples: 5 download_size: 166184960 dataset_size: 160938681 - config_name: high_school_world_history features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 378617 num_examples: 237 - name: validation num_bytes: 45501 num_examples: 26 - name: dev num_bytes: 4882 num_examples: 5 download_size: 166184960 dataset_size: 161030377 - config_name: human_aging features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 46098 num_examples: 223 - name: validation num_bytes: 4707 num_examples: 23 - name: dev num_bytes: 1008 num_examples: 5 download_size: 166184960 dataset_size: 160653190 - config_name: human_sexuality features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 32110 num_examples: 131 - name: validation num_bytes: 2421 num_examples: 12 - name: dev num_bytes: 1077 num_examples: 5 download_size: 166184960 dataset_size: 160636985 - config_name: international_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 53531 num_examples: 121 - name: validation num_bytes: 6473 num_examples: 13 - name: dev num_bytes: 2418 num_examples: 5 download_size: 166184960 dataset_size: 160663799 - config_name: jurisprudence features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33986 num_examples: 108 - name: validation num_bytes: 3729 num_examples: 11 - name: dev num_bytes: 1303 num_examples: 5 download_size: 166184960 dataset_size: 160640395 - config_name: logical_fallacies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 50117 num_examples: 163 - name: validation num_bytes: 5103 num_examples: 18 - name: dev num_bytes: 1573 num_examples: 5 download_size: 166184960 dataset_size: 160658170 - config_name: machine_learning features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 33880 num_examples: 112 - name: validation num_bytes: 3232 num_examples: 11 - name: dev num_bytes: 2323 num_examples: 5 download_size: 166184960 dataset_size: 160640812 - config_name: management features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20002 num_examples: 103 - name: validation num_bytes: 1820 num_examples: 11 - name: dev num_bytes: 898 num_examples: 5 download_size: 166184960 dataset_size: 160624097 - config_name: marketing features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 63025 num_examples: 234 - name: validation num_bytes: 7394 num_examples: 25 - name: dev num_bytes: 1481 num_examples: 5 download_size: 166184960 dataset_size: 160673277 - config_name: medical_genetics features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 20864 num_examples: 100 - name: validation num_bytes: 3005 num_examples: 11 - name: dev num_bytes: 1089 num_examples: 5 download_size: 166184960 dataset_size: 160626335 - config_name: miscellaneous features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 147704 num_examples: 783 - name: validation num_bytes: 14330 num_examples: 86 - name: dev num_bytes: 699 num_examples: 5 download_size: 166184960 dataset_size: 160764110 - config_name: moral_disputes features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 107818 num_examples: 346 - name: validation num_bytes: 12420 num_examples: 38 - name: dev num_bytes: 1755 num_examples: 5 download_size: 166184960 dataset_size: 160723370 - config_name: moral_scenarios features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 374026 num_examples: 895 - name: validation num_bytes: 42338 num_examples: 100 - name: dev num_bytes: 2058 num_examples: 5 download_size: 166184960 dataset_size: 161019799 - config_name: nutrition features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 92410 num_examples: 306 - name: validation num_bytes: 8436 num_examples: 33 - name: dev num_bytes: 2085 num_examples: 5 download_size: 166184960 dataset_size: 160704308 - config_name: philosophy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 80073 num_examples: 311 - name: validation num_bytes: 9184 num_examples: 34 - name: dev num_bytes: 988 num_examples: 5 download_size: 166184960 dataset_size: 160691622 - config_name: prehistory features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 89594 num_examples: 324 - name: validation num_bytes: 10285 num_examples: 35 - name: dev num_bytes: 1878 num_examples: 5 download_size: 166184960 dataset_size: 160703134 - config_name: professional_accounting features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 124550 num_examples: 282 - name: validation num_bytes: 14372 num_examples: 31 - name: dev num_bytes: 2148 num_examples: 5 download_size: 166184960 dataset_size: 160742447 - config_name: professional_law features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 1891762 num_examples: 1534 - name: validation num_bytes: 203519 num_examples: 170 - name: dev num_bytes: 6610 num_examples: 5 download_size: 166184960 dataset_size: 162703268 - config_name: professional_medicine features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 217561 num_examples: 272 - name: validation num_bytes: 23847 num_examples: 31 - name: dev num_bytes: 3807 num_examples: 5 download_size: 166184960 dataset_size: 160846592 - config_name: professional_psychology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 225899 num_examples: 612 - name: validation num_bytes: 29101 num_examples: 69 - name: dev num_bytes: 2267 num_examples: 5 download_size: 166184960 dataset_size: 160858644 - config_name: public_relations features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28760 num_examples: 110 - name: validation num_bytes: 4566 num_examples: 12 - name: dev num_bytes: 1496 num_examples: 5 download_size: 166184960 dataset_size: 160636199 - config_name: security_studies features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 204844 num_examples: 245 - name: validation num_bytes: 22637 num_examples: 27 - name: dev num_bytes: 5335 num_examples: 5 download_size: 166184960 dataset_size: 160834193 - config_name: sociology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 66243 num_examples: 201 - name: validation num_bytes: 7184 num_examples: 22 - name: dev num_bytes: 1613 num_examples: 5 download_size: 166184960 dataset_size: 160676417 - config_name: us_foreign_policy features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 28443 num_examples: 100 - name: validation num_bytes: 3264 num_examples: 11 - name: dev num_bytes: 1611 num_examples: 5 download_size: 166184960 dataset_size: 160634695 - config_name: virology features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 38759 num_examples: 166 - name: validation num_bytes: 5463 num_examples: 18 - name: dev num_bytes: 1096 num_examples: 5 download_size: 166184960 dataset_size: 160646695 - config_name: world_religions features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D splits: - name: auxiliary_train num_bytes: 160601377 num_examples: 99842 - name: test num_bytes: 25274 num_examples: 171 - name: validation num_bytes: 2765 num_examples: 19 - name: dev num_bytes: 670 num_examples: 5 download_size: 166184960 dataset_size: 160630086 --- # Dataset Card for MMLU ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository**: https://github.com/hendrycks/test - **Paper**: https://arxiv.org/abs/2009.03300 ### Dataset Summary [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] ### Supported Tasks and Leaderboards | Model | Authors | Humanities | Social Science | STEM | Other | Average | |------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:| | [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9 | [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9 | [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4 | Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 ### Languages English ## Dataset Structure ### Data Instances An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"], "answer": "D" } ``` ### Data Fields - `question`: a string feature - `choices`: a list of 4 string features - `answer`: a ClassLabel feature ### Data Splits - `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc. - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask | | auxiliary_train | dev | val | test | | ----- | :------: | :-----: | :-----: | :-----: | | TOTAL | 99842 | 285 | 1531 | 14042 ## Dataset Creation ### Curation Rationale Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn. ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [MIT License](https://github.com/hendrycks/test/blob/master/LICENSE) ### Citation Information If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from: ``` @article{hendryckstest2021, title={Measuring Massive Multitask Language Understanding}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ``` ### Contributions Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
category3/PDBookCovers
--- license: cc0-1.0 ---
micsell/common_voice_en
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 283633490.0 num_examples: 8000 - name: test num_bytes: 82967419.0 num_examples: 2000 download_size: 368007132 dataset_size: 366600909.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
AdapterOcean/math_dataset_standardized_cluster_3_alpaca
--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 28168082 num_examples: 18654 download_size: 13122952 dataset_size: 28168082 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "math_dataset_standardized_cluster_3_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
atsushi3110/cross-lingual-openorcha-830k-en-ja
--- license: cc-by-sa-4.0 ---
BirdL/DalleCatsAndDogs
--- dataset_info: features: - name: Images dtype: image - name: class dtype: string splits: - name: train num_bytes: 49662722.0 num_examples: 500 download_size: 49664703 dataset_size: 49662722.0 --- # Dataset Card for "DalleCatsAndDogs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_abideen__gemma-2b-openhermes
--- pretty_name: Evaluation run of abideen/gemma-2b-openhermes dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [abideen/gemma-2b-openhermes](https://huggingface.co/abideen/gemma-2b-openhermes)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_abideen__gemma-2b-openhermes\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-22T12:05:01.077115](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__gemma-2b-openhermes/blob/main/results_2024-02-22T12-05-01.077115.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.37696524742191334,\n\ \ \"acc_stderr\": 0.03381316358729798,\n \"acc_norm\": 0.3815378335823341,\n\ \ \"acc_norm_stderr\": 0.03461953317836164,\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.458323806326475,\n\ \ \"mc2_stderr\": 0.015931044127458407\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4044368600682594,\n \"acc_stderr\": 0.014342036483436172,\n\ \ \"acc_norm\": 0.439419795221843,\n \"acc_norm_stderr\": 0.01450374782358013\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4810794662417845,\n\ \ \"acc_stderr\": 0.00498620758186293,\n \"acc_norm\": 0.627365066719777,\n\ \ \"acc_norm_stderr\": 0.004825179407757572\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3851851851851852,\n\ \ \"acc_stderr\": 0.042039210401562783,\n \"acc_norm\": 0.3851851851851852,\n\ \ \"acc_norm_stderr\": 0.042039210401562783\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.3355263157894737,\n \"acc_stderr\": 0.03842498559395269,\n\ \ \"acc_norm\": 0.3355263157894737,\n \"acc_norm_stderr\": 0.03842498559395269\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n\ \ \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.48,\n \ \ \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.42641509433962266,\n \"acc_stderr\": 0.030437794342983042,\n\ \ \"acc_norm\": 0.42641509433962266,\n \"acc_norm_stderr\": 0.030437794342983042\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3402777777777778,\n\ \ \"acc_stderr\": 0.03962135573486219,\n \"acc_norm\": 0.3402777777777778,\n\ \ \"acc_norm_stderr\": 0.03962135573486219\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768077,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768077\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3583815028901734,\n\ \ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.3583815028901734,\n\ \ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.038739587141493524,\n\ \ \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.038739587141493524\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.35319148936170214,\n \"acc_stderr\": 0.031245325202761923,\n\ \ \"acc_norm\": 0.35319148936170214,\n \"acc_norm_stderr\": 0.031245325202761923\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n\ \ \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n\ \ \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.04154659671707546,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.04154659671707546\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.24867724867724866,\n \"acc_stderr\": 0.022261817692400168,\n \"\ acc_norm\": 0.24867724867724866,\n \"acc_norm_stderr\": 0.022261817692400168\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.25396825396825395,\n\ \ \"acc_stderr\": 0.03893259610604674,\n \"acc_norm\": 0.25396825396825395,\n\ \ \"acc_norm_stderr\": 0.03893259610604674\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n\ \ \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n\ \ \"acc_norm_stderr\": 0.02645087448904277\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.29064039408866993,\n \"acc_stderr\": 0.0319474007226554,\n\ \ \"acc_norm\": 0.29064039408866993,\n \"acc_norm_stderr\": 0.0319474007226554\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\"\ : 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.46060606060606063,\n \"acc_stderr\": 0.03892207016552012,\n\ \ \"acc_norm\": 0.46060606060606063,\n \"acc_norm_stderr\": 0.03892207016552012\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.4595959595959596,\n \"acc_stderr\": 0.035507024651313425,\n \"\ acc_norm\": 0.4595959595959596,\n \"acc_norm_stderr\": 0.035507024651313425\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.47668393782383417,\n \"acc_stderr\": 0.03604513672442207,\n\ \ \"acc_norm\": 0.47668393782383417,\n \"acc_norm_stderr\": 0.03604513672442207\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.3282051282051282,\n \"acc_stderr\": 0.023807633198657262,\n\ \ \"acc_norm\": 0.3282051282051282,\n \"acc_norm_stderr\": 0.023807633198657262\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2,\n \"acc_stderr\": 0.024388430433987664,\n \"acc_norm\"\ : 0.2,\n \"acc_norm_stderr\": 0.024388430433987664\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\"\ : {\n \"acc\": 0.3403361344537815,\n \"acc_stderr\": 0.030778057422931673,\n\ \ \"acc_norm\": 0.3403361344537815,\n \"acc_norm_stderr\": 0.030778057422931673\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2582781456953642,\n \"acc_stderr\": 0.035737053147634576,\n \"\ acc_norm\": 0.2582781456953642,\n \"acc_norm_stderr\": 0.035737053147634576\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.5100917431192661,\n \"acc_stderr\": 0.021432956203453316,\n \"\ acc_norm\": 0.5100917431192661,\n \"acc_norm_stderr\": 0.021432956203453316\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.2037037037037037,\n \"acc_stderr\": 0.027467401804057986,\n \"\ acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.027467401804057986\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.4215686274509804,\n \"acc_stderr\": 0.03465868196380758,\n \"\ acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.03465868196380758\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5189873417721519,\n \"acc_stderr\": 0.03252375148090448,\n \ \ \"acc_norm\": 0.5189873417721519,\n \"acc_norm_stderr\": 0.03252375148090448\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3901345291479821,\n\ \ \"acc_stderr\": 0.03273766725459157,\n \"acc_norm\": 0.3901345291479821,\n\ \ \"acc_norm_stderr\": 0.03273766725459157\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.42748091603053434,\n \"acc_stderr\": 0.04338920305792401,\n\ \ \"acc_norm\": 0.42748091603053434,\n \"acc_norm_stderr\": 0.04338920305792401\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.5206611570247934,\n \"acc_stderr\": 0.04560456086387235,\n \"\ acc_norm\": 0.5206611570247934,\n \"acc_norm_stderr\": 0.04560456086387235\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.46296296296296297,\n\ \ \"acc_stderr\": 0.04820403072760627,\n \"acc_norm\": 0.46296296296296297,\n\ \ \"acc_norm_stderr\": 0.04820403072760627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.3619631901840491,\n \"acc_stderr\": 0.037757007291414416,\n\ \ \"acc_norm\": 0.3619631901840491,\n \"acc_norm_stderr\": 0.037757007291414416\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\ \ \"acc_stderr\": 0.044642857142857116,\n \"acc_norm\": 0.33035714285714285,\n\ \ \"acc_norm_stderr\": 0.044642857142857116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.44660194174757284,\n \"acc_stderr\": 0.04922424153458933,\n\ \ \"acc_norm\": 0.44660194174757284,\n \"acc_norm_stderr\": 0.04922424153458933\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.594017094017094,\n\ \ \"acc_stderr\": 0.03217180182641086,\n \"acc_norm\": 0.594017094017094,\n\ \ \"acc_norm_stderr\": 0.03217180182641086\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.46360153256704983,\n\ \ \"acc_stderr\": 0.01783252407959326,\n \"acc_norm\": 0.46360153256704983,\n\ \ \"acc_norm_stderr\": 0.01783252407959326\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.407514450867052,\n \"acc_stderr\": 0.0264545781469315,\n\ \ \"acc_norm\": 0.407514450867052,\n \"acc_norm_stderr\": 0.0264545781469315\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2536312849162011,\n\ \ \"acc_stderr\": 0.01455155365936992,\n \"acc_norm\": 0.2536312849162011,\n\ \ \"acc_norm_stderr\": 0.01455155365936992\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.02849199358617157,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.02849199358617157\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.40192926045016075,\n\ \ \"acc_stderr\": 0.027846476005930473,\n \"acc_norm\": 0.40192926045016075,\n\ \ \"acc_norm_stderr\": 0.027846476005930473\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.027339546640662727,\n\ \ \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.027339546640662727\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3049645390070922,\n \"acc_stderr\": 0.027464708442022135,\n \ \ \"acc_norm\": 0.3049645390070922,\n \"acc_norm_stderr\": 0.027464708442022135\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.31877444589308995,\n\ \ \"acc_stderr\": 0.0119018956357861,\n \"acc_norm\": 0.31877444589308995,\n\ \ \"acc_norm_stderr\": 0.0119018956357861\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.20220588235294118,\n \"acc_stderr\": 0.024398192986654924,\n\ \ \"acc_norm\": 0.20220588235294118,\n \"acc_norm_stderr\": 0.024398192986654924\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.380718954248366,\n \"acc_stderr\": 0.019643801557924806,\n \ \ \"acc_norm\": 0.380718954248366,\n \"acc_norm_stderr\": 0.019643801557924806\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.41818181818181815,\n\ \ \"acc_stderr\": 0.0472457740573157,\n \"acc_norm\": 0.41818181818181815,\n\ \ \"acc_norm_stderr\": 0.0472457740573157\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.47346938775510206,\n \"acc_stderr\": 0.03196412734523272,\n\ \ \"acc_norm\": 0.47346938775510206,\n \"acc_norm_stderr\": 0.03196412734523272\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.43283582089552236,\n\ \ \"acc_stderr\": 0.03503490923673281,\n \"acc_norm\": 0.43283582089552236,\n\ \ \"acc_norm_stderr\": 0.03503490923673281\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4036144578313253,\n\ \ \"acc_stderr\": 0.038194861407583984,\n \"acc_norm\": 0.4036144578313253,\n\ \ \"acc_norm_stderr\": 0.038194861407583984\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03811079669833531,\n\ \ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03811079669833531\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29008567931456547,\n\ \ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.458323806326475,\n\ \ \"mc2_stderr\": 0.015931044127458407\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6093133385951065,\n \"acc_stderr\": 0.01371253603655665\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.056103108415466264,\n \ \ \"acc_stderr\": 0.006338668431321893\n }\n}\n```" repo_url: https://huggingface.co/abideen/gemma-2b-openhermes leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|arc:challenge|25_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-22T12-05-01.077115.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|gsm8k|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hellaswag|10_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-22T12-05-01.077115.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-management|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-22T12-05-01.077115.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|truthfulqa:mc|0_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-22T12-05-01.077115.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_22T12_05_01.077115 path: - '**/details_harness|winogrande|5_2024-02-22T12-05-01.077115.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-22T12-05-01.077115.parquet' - config_name: results data_files: - split: 2024_02_22T12_05_01.077115 path: - results_2024-02-22T12-05-01.077115.parquet - split: latest path: - results_2024-02-22T12-05-01.077115.parquet --- # Dataset Card for Evaluation run of abideen/gemma-2b-openhermes <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [abideen/gemma-2b-openhermes](https://huggingface.co/abideen/gemma-2b-openhermes) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_abideen__gemma-2b-openhermes", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-22T12:05:01.077115](https://huggingface.co/datasets/open-llm-leaderboard/details_abideen__gemma-2b-openhermes/blob/main/results_2024-02-22T12-05-01.077115.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.37696524742191334, "acc_stderr": 0.03381316358729798, "acc_norm": 0.3815378335823341, "acc_norm_stderr": 0.03461953317836164, "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.458323806326475, "mc2_stderr": 0.015931044127458407 }, "harness|arc:challenge|25": { "acc": 0.4044368600682594, "acc_stderr": 0.014342036483436172, "acc_norm": 0.439419795221843, "acc_norm_stderr": 0.01450374782358013 }, "harness|hellaswag|10": { "acc": 0.4810794662417845, "acc_stderr": 0.00498620758186293, "acc_norm": 0.627365066719777, "acc_norm_stderr": 0.004825179407757572 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3851851851851852, "acc_stderr": 0.042039210401562783, "acc_norm": 0.3851851851851852, "acc_norm_stderr": 0.042039210401562783 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.3355263157894737, "acc_stderr": 0.03842498559395269, "acc_norm": 0.3355263157894737, "acc_norm_stderr": 0.03842498559395269 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.48, "acc_stderr": 0.05021167315686779, "acc_norm": 0.48, "acc_norm_stderr": 0.05021167315686779 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.42641509433962266, "acc_stderr": 0.030437794342983042, "acc_norm": 0.42641509433962266, "acc_norm_stderr": 0.030437794342983042 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.3402777777777778, "acc_stderr": 0.03962135573486219, "acc_norm": 0.3402777777777778, "acc_norm_stderr": 0.03962135573486219 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.26, "acc_stderr": 0.04408440022768077, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768077 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3583815028901734, "acc_stderr": 0.036563436533531585, "acc_norm": 0.3583815028901734, "acc_norm_stderr": 0.036563436533531585 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.18627450980392157, "acc_stderr": 0.038739587141493524, "acc_norm": 0.18627450980392157, "acc_norm_stderr": 0.038739587141493524 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.35319148936170214, "acc_stderr": 0.031245325202761923, "acc_norm": 0.35319148936170214, "acc_norm_stderr": 0.031245325202761923 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.46206896551724136, "acc_stderr": 0.04154659671707546, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.04154659671707546 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.24867724867724866, "acc_stderr": 0.022261817692400168, "acc_norm": 0.24867724867724866, "acc_norm_stderr": 0.022261817692400168 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.25396825396825395, "acc_stderr": 0.03893259610604674, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.03893259610604674 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3161290322580645, "acc_stderr": 0.02645087448904277, "acc_norm": 0.3161290322580645, "acc_norm_stderr": 0.02645087448904277 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.29064039408866993, "acc_stderr": 0.0319474007226554, "acc_norm": 0.29064039408866993, "acc_norm_stderr": 0.0319474007226554 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.46060606060606063, "acc_stderr": 0.03892207016552012, "acc_norm": 0.46060606060606063, "acc_norm_stderr": 0.03892207016552012 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.4595959595959596, "acc_stderr": 0.035507024651313425, "acc_norm": 0.4595959595959596, "acc_norm_stderr": 0.035507024651313425 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.47668393782383417, "acc_stderr": 0.03604513672442207, "acc_norm": 0.47668393782383417, "acc_norm_stderr": 0.03604513672442207 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.3282051282051282, "acc_stderr": 0.023807633198657262, "acc_norm": 0.3282051282051282, "acc_norm_stderr": 0.023807633198657262 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2, "acc_stderr": 0.024388430433987664, "acc_norm": 0.2, "acc_norm_stderr": 0.024388430433987664 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.3403361344537815, "acc_stderr": 0.030778057422931673, "acc_norm": 0.3403361344537815, "acc_norm_stderr": 0.030778057422931673 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2582781456953642, "acc_stderr": 0.035737053147634576, "acc_norm": 0.2582781456953642, "acc_norm_stderr": 0.035737053147634576 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5100917431192661, "acc_stderr": 0.021432956203453316, "acc_norm": 0.5100917431192661, "acc_norm_stderr": 0.021432956203453316 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2037037037037037, "acc_stderr": 0.027467401804057986, "acc_norm": 0.2037037037037037, "acc_norm_stderr": 0.027467401804057986 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.4215686274509804, "acc_stderr": 0.03465868196380758, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.03465868196380758 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5189873417721519, "acc_stderr": 0.03252375148090448, "acc_norm": 0.5189873417721519, "acc_norm_stderr": 0.03252375148090448 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.3901345291479821, "acc_stderr": 0.03273766725459157, "acc_norm": 0.3901345291479821, "acc_norm_stderr": 0.03273766725459157 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.42748091603053434, "acc_stderr": 0.04338920305792401, "acc_norm": 0.42748091603053434, "acc_norm_stderr": 0.04338920305792401 }, "harness|hendrycksTest-international_law|5": { "acc": 0.5206611570247934, "acc_stderr": 0.04560456086387235, "acc_norm": 0.5206611570247934, "acc_norm_stderr": 0.04560456086387235 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.46296296296296297, "acc_stderr": 0.04820403072760627, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.04820403072760627 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3619631901840491, "acc_stderr": 0.037757007291414416, "acc_norm": 0.3619631901840491, "acc_norm_stderr": 0.037757007291414416 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.33035714285714285, "acc_stderr": 0.044642857142857116, "acc_norm": 0.33035714285714285, "acc_norm_stderr": 0.044642857142857116 }, "harness|hendrycksTest-management|5": { "acc": 0.44660194174757284, "acc_stderr": 0.04922424153458933, "acc_norm": 0.44660194174757284, "acc_norm_stderr": 0.04922424153458933 }, "harness|hendrycksTest-marketing|5": { "acc": 0.594017094017094, "acc_stderr": 0.03217180182641086, "acc_norm": 0.594017094017094, "acc_norm_stderr": 0.03217180182641086 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.46360153256704983, "acc_stderr": 0.01783252407959326, "acc_norm": 0.46360153256704983, "acc_norm_stderr": 0.01783252407959326 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.407514450867052, "acc_stderr": 0.0264545781469315, "acc_norm": 0.407514450867052, "acc_norm_stderr": 0.0264545781469315 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2536312849162011, "acc_stderr": 0.01455155365936992, "acc_norm": 0.2536312849162011, "acc_norm_stderr": 0.01455155365936992 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.45098039215686275, "acc_stderr": 0.02849199358617157, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.02849199358617157 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.40192926045016075, "acc_stderr": 0.027846476005930473, "acc_norm": 0.40192926045016075, "acc_norm_stderr": 0.027846476005930473 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4074074074074074, "acc_stderr": 0.027339546640662727, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.027339546640662727 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3049645390070922, "acc_stderr": 0.027464708442022135, "acc_norm": 0.3049645390070922, "acc_norm_stderr": 0.027464708442022135 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.31877444589308995, "acc_stderr": 0.0119018956357861, "acc_norm": 0.31877444589308995, "acc_norm_stderr": 0.0119018956357861 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.20220588235294118, "acc_stderr": 0.024398192986654924, "acc_norm": 0.20220588235294118, "acc_norm_stderr": 0.024398192986654924 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.380718954248366, "acc_stderr": 0.019643801557924806, "acc_norm": 0.380718954248366, "acc_norm_stderr": 0.019643801557924806 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.41818181818181815, "acc_stderr": 0.0472457740573157, "acc_norm": 0.41818181818181815, "acc_norm_stderr": 0.0472457740573157 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.47346938775510206, "acc_stderr": 0.03196412734523272, "acc_norm": 0.47346938775510206, "acc_norm_stderr": 0.03196412734523272 }, "harness|hendrycksTest-sociology|5": { "acc": 0.43283582089552236, "acc_stderr": 0.03503490923673281, "acc_norm": 0.43283582089552236, "acc_norm_stderr": 0.03503490923673281 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-virology|5": { "acc": 0.4036144578313253, "acc_stderr": 0.038194861407583984, "acc_norm": 0.4036144578313253, "acc_norm_stderr": 0.038194861407583984 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.4444444444444444, "acc_stderr": 0.03811079669833531, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.03811079669833531 }, "harness|truthfulqa:mc|0": { "mc1": 0.29008567931456547, "mc1_stderr": 0.01588623687420952, "mc2": 0.458323806326475, "mc2_stderr": 0.015931044127458407 }, "harness|winogrande|5": { "acc": 0.6093133385951065, "acc_stderr": 0.01371253603655665 }, "harness|gsm8k|5": { "acc": 0.056103108415466264, "acc_stderr": 0.006338668431321893 } } ``` ## 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]
andrewsunanda/fast_food_image_classification
--- task_categories: - image-classification language: - en ---
james-burton/aug-text-exps-v3
--- dataset_info: features: - name: model_name dtype: string - name: predicted_class dtype: string - name: task_name dtype: string - name: narration dtype: string - name: values sequence: string - name: sign sequence: string - name: narrative_id dtype: int32 - name: unique_id dtype: int32 - name: classes_dict dtype: string - name: narrative_questions sequence: string - name: feature_nums sequence: string - name: ft_num2name dtype: string - name: old2new_ft_nums dtype: string - name: old2new_classes dtype: string - name: predicted_class_label dtype: string - name: class2name dtype: string splits: - name: train num_bytes: 8651458 num_examples: 3280 - name: validation num_bytes: 121591 num_examples: 47 - name: test num_bytes: 252513 num_examples: 94 download_size: 2382860 dataset_size: 9025562 --- # Dataset Card for "aug-text-exps-v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
VedCodes/New_dataset_llm
--- task_categories: - text-generation language: - en tags: - medical size_categories: - n<1K ---
ChirathD/sinCorpus
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 99188239 num_examples: 43328 download_size: 41545918 dataset_size: 99188239 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_Azazelle__SlimMelodicMaid
--- pretty_name: Evaluation run of Azazelle/SlimMelodicMaid dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Azazelle/SlimMelodicMaid](https://huggingface.co/Azazelle/SlimMelodicMaid) on\ \ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Azazelle__SlimMelodicMaid\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-30T02:54:45.572792](https://huggingface.co/datasets/open-llm-leaderboard/details_Azazelle__SlimMelodicMaid/blob/main/results_2023-12-30T02-54-45.572792.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6495626338556433,\n\ \ \"acc_stderr\": 0.03193676074571155,\n \"acc_norm\": 0.6515021650371259,\n\ \ \"acc_norm_stderr\": 0.03257111121158258,\n \"mc1\": 0.43084455324357407,\n\ \ \"mc1_stderr\": 0.017335272475332363,\n \"mc2\": 0.6087927851947197,\n\ \ \"mc2_stderr\": 0.015566919235032412\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6424914675767918,\n \"acc_stderr\": 0.014005494275916576,\n\ \ \"acc_norm\": 0.6715017064846417,\n \"acc_norm_stderr\": 0.013724978465537302\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6796454889464251,\n\ \ \"acc_stderr\": 0.0046565916786067574,\n \"acc_norm\": 0.8600876319458275,\n\ \ \"acc_norm_stderr\": 0.0034618713240671954\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\ \ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\ \ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\ \ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6127659574468085,\n \"acc_stderr\": 0.03184389265339526,\n\ \ \"acc_norm\": 0.6127659574468085,\n \"acc_norm_stderr\": 0.03184389265339526\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"\ acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n\ \ \"acc_stderr\": 0.023157879349083522,\n \"acc_norm\": 0.7903225806451613,\n\ \ \"acc_norm_stderr\": 0.023157879349083522\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026705,\n \"\ acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026705\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.02390115797940253,\n \ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.02390115797940253\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7310924369747899,\n \"acc_stderr\": 0.02880139219363127,\n \ \ \"acc_norm\": 0.7310924369747899,\n \"acc_norm_stderr\": 0.02880139219363127\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\ acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7354260089686099,\n\ \ \"acc_stderr\": 0.029605103217038325,\n \"acc_norm\": 0.7354260089686099,\n\ \ \"acc_norm_stderr\": 0.029605103217038325\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097654,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097654\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822585,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822585\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\ \ \"acc_stderr\": 0.013664230995834829,\n \"acc_norm\": 0.822477650063857,\n\ \ \"acc_norm_stderr\": 0.013664230995834829\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.02344582627654554,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.02344582627654554\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3743016759776536,\n\ \ \"acc_stderr\": 0.01618544417945717,\n \"acc_norm\": 0.3743016759776536,\n\ \ \"acc_norm_stderr\": 0.01618544417945717\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n\ \ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4869621903520209,\n\ \ \"acc_stderr\": 0.012765893883835332,\n \"acc_norm\": 0.4869621903520209,\n\ \ \"acc_norm_stderr\": 0.012765893883835332\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7095588235294118,\n \"acc_stderr\": 0.027576468622740543,\n\ \ \"acc_norm\": 0.7095588235294118,\n \"acc_norm_stderr\": 0.027576468622740543\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6454248366013072,\n \"acc_stderr\": 0.0193533605475537,\n \ \ \"acc_norm\": 0.6454248366013072,\n \"acc_norm_stderr\": 0.0193533605475537\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\ \ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\ \ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7591836734693878,\n \"acc_stderr\": 0.02737294220178816,\n\ \ \"acc_norm\": 0.7591836734693878,\n \"acc_norm_stderr\": 0.02737294220178816\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.025196929874827075\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.43084455324357407,\n\ \ \"mc1_stderr\": 0.017335272475332363,\n \"mc2\": 0.6087927851947197,\n\ \ \"mc2_stderr\": 0.015566919235032412\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090254\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6080363912054587,\n \ \ \"acc_stderr\": 0.013447140886023815\n }\n}\n```" repo_url: https://huggingface.co/Azazelle/SlimMelodicMaid leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|arc:challenge|25_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-30T02-54-45.572792.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|gsm8k|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hellaswag|10_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-54-45.572792.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-54-45.572792.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-30T02-54-45.572792.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_30T02_54_45.572792 path: - '**/details_harness|winogrande|5_2023-12-30T02-54-45.572792.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-30T02-54-45.572792.parquet' - config_name: results data_files: - split: 2023_12_30T02_54_45.572792 path: - results_2023-12-30T02-54-45.572792.parquet - split: latest path: - results_2023-12-30T02-54-45.572792.parquet --- # Dataset Card for Evaluation run of Azazelle/SlimMelodicMaid <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Azazelle/SlimMelodicMaid](https://huggingface.co/Azazelle/SlimMelodicMaid) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Azazelle__SlimMelodicMaid", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-30T02:54:45.572792](https://huggingface.co/datasets/open-llm-leaderboard/details_Azazelle__SlimMelodicMaid/blob/main/results_2023-12-30T02-54-45.572792.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.6495626338556433, "acc_stderr": 0.03193676074571155, "acc_norm": 0.6515021650371259, "acc_norm_stderr": 0.03257111121158258, "mc1": 0.43084455324357407, "mc1_stderr": 0.017335272475332363, "mc2": 0.6087927851947197, "mc2_stderr": 0.015566919235032412 }, "harness|arc:challenge|25": { "acc": 0.6424914675767918, "acc_stderr": 0.014005494275916576, "acc_norm": 0.6715017064846417, "acc_norm_stderr": 0.013724978465537302 }, "harness|hellaswag|10": { "acc": 0.6796454889464251, "acc_stderr": 0.0046565916786067574, "acc_norm": 0.8600876319458275, "acc_norm_stderr": 0.0034618713240671954 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595853, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595853 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933714, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933714 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6473988439306358, "acc_stderr": 0.03643037168958548, "acc_norm": 0.6473988439306358, "acc_norm_stderr": 0.03643037168958548 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6127659574468085, "acc_stderr": 0.03184389265339526, "acc_norm": 0.6127659574468085, "acc_norm_stderr": 0.03184389265339526 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42592592592592593, "acc_stderr": 0.02546714904546955, "acc_norm": 0.42592592592592593, "acc_norm_stderr": 0.02546714904546955 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04426266681379909, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04426266681379909 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083522, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083522 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175008, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7929292929292929, "acc_stderr": 0.02886977846026705, "acc_norm": 0.7929292929292929, "acc_norm_stderr": 0.02886977846026705 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.02390115797940253, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.02390115797940253 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.028972648884844267, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.028972648884844267 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7310924369747899, "acc_stderr": 0.02880139219363127, "acc_norm": 0.7310924369747899, "acc_norm_stderr": 0.02880139219363127 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926917, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926917 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7354260089686099, "acc_stderr": 0.029605103217038325, "acc_norm": 0.7354260089686099, "acc_norm_stderr": 0.029605103217038325 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596914, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596914 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8181818181818182, "acc_stderr": 0.03520893951097654, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.03520893951097654 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness|hendrycksTest-management|5": { "acc": 0.8058252427184466, "acc_stderr": 0.03916667762822585, "acc_norm": 0.8058252427184466, "acc_norm_stderr": 0.03916667762822585 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165612, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165612 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.822477650063857, "acc_stderr": 0.013664230995834829, "acc_norm": 0.822477650063857, "acc_norm_stderr": 0.013664230995834829 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7456647398843931, "acc_stderr": 0.02344582627654554, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.02344582627654554 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3743016759776536, "acc_stderr": 0.01618544417945717, "acc_norm": 0.3743016759776536, "acc_norm_stderr": 0.01618544417945717 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7438271604938271, "acc_stderr": 0.024288533637726095, "acc_norm": 0.7438271604938271, "acc_norm_stderr": 0.024288533637726095 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4869621903520209, "acc_stderr": 0.012765893883835332, "acc_norm": 0.4869621903520209, "acc_norm_stderr": 0.012765893883835332 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7095588235294118, "acc_stderr": 0.027576468622740543, "acc_norm": 0.7095588235294118, "acc_norm_stderr": 0.027576468622740543 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6454248366013072, "acc_stderr": 0.0193533605475537, "acc_norm": 0.6454248366013072, "acc_norm_stderr": 0.0193533605475537 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6454545454545455, "acc_stderr": 0.045820048415054174, "acc_norm": 0.6454545454545455, "acc_norm_stderr": 0.045820048415054174 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7591836734693878, "acc_stderr": 0.02737294220178816, "acc_norm": 0.7591836734693878, "acc_norm_stderr": 0.02737294220178816 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.025196929874827075, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.025196929874827075 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness|truthfulqa:mc|0": { "mc1": 0.43084455324357407, "mc1_stderr": 0.017335272475332363, "mc2": 0.6087927851947197, "mc2_stderr": 0.015566919235032412 }, "harness|winogrande|5": { "acc": 0.7861089187056038, "acc_stderr": 0.011524466954090254 }, "harness|gsm8k|5": { "acc": 0.6080363912054587, "acc_stderr": 0.013447140886023815 } } ``` ## 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]
IceMasterT/BTC-Data-Daily-2014-2023
--- license: mit task_categories: - token-classification - text-classification language: - en tags: - finance pretty_name: Bitcoin Data size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
CyberHarem/dusevnyj_neuralcloud
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of dusevnyj (Neural Cloud) This is the dataset of dusevnyj (Neural Cloud), containing 12 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
HydraIndicLM/odia_alpaca_dolly_67k
--- dataset_info: features: - name: output dtype: string - name: instruction dtype: string - name: input dtype: string splits: - name: train num_bytes: 117352849 num_examples: 64389 download_size: 44003356 dataset_size: 117352849 configs: - config_name: default data_files: - split: train path: data/train-* --- ## About This repo contains a 67K instruction set for Odia, translated from Alpaca and Dolly. ## Citation If you find this repository useful, please consider giving 👏 and citing: ``` @misc{OdiaAlpacaDolly, author = {Sambit Sekhar and Shantipriya Parida}, title = {Odia Instruction Set Based on Alpaca and Dolly}, year = {2023}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/OdiaGenAI}}, } ```
heliosprime/twitter_dataset_1712970354
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 6184 num_examples: 14 download_size: 8268 dataset_size: 6184 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1712970354" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlignmentResearch/IMDB
--- dataset_info: - config_name: default features: - name: text dtype: string - name: clf_label dtype: class_label: names: '0': neg '1': pos - name: chunked_text sequence: string splits: - name: train num_bytes: 61222150 num_examples: 24477 - name: validation num_bytes: 60091078 num_examples: 24513 download_size: 79107768 dataset_size: 121313228 - config_name: neg features: - name: text dtype: string - name: clf_label dtype: class_label: names: '0': neg '1': pos - name: chunked_text sequence: string splits: - name: train num_bytes: 30669853.46651959 num_examples: 12262 - name: validation num_bytes: 30098244.020886876 num_examples: 12278 download_size: 39410373 dataset_size: 60768097.48740646 - config_name: pos features: - name: text dtype: string - name: clf_label dtype: class_label: names: '0': neg '1': pos - name: chunked_text sequence: string splits: - name: train num_bytes: 30552296.53348041 num_examples: 12215 - name: validation num_bytes: 29992833.979113124 num_examples: 12235 download_size: 39739964 dataset_size: 60545130.51259354 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - config_name: neg data_files: - split: train path: neg/train-* - split: validation path: neg/validation-* - config_name: pos data_files: - split: train path: pos/train-* - split: validation path: pos/validation-* ---
Freela/leom
--- license: openrail ---
liuyanchen1015/MULTI_VALUE_qqp_definite_for_indefinite_articles
--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 2270650 num_examples: 13219 - name: test num_bytes: 23668466 num_examples: 137338 - name: train num_bytes: 20579019 num_examples: 119383 download_size: 28797821 dataset_size: 46518135 --- # Dataset Card for "MULTI_VALUE_qqp_definite_for_indefinite_articles" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sofoklis/stem_like
--- dataset_info: features: - name: number dtype: int64 - name: name dtype: string - name: sequence dtype: string - name: spaced_sequence dtype: string - name: array sequence: sequence: float64 - name: image dtype: image splits: - name: train num_bytes: 347058.9 num_examples: 90 - name: test num_bytes: 38562.1 num_examples: 10 - name: valid num_bytes: 69411.78 num_examples: 18 download_size: 97724 dataset_size: 455032.78 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
rntc/few_shot_ncbi_disease_wikipedia
--- dataset_info: features: - name: prompt dtype: string - name: gold dtype: string - name: doc_id dtype: int64 - name: sent_offset sequence: int64 - name: sent_len dtype: int64 - name: context dtype: string splits: - name: train num_bytes: 4358257 num_examples: 978 download_size: 664033 dataset_size: 4358257 configs: - config_name: default data_files: - split: train path: data/train-* ---
shivam9980/Gemma-news-hindi
--- license: apache-2.0 ---
kaleemWaheed/twitter_dataset_1713105477
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 27976 num_examples: 66 download_size: 16111 dataset_size: 27976 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-x1-en-ko
--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string splits: - name: train num_bytes: 91997 num_examples: 105 download_size: 50077 dataset_size: 91997 configs: - config_name: default data_files: - split: train path: 3d8d18c1775c05f6/train-* ---
result-kand2-sdxl-wuerst-karlo/16193144
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 169 num_examples: 10 download_size: 1324 dataset_size: 169 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "16193144" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CM/codexglue_codetrans
--- dataset_info: features: - name: id dtype: int32 - name: java dtype: string - name: cs dtype: string splits: - name: train num_bytes: 4372641 num_examples: 10300 - name: validation num_bytes: 226407 num_examples: 500 - name: test num_bytes: 418587 num_examples: 1000 download_size: 0 dataset_size: 5017635 --- # Dataset Card for "codexglue_codetrans" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shreevigneshs/iwslt-2023-en-es-train-val-split-0.1
--- dataset_info: features: - name: en dtype: string - name: es dtype: string - name: es_annotated dtype: string - name: styles dtype: int64 splits: - name: train num_bytes: 258566.0 num_examples: 720 - name: val num_bytes: 28014.0 num_examples: 80 - name: if_test num_bytes: 225583.0 num_examples: 600 - name: f_test num_bytes: 225065.0 num_examples: 600 download_size: 283452 dataset_size: 737228.0 --- # Dataset Card for "iwslt-2023-en-es-train-val-split-0.1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BangumiBase/citrus
--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Citrus This is the image base of bangumi Citrus, we detected 18 characters, 1393 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 374 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 58 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 49 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 29 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 17 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 73 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 241 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 30 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 97 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 15 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 7 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | N/A | | 11 | 24 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 31 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 11 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 90 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 76 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 44 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | noise | 127 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
Nadav/pixel_glue_qqp_high_noise
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: validation num_bytes: 1445009192.25 num_examples: 40430 download_size: 1443796278 dataset_size: 1445009192.25 --- # Dataset Card for "pixel_glue_qqp_high_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tr416/dataset_20231006_234030
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 762696.0 num_examples: 297 - name: test num_bytes: 7704.0 num_examples: 3 download_size: 73965 dataset_size: 770400.0 --- # Dataset Card for "dataset_20231006_234030" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CHEN0312/gssgd
--- license: apache-2.0 ---
liuyanchen1015/MULTI_VALUE_qqp_a_ing
--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1769080 num_examples: 9569 - name: test num_bytes: 17265757 num_examples: 94216 - name: train num_bytes: 15748086 num_examples: 84826 download_size: 21690691 dataset_size: 34782923 --- # Dataset Card for "MULTI_VALUE_qqp_a_ing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vinicm/modelomichelle
--- license: openrail ---
CyberHarem/kamisato_ayaka_genshin
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kamisato_ayaka/神里綾華/神里绫华 (Genshin Impact) This is the dataset of kamisato_ayaka/神里綾華/神里绫华 (Genshin Impact), containing 500 images and their tags. The core tags of this character are `blue_eyes, blunt_bangs, long_hair, ribbon, ponytail, hair_ribbon, blue_hair, sidelocks, mole_under_eye, mole, hair_ornament, breasts, tress_ribbon, light_blue_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:---------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 1.36 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kamisato_ayaka_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 1.08 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kamisato_ayaka_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1400 | 2.16 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kamisato_ayaka_genshin/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kamisato_ayaka_genshin', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, alternate_costume, looking_at_viewer, white_dress, bare_shoulders, blush, solo, smile, closed_mouth, medium_breasts, sleeveless_dress, bow, white_hair, bare_arms, blunt_tresses, cleavage, collarbone, spaghetti_strap, flower, outdoors, very_long_hair | | 1 | 47 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, breastplate, japanese_armor, japanese_clothes, solo, folding_fan, holding_fan, looking_at_viewer, armored_dress, smile, arm_guards, gloves, bridal_gauntlets, closed_mouth, blush, petals, tassel, blue_skirt | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, breastplate, holding_sword, japanese_armor, japanese_clothes, solo, katana, looking_at_viewer, arm_guards, tassel, armored_dress, bridal_gauntlets, blue_skirt, closed_mouth, gloves, petals | | 3 | 22 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, solo, looking_at_viewer, wide_sleeves, long_sleeves, obi, blue_kimono, floral_print, smile, hair_flower, blush, closed_mouth, folding_fan, holding_fan, alternate_costume, very_long_hair | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, bare_shoulders, looking_at_viewer, solo, blush, chest_sarashi, cleavage, medium_breasts, closed_mouth, off_shoulder, indoors, kimono, skirt, very_long_hair, collarbone, cup, large_breasts, petals, smile, tassel, thighs | | 5 | 22 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, white_shirt, looking_at_viewer, pleated_skirt, serafuku, solo, hair_bow, long_sleeves, blue_skirt, blush, smile, alternate_costume, blunt_tresses, white_hair, white_sailor_collar, school_bag, outdoors, blue_sky, cowboy_shot, closed_mouth, day, blue_neckerchief, cloud, thighs, very_long_hair | | 6 | 15 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, looking_at_viewer, solo, thighs, bare_shoulders, navel, outdoors, stomach, blush, cleavage, very_long_hair, collarbone, blue_sky, cowboy_shot, water, alternate_costume, day, wet, bare_arms, cloud, large_breasts, medium_breasts, smile, white_hair, flower_knot, halterneck, ocean, parted_lips, standing, white_bikini, blunt_tresses | | 7 | 32 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, blue_dress, butterfly_hair_ornament, hair_flower, official_alternate_costume, official_alternate_hairstyle, solo, puffy_long_sleeves, braid, blunt_tresses, brown_headwear, looking_at_viewer, white_collar, multicolored_dress, smile, hat_flower, white_pantyhose, blush, medium_breasts, holding, neck_tassel, outdoors, white_flower, back_bow, blue_butterfly, closed_mouth, hand_up, petals | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | alternate_costume | looking_at_viewer | white_dress | bare_shoulders | blush | solo | smile | closed_mouth | medium_breasts | sleeveless_dress | bow | white_hair | bare_arms | blunt_tresses | cleavage | collarbone | spaghetti_strap | flower | outdoors | very_long_hair | breastplate | japanese_armor | japanese_clothes | folding_fan | holding_fan | armored_dress | arm_guards | gloves | bridal_gauntlets | petals | tassel | blue_skirt | holding_sword | katana | wide_sleeves | long_sleeves | obi | blue_kimono | floral_print | hair_flower | chest_sarashi | off_shoulder | indoors | kimono | skirt | cup | large_breasts | thighs | white_shirt | pleated_skirt | serafuku | hair_bow | white_sailor_collar | school_bag | blue_sky | cowboy_shot | day | blue_neckerchief | cloud | navel | stomach | water | wet | flower_knot | halterneck | ocean | parted_lips | standing | white_bikini | blue_dress | butterfly_hair_ornament | official_alternate_costume | official_alternate_hairstyle | puffy_long_sleeves | braid | brown_headwear | white_collar | multicolored_dress | hat_flower | white_pantyhose | holding | neck_tassel | white_flower | back_bow | blue_butterfly | hand_up | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------------------|:--------------|:-----------------|:--------|:-------|:--------|:---------------|:-----------------|:-------------------|:------|:-------------|:------------|:----------------|:-----------|:-------------|:------------------|:---------|:-----------|:-----------------|:--------------|:-----------------|:-------------------|:--------------|:--------------|:----------------|:-------------|:---------|:-------------------|:---------|:---------|:-------------|:----------------|:---------|:---------------|:---------------|:------|:--------------|:---------------|:--------------|:----------------|:---------------|:----------|:---------|:--------|:------|:----------------|:---------|:--------------|:----------------|:-----------|:-----------|:----------------------|:-------------|:-----------|:--------------|:------|:-------------------|:--------|:--------|:----------|:--------|:------|:--------------|:-------------|:--------|:--------------|:-----------|:---------------|:-------------|:--------------------------|:-----------------------------|:-------------------------------|:---------------------|:--------|:-----------------|:---------------|:---------------------|:-------------|:------------------|:----------|:--------------|:---------------|:-----------|:-----------------|:----------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 47 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | | | X | X | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 12 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | | | X | | X | | | | | | | | | | | | | X | X | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 22 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | | X | X | X | X | | | | | | | | | | | | X | | | | X | X | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | | X | X | X | X | X | X | | | | | | X | X | | | | X | | | | | | | | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 22 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | | | X | X | X | X | | | | X | | X | | | | | X | X | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 15 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | | X | X | X | X | | X | | | X | X | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 7 | 32 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | X | | | X | X | X | X | X | | | | | X | | | | | X | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
Nyameri/AIXDR
--- license: mit task_categories: - summarization - feature-extraction pretty_name: AI threat Hunter's playbook size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name <!-- AI XDR playbook --> 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 <!-- AI xdr paper XDR (Extended Detection and Response) is a security solution that combines multiple detection and response technologies to provide a more comprehensive view of an organization's security posture, making it easier to recognize and respond to potential threats[1]. AI/ML (Artificial Intelligence/Machine Learning) is a key component of XDR, as it enables advanced analytics techniques to identify potential threats and automate response actions[1][2]. Here are some ways in which AI enhances XDR platforms: - **Advanced analytics**: XDR solutions use advanced analytics techniques supported by machine learning (ML) models to identify potential threats and automate response actions[1][5]. - **Automated response**: XDR solutions can automatically block or quarantine malicious files and alert security teams to potential incidents[1]. - **Single pane of glass view**: XDR solutions provide a unified view of all security events and incidents, making it easier for security teams to investigate and respond to threats[1]. - **Detecting unknown or zero-day threats**: AI-powered XDR solutions can detect unknown or zero-day threats, making them more effective than traditional detection and response technologies that rely on rule-based or signature-based detection methods[1][5]. - **Predicting future cyberattacks**: AI is able to predict future cyberattacks and identify their mechanisms to determine their origin, accelerating responses to attacks[5]. XDR platforms with AI can perform analyses on every layer of an organization's infrastructure, including those that were previously inaccessible to analysts[5]. AI analyzes logs and compares current activities on an organization's infrastructure to detect any unusual action on all its infrastructures, including servers, workstations, and networks[5]. Additionally, an AI-powered XDR with Next Generation Antivirus (NGAV) can detect unknown malicious files[5]. If an anomaly is detected, the sensors immediately send the information back to the XDR, which can automatically prioritize alerts so that security teams can immediately respond to potential threats[5]. Citations: [1] Machine Learning and Artificial Intelligence (AI/ML): The Secret Sauce Behind XDR https://www.computer.org/publications/tech-news/trends/the-secret-sauce-behind-xdr/ [2] AI-Driven XDR: Defeating the Most Complex Attack Sequences - Cybereason https://www.cybereason.com/blog/ai-driven-xdr-defeating-the-most-complex-attack-sequences [3] Harnessing the Power of AI-Driven XDR - Cybereason https://www.cybereason.com/blog/harnessing-the-power-of-ai-driven-xdr [4] Explainable dimensionality reduction (XDR) to unbox AI 'black box' models: A study of AI perspectives on the ethnic styles of village dwellings - Nature https://www.nature.com/articles/s41599-023-01505-4 [5] How does AI enhance XDR platforms? - TEHTRIS https://tehtris.com/en/blog/how-does-ai-enhance-xdr-platforms [6] XDR Should Be Viewed as An Open Architecture - Vectra AI https://www.vectra.ai/resources/research-reports/esg-xdr-open-architecture By Perplexity at https://www.perplexity.ai/search/fd37ce22-dccf-4aa9-8478-d24cf6db23c4?s=m --> - **Curated by:** [Edward Nyameri ] - **Funded by [optional]:** [Nil funding but any interested POC is welcome] - **Shared by [optional]:** [Edward Nyameri ] - **Language(s) (NLP):** [LLM] - **License:** [MIT] ### Dataset Sources [optional] <!-- schooly-Computer Breaches --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Threat Hunting for AI cyber Security Tool Kit --> ### Direct Use <!-- application platform analysis for Threat Hunters--> [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 <!-- Advancement of the Threat Hunt using Computational Intelligence to curb & contain comprising of information --> [More Information Needed] ### Source Data <!-- 🏫 Computer Breaches--> #### 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]
varun-d/asdfasdfa
--- license: openrail ---
heliosprime/twitter_dataset_1713191473
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 26387 num_examples: 72 download_size: 22992 dataset_size: 26387 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713191473" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Violence/Cloud
--- license: afl-3.0 ---
apsys/vc-pitches
--- license: apache-2.0 ---
mdacampora/tax-convos-sample2
--- dataset_info: features: - name: id dtype: int64 - name: turns list: - name: role dtype: string - name: text dtype: string splits: - name: train num_bytes: 3823 num_examples: 5 download_size: 4907 dataset_size: 3823 --- # Dataset Card for "tax-convos-sample2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
genia-vdg/genia-dataset-01
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 833117.0 num_examples: 24 download_size: 323148 dataset_size: 833117.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
zolak/twitter_dataset_80_1713048912
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2782422 num_examples: 6746 download_size: 1395589 dataset_size: 2782422 configs: - config_name: default data_files: - split: train path: data/train-* ---
CarperAI/openai_summarize_comparisons
--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: test num_bytes: 143018505 num_examples: 83629 - name: train num_bytes: 157425966 num_examples: 92534 - name: valid1 num_bytes: 56686271 num_examples: 33082 - name: valid2 num_bytes: 86396487 num_examples: 50715 download_size: 20257716 dataset_size: 443527229 ---
TinyPixel/f_2
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 1645034658 num_examples: 1000000 download_size: 950054945 dataset_size: 1645034658 --- # Dataset Card for "f_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Livingwithmachines/MapReader_Data_SIGSPATIAL_2022
--- annotations_creators: - expert-generated language: - en language_creators: [] license: - cc-by-nc-sa-4.0 multilinguality: [] pretty_name: MapReader Data SIGSPATIAL 2022 size_categories: - 10K<n<100K source_datasets: [] tags: - maps - historical - National Library of Scotland - heritage - humanities - lam task_categories: - image-classification task_ids: - multi-class-image-classification --- # Gold standards and outputs ## Dataset Description - MapReader’s GitHub: https://github.com/Living-with-machines/MapReader - MapReader paper: https://dl.acm.org/doi/10.1145/3557919.3565812 - Zenodo link for gold standards and outputs: https://doi.org/10.5281/zenodo.7147906 - Contacts: Katherine McDonough, The Alan Turing Institute, kmcdonough at turing.ac.uk; Kasra Hosseini, The Alan Turing Institute, k.hosseinizad at gmail.com ### Dataset Summary Here we share gold standard annotations and outputs from early experiments using MapReader. MapReader creates datasets for humanities research using historical map scans and metadata as inputs. Using maps provided by the National Library of Scotland, these annotations and outputs reflect labeling tasks relevant to historical research on the [Living with Machines](https://livingwithmachines.ac.uk/) project. Data shared here is derived from maps printed in nineteenth-century Britain by the Ordnance Survey, Britain's state mapping agency. These maps cover England, Wales, and Scotland from 1888 to 1913. ## Directory structure The gold standards and outputs are stored on [Zenodo](https://doi.org/10.5281/zenodo.7147906). It contains the following directories/files: ``` MapReader_Data_SIGSPATIAL_2022 ├── README ├── annotations │   ├── maps │   │   ├── map_100942121.png │   │   ├── ... │   │   └── map_99383316.png │   ├── slice_meters_100_100 │   │   ├── test │   │   │   ├── patch-...PNG │   │   │   ├── ... │   │   │   └── patch-...PNG │   │   ├── train │   │   │   ├── patch-...PNG │   │   │   ├── ... │   │   │   └── patch-...PNG │   │   └── val │   │      ├── patch-...PNG │   │      ├── ... │   │      └── patch-...PNG │   ├── test.csv │   ├── train.csv │   └── valid.csv └── outputs ├── label_01_03 │   ├── pred_01_03_all.csv │   ├── pred_01_03_keep_01_0250.csv │   ├── pred_01_03_keep_05_0500.csv │   └── pred_01_03_keep_10_1000.csv ├── label_02 │   ├── pred_02_all.csv │   ├── pred_02_keep_01_0250.csv │   ├── pred_02_keep_05_0500.csv │   └── pred_02_keep_10_1000.csv ├── patches_all.csv ├── percentage │   └── pred_02_keep_1_250_01_03_keep_1_250_percentage.csv └── resources ├── StopsGB4paper.csv └── six_inch4paper.json ``` ## annotations The `annotations` directory is as follows: ``` ├── annotations │ ├── maps │ │ ├── map_100942121.png │ │ ├── ... │ │ └── map_99383316.png │ ├── slice_meters_100_100 │ │ ├── test │ │ │ ├── patch-...PNG │ │ │ ├── ... │ │ │ └── patch-...PNG │ │ ├── train │ │ │ ├── patch-...PNG │ │ │ ├── ... │ │ │ └── patch-...PNG │ │ └── val │ │ ├── patch-...PNG │ │ ├── ... │ │ └── patch-...PNG │ ├── test.csv │ ├── train.csv │ └── valid.csv ``` ### annotations/train.csv, valid.csv and test.csv In the `MapReader_Data_SIGSPATIAL_2022/annotations` directory, there are three CSV files, namely `train.csv`, `valid.csv` and `test.csv`. These files have two columns: ``` image_id,label slice_meters_100_100/train/patch-1390-3892-1529-4031-#map_101590193.png#.PNG,0 slice_meters_100_100/train/patch-1716-3960-1848-4092-#map_101439245.png#.PNG,0 ... ``` in which: - `image_id`: path to each labelled patch. For example in `slice_meters_100_100/train/patch-1390-3892-1529-4031-#map_101590193.png#.PNG`: - `slice_meters_100_100/train`: directory where the patch is stored. (in this example, it is a patch used for training) - `patch-1390-3892-1529-4031-#map_101590193.png#.PNG` has two parts itself: `patch-1390-3892-1529-4031` is the patch ID, and the patch itself is extracted from `map_101590193.png` map sheet. - `label`: label assigned to each patch by an annotator. - Labels: 0: no [building or railspace]; 1: railspace; 2: building; and 3: railspace and [non railspace] building. ### annotations/slice_meters_100_100 Patches used for training, validation, and test in PNG format. ``` ├── annotations │ ├── slice_meters_100_100 │ │ ├── test │ │ │ ├── patch-...PNG │ │ │ ├── ... │ │ │ └── patch-...PNG │ │ ├── train │ │ │ ├── patch-...PNG │ │ │ ├── ... │ │ │ └── patch-...PNG │ │ └── val │ │ ├── patch-...PNG │ │ ├── ... │ │ └── patch-...PNG ``` ### annotations/maps Map sheets retrieved from the National Library of Scotland via webservers. These maps were later sliced into patches which can be found in `annotations/slice_meters_100_100`. ``` ├── annotations │ ├── maps │ │ ├── map_100942121.png │ │ ├── ... │ │ └── map_99383316.png ``` ## outputs The `outputs` directory is as follows: ``` └── outputs ├── label_01_03 │   ├── pred_01_03_all.csv │   ├── pred_01_03_keep_01_0250.csv │   ├── pred_01_03_keep_05_0500.csv │   └── pred_01_03_keep_10_1000.csv ├── label_02 │   ├── pred_02_all.csv │   ├── pred_02_keep_01_0250.csv │   ├── pred_02_keep_05_0500.csv │   └── pred_02_keep_10_1000.csv ├── patches_all.csv ├── percentage │   └── pred_02_keep_1_250_01_03_keep_1_250_percentage.csv └── resources ├── StopsGB4paper.csv └── six_inch4paper.json ``` ### outputs/label_01_03 Starting with: ``` └── outputs ├── label_01_03 │   ├── pred_01_03_all.csv │   ├── pred_01_03_keep_01_0250.csv │   ├── pred_01_03_keep_05_0500.csv │   └── pred_01_03_keep_10_1000.csv ``` The file `pred_01_03_all.csv` contains the following columns: ``` ,center_lon,center_lat,pred,conf,mean_pixel_RGB,std_pixel_RGB,mean_pixel_A,image_id,parent_id,pub_date,url,x,y,z,opening_year_quicks,closing_year_quicks,dist2quicks 0,-0.4011055106547341,52.61260776720805,1,0.9898980855941772,0.8450341820716858,0.1668068021535873,1.0,patch-3014-0-3151-137-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3880925.8529841416,-27169.29919979412,5044483.051365171,1867,1929,1121.9150481268305 1,-0.399645312864389,52.61260776720805,1,0.9999995231628418,0.823089599609375,0.1925655305385589,1.0,patch-3151-0-3288-137-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3880926.544140446,-27070.392789791513,5044483.051365171,1867,1929,1113.0714735200893 ... ``` - **center_lon**: longitude of the patch center - **center_lat**: latitude of the patch center - **pred**: predicted label for the patch - **conf**: model confidence - **mean_pixel_RGB**: mean pixel intensities, using all three channels - **std_pixel_RGB**: standard deviations of pixel intensities, using all three channels - **mean_pixel_A**: mean pixel intensities of alpha channel - **image_id**: patch ID - **parent_id**: ID of the map sheet that the patch belongs to - **pub_date**: publication date of the map sheet that the patch belongs to - **url**: URL of the map sheet that the patch belongs to - **x, y, z**: to compute distances (using k-d tree) - **opening_year_quicks**: Date when the railway station first opened - **closing_year_quicks**: Date when the railway station last closed, - **dist2quicks**: distance to the closest StopsGB in meters. NB: See `outputs/resources` below for description of the StopsGB (railway station) data and links to related publications. --- The other files in `outputs/label_01_03` have the same columns as `pred_01_03_all.csv` (described above). The difference is: - `pred_01_03_all.csv`: all patches predicted as labels 1 (railspace) or 3 (railspace and [non railspace] building). - `pred_01_03_keep_01_0250.csv`: similar to `pred_01_03_all.csv` except that we removed those patches that had no other neighboring patches with the same label within a radius of 250 meters. Note 01 and 0250 in the name. 01 means one neighboring patch and 0250 means 250 meters. - `pred_01_03_keep_05_0500.csv`: similar to `pred_01_03_all.csv` except that we removed those patches that had less than five neighboring patches with the same label within a radius of 500 meters. - `pred_01_03_keep_10_1000.csv`: similar to `pred_01_03_all.csv` except that we removed those patches that had less than ten neighboring patches with the same label within a radius of 1000 meters. ### outputs/label_02 Next, these files: ``` ├── label_02 │   ├── pred_02_all.csv │   ├── pred_02_keep_01_0250.csv │   ├── pred_02_keep_05_0500.csv │   └── pred_02_keep_10_1000.csv ``` Are the same as the files described above for `label_01_03` except for label 02 (i.e., building). ### outputs/patches_all.csv And last: ``` └── outputs ├── patches_all.csv ``` The file `patches_all.csv` has the following columns: ⚠️ this file contains the results for 30,490,411 patches used in the MapReader paper. ``` center_lat,center_lon,pred 52.61260776720805,-0.4332298620423274,0 52.61260776720805,-0.4317696642519822,0 ... ``` in which: - **center_lon**: longitude of the patch center - **center_lat**: latitude of the patch center - **pred**: predicted label for the patch ### outputs/percentage We have added one file in `outputs/percentage`: ``` └── outputs ├── percentage │   └── pred_02_keep_1_250_01_03_keep_1_250_percentage.csv ``` This file has the following columns: ``` ,center_lon,center_lat,pred,conf,mean_pixel_RGB,std_pixel_RGB,mean_pixel_A,image_id,parent_id,pub_date,url,x,y,z,dist2rail,dist2quicks,dist2quicks_km,dist2rail_km,dist2rail_minus_station,dist2quicks_km_quantized,dist2rail_km_quantized,dist2rail_minus_station_quantized,perc_neigh_rails,perc_neigh_builds,harmonic_mean_rail_build 0,-0.4040259062354244,52.61260776720805,2,0.9999010562896729,0.8095282316207886,0.1955385357141494,1.0,patch-2740-0-2877-137-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3880924.4631095687,-27367.11196679585,5044483.051365171,197.8176497186437,1164.8640633870857,1.1648640633870857,0.1978176497186437,0.9670464136684418,1.0,0.0,0.5,7.198443579766536,4.669260700389105,5.664349046373668 1,-0.4054861040257695,52.61171342293056,2,0.9999876022338868,0.8741853833198547,0.1160899400711059,1.0,patch-2603-137-2740-274-#map_100890251.png#.PNG,map_100890251.png,1902,https://maps.nls.uk/view/100890251,3881002.836728637,-27466.57793328472,5044422.621073416,296.73252022623865,1290.9640259717814,1.2909640259717814,0.2967325202262386,0.9942315057455428,1.0,0.0,0.5,7.050092764378478,4.452690166975881,5.45813633371237 ... ``` in which: - **center_lon**: longitude of the patch center - **center_lat**: latitude of the patch center - **pred**: predicted label for the patch - **conf**: model confidence - **mean_pixel_RGB**: mean pixel intensities, using all three channels - **std_pixel_RGB**: standard deviations of pixel intensities, using all three channels - **mean_pixel_A**: mean pixel intensities of alpha channel - **image_id**: patch ID - **parent_id**: ID of the map sheet that the patch belongs to - **pub_date**: publication date of the map sheet that the patch belongs to - **url**: URL of the map sheet that the patch belongs to - **x, y, z**: to compute distances (using k-d tree) - **dist2rail**: distance to the closest railspace patch (i.e., the patch that is classified as 1: railspace or 3: railspace and [non railspace] building) - **dist2quicks**: distance to the closest StopsGB station in meters. - **dist2quicks_km**: distance to the closest StopsGB station in km. - **dist2rail_km**: similar to **dist2rail** except in km. - **dist2rail_minus_station**: | dist2rail_km - dist2quicks_km | - **dist2quicks_km_quantized**: discrete version of **dist2quicks_km**, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf). - **dist2rail_km_quantized**: discrete version of **dist2rail_km**, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf). - **dist2rail_minus_station_quantized**: discrete version of **dist2rail_minus_station**, we used these intervals: [0. , 0.5), [0.5, 1.), [1., 1.5), ... , [4.5, 5.) and [5., inf). - **perc_neigh_rails**: what is the percentage of neighboring patches predicted as rail (labels 01 and 03). - **perc_neigh_builds**: what is the percentage of neighboring patches predicted as building (label 02). - **harmonic_mean_rail_build**: Harmonic mean of *perc_neigh_rails* and **perc_neigh_builds**. These additional `percentage` attributes shed light on the relationship between 'railspace' and stations, something we explore in further Living with Machines research. ### outputs/resources Finally, we have the following files: ``` └── outputs └── resources ├── StopsGB4paper.csv └── six_inch4paper.json ``` - `StopsGB4paper.csv`: this is a trimmed down version of StopsGB, a dataset documenting passenger railway stations in Great Britain (see [this link](https://bl.iro.bl.uk/concern/datasets/0abea1b1-2a43-4422-ba84-39b354c8bb09?locale=en) for the complete dataset). We filtered the stations as follows: - Keep only stations for which "ghost_entry" and "cross_ref" columns are "False". (These two fields help remove records in the StopsGB dataset that are not actually stations, but relics of the original publication formatting.) - "Opening" was NOT "unknown". - The map sheet was surveyed during a year when the station was operational (i.e., "opening_year_quicks" <= survey_date_of_map_sheet <= "closing_year_quicks"). You can learn more about the StopsGB dataset and how it was created from this paper: ``` Mariona Coll Ardanuy, Kaspar Beelen, Jon Lawrence, Katherine McDonough, Federico Nanni, Joshua Rhodes, Giorgia Tolfo, and Daniel C.S. Wilson. "Station to Station: Linking and Enriching Historical British Railway Data." In Computational Humanities Research (CHR2021). 2021. ``` ```bibtex @inproceedings{lwm-station-to-station-2021, title = "Station to Station: Linking and Enriching Historical British Railway Data", author = "Coll Ardanuy, Mariona and Beelen, Kaspar and Lawrence, Jon and McDonough, Katherine and Nanni, Federico and Rhodes, Joshua and Tolfo, Giorgia and Wilson, Daniel CS", booktitle = "Computational Humanities Research", year = "2021", } ``` - `six_inch4paper.json`: similar to [metadata_OS_Six_Inch_GB_WFS_light.json](https://github.com/Living-with-machines/MapReader/blob/main/mapreader/persistent_data/metadata_OS_Six_Inch_GB_WFS_light.json) on MapReader's GitHub with some minor changes. ## Dataset Creation ### Curation Rationale These annotations of map patches are part of a research project to develop humanistic methods for structuring visual information on digitized historical maps. Dividing thousands of nineteenth-century map sheets into 100m x 100m patches and labeling those patches with historically-meaningful concepts diverges from traditional methods for creating data from maps, both in terms of scale (the number of maps being examined), and of type (raster-style patches vs. pixel-level vector data). For more on the rationale for this approach, see the following paper: ``` Kasra Hosseini, Katherine McDonough, Daniel van Strien, Olivia Vane, Daniel C S Wilson, Maps of a Nation? The Digitized Ordnance Survey for New Historical Research, *Journal of Victorian Culture*, Volume 26, Issue 2, April 2021, Pages 284–299. ``` ```bibtex @article{hosseini_maps_2021, title = {Maps of a Nation? The Digitized Ordnance Survey for New Historical Research}, volume = {26}, rights = {All rights reserved}, issn = {1355-5502}, url = {https://doi.org/10.1093/jvcult/vcab009}, doi = {10.1093/jvcult/vcab009}, shorttitle = {Maps of a Nation?}, pages = {284--299}, number = {2}, journaltitle = {Journal of Victorian Culture}, author = {Hosseini, Kasra and {McDonough}, Katherine and van Strien, Daniel and Vane, Olivia and Wilson, Daniel C S}, urldate = {2021-05-19}, date = {2021-04-01}, } ``` ### Source Data #### Initial Data Access Data was accessed via the National Library of Scotland's Historical Maps API: https://maps.nls.uk/projects/subscription-api/ The data shared here is derived from the six-inch to one mile sheets printed between 1888-1913: https://maps.nls.uk/projects/subscription-api/#gb6inch ### Annotations and Outputs The annotations and output datasets collected here are related to experiments to identify the 'footprint' of rail infrastructure in the UK, a concept we call 'railspace'. We also created a dataset to identify buildings on the maps. #### Annotation process The custom annotation interface built into MapReader is designed specifically to assist researchers in labeling patches relevant to concepts of interest to their research questions. Our **guidelines** for the data shared here were: - for any non-null label (railspace, building, or railspace + building), if a patch contains any visual signal for that label (e.g. 'railspace'), it should be assigned the relevant label. For example, if it is possible for an annotator to see a railway track passing through the corner of a patch, that patch is labeled as 'railspace'. - the context around the patch should not be used as an aid in extreme cases where it is nearly impossible to determine whether a patch contains a non-null label - however, the patch context shown in the annotation interface can be used to quickly distinguish between different content types, particularly where the contiguity of a type across patches is useful in determining what label to assign - for 'railspace': use this label for any type of rail infrastructure as determined by expert labelers. This includes, for example, single-track mining railroads; larger double-track passenger routes; sidings and embankments; etc. It excludes urban trams. - for 'building': use this label for any size building - for 'building + railspace': use this label for patches combining these two types of content Because 'none' (e.g. null) patches made up the vast majority of patches in the total dataset from these map sheets, we ordered patches to annotate based on their pixel intensity. This allowed us to focus first on patches containing more visual content printed on the map sheet, and later to move more quickly through the patches that captured parts of the map with little to no printed features. #### Who are the annotators? Data shared here was annotated by Kasra Hosseini and Katherine McDonough. Members of the Living with Machines research team contributed early annotations during the development of MapReader: Ruth Ahnert, Kaspar Beelen, Mariona Coll-Ardanuy, Emma Griffin, Tim Hobson, Jon Lawrence, Giorgia Tolfo, Daniel van Strien, Olivia Vane, and Daniel C.S. Wilson. ## Credits and re-use terms ### MapReader outputs The files shared here (other than ```resources```) under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC-BY-NC-SA) licence. If you are interested in working with OS maps used to create these results, please also note the re-use terms of the original map images and metadata detailed below. ### Digitized maps MapReader can retrieve maps from NLS (National Library of Scotland) via webservers. For all the digitized maps (retrieved or locally stored), please note the re-use terms: Use of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC-BY-NC-SA) licence. Please refer to https://maps.nls.uk/copyright.html#exceptions-os for details on copyright and re-use license. ### Map metadata We have provided some metadata files in on MapReader’s GitHub page (https://github.com/Living-with-machines/MapReader/tree/main/mapreader/persistent_data). For all these file, please note the re-use terms: Use of the digitised maps for commercial purposes is currently restricted by contract. Use of these digitised maps for non-commercial purposes is permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/) (CC-BY-NC-SA) licence. Please refer to https://maps.nls.uk/copyright.html#exceptions-os for details on copyright and re-use license. ## Acknowledgements This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1). Living with Machines, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund, is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library and the Universities of Cambridge, East Anglia, Exeter, and Queen Mary University of London.
mask-distilled-one-sec-cv12/chunk_158
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1016276636 num_examples: 199583 download_size: 1035715202 dataset_size: 1016276636 --- # Dataset Card for "chunk_158" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HydraIndicLM/punjabi_alpaca_52K
--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 46649317 num_examples: 52002 download_size: 18652304 dataset_size: 46649317 configs: - config_name: default data_files: - split: train path: data/train-* --- ## About This repo contains a 52K instruction set for Punjabi, translated from Alpaca. ## Citation If you find this repository useful, please consider giving 👏 and citing: ``` @misc{PunjabiAlpaca, author = {Sambit Sekhar and Shantipriya Parida}, title = {Punjabi Instruction Set Based on Alpaca}, year = {2023}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/OdiaGenAI}}, } ``` ## License This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
CyberHarem/higana_pokemon
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of higana (Pokémon) This is the dataset of higana (Pokémon), containing 223 images and their tags. The core tags of this character are `black_hair, breasts, short_hair, red_eyes, dark_skin, large_breasts, short_ponytail, dark-skinned_female`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 223 | 198.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/higana_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 223 | 121.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/higana_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 540 | 254.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/higana_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 223 | 181.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/higana_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 540 | 337.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/higana_pokemon/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/higana_pokemon', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, nipples, blush, nude, smile, looking_at_viewer, navel, pussy | | 1 | 21 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1boy, 1girl, hetero, sex, solo_focus, vaginal, blush, sweat, nude, nipples, penis, girl_on_top, open_mouth, bar_censor, pussy, cowgirl_position | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1boy, 1girl, barefoot, blush, feet, hetero, penis, solo_focus, toes, mosaic_censoring, navel, smile, two-footed_footjob, nipples, sweat, bikini, cleavage, ejaculation, naked_cape, naked_cloak, nude, open_mouth, pov | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, looking_at_viewer, short_shorts, smile, blush, grey_thighhighs, solo, bangs, cloak, over-kneehighs, bare_shoulders, black_shirt, cleavage, grey_shorts, open_mouth, pokemon_(creature), simple_background, sleeveless_shirt, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | nipples | blush | nude | smile | looking_at_viewer | navel | pussy | 1boy | hetero | sex | solo_focus | vaginal | sweat | penis | girl_on_top | open_mouth | bar_censor | cowgirl_position | barefoot | feet | toes | mosaic_censoring | two-footed_footjob | bikini | cleavage | ejaculation | naked_cape | naked_cloak | pov | short_shorts | grey_thighhighs | bangs | cloak | over-kneehighs | bare_shoulders | black_shirt | grey_shorts | pokemon_(creature) | simple_background | sleeveless_shirt | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:--------|:-------|:--------|:--------------------|:--------|:--------|:-------|:---------|:------|:-------------|:----------|:--------|:--------|:--------------|:-------------|:-------------|:-------------------|:-----------|:-------|:-------|:-------------------|:---------------------|:---------|:-----------|:--------------|:-------------|:--------------|:------|:---------------|:------------------|:--------|:--------|:-----------------|:-----------------|:--------------|:--------------|:---------------------|:--------------------|:-------------------|:-------------------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 21 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | X | X | | X | | X | X | | X | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | | X | X | | | | | | | | | | | X | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
Imran1/dogbalance_data
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Australian_shepherd '1': Chihuahua '2': French_bulldog splits: - name: train num_bytes: 18102241.0 num_examples: 735 download_size: 18093424 dataset_size: 18102241.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/VALUE_cola_negative_inversion
--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 368 num_examples: 3 - name: test num_bytes: 166 num_examples: 1 - name: train num_bytes: 596 num_examples: 8 download_size: 7183 dataset_size: 1130 --- # Dataset Card for "VALUE_cola_negative_inversion" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jason-lee08/TinyStoriesWithExclamations
--- dataset_info: features: - name: text struct: - name: attention_mask sequence: int64 - name: input_ids sequence: int64 splits: - name: train num_bytes: 7567697960 num_examples: 2119719 - name: validation num_bytes: 76086656 num_examples: 21990 download_size: 816196858 dataset_size: 7643784616 --- # Dataset Card for "TinyStoriesWithExclamations" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lokesh2002/txt2txt
--- license: apache-2.0 ---
open-llm-leaderboard/details_Lajonbot__Llama-2-13b-hf-instruct-pl-lora_unload
--- pretty_name: Evaluation run of Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload](https://huggingface.co/Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Lajonbot__Llama-2-13b-hf-instruct-pl-lora_unload\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-14T22:54:23.964972](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__Llama-2-13b-hf-instruct-pl-lora_unload/blob/main/results_2023-10-14T22-54-23.964972.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0020973154362416107,\n\ \ \"em_stderr\": 0.00046850650303681895,\n \"f1\": 0.05818162751677859,\n\ \ \"f1_stderr\": 0.0013245165484434952,\n \"acc\": 0.4407302535404773,\n\ \ \"acc_stderr\": 0.01044050090848239\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.00046850650303681895,\n\ \ \"f1\": 0.05818162751677859,\n \"f1_stderr\": 0.0013245165484434952\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11902956785443518,\n \ \ \"acc_stderr\": 0.008919702911161629\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803152\n\ \ }\n}\n```" repo_url: https://huggingface.co/Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|arc:challenge|25_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-16T12:50:25.764084.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_14T22_54_23.964972 path: - '**/details_harness|drop|3_2023-10-14T22-54-23.964972.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-14T22-54-23.964972.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_14T22_54_23.964972 path: - '**/details_harness|gsm8k|5_2023-10-14T22-54-23.964972.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-14T22-54-23.964972.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hellaswag|10_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-16T12:50:25.764084.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-management|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-16T12:50:25.764084.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_16T12_50_25.764084 path: - '**/details_harness|truthfulqa:mc|0_2023-08-16T12:50:25.764084.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-16T12:50:25.764084.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_14T22_54_23.964972 path: - '**/details_harness|winogrande|5_2023-10-14T22-54-23.964972.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-14T22-54-23.964972.parquet' - config_name: results data_files: - split: 2023_08_16T12_50_25.764084 path: - results_2023-08-16T12:50:25.764084.parquet - split: 2023_10_14T22_54_23.964972 path: - results_2023-10-14T22-54-23.964972.parquet - split: latest path: - results_2023-10-14T22-54-23.964972.parquet --- # Dataset Card for Evaluation run of Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload](https://huggingface.co/Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Lajonbot__Llama-2-13b-hf-instruct-pl-lora_unload", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-14T22:54:23.964972](https://huggingface.co/datasets/open-llm-leaderboard/details_Lajonbot__Llama-2-13b-hf-instruct-pl-lora_unload/blob/main/results_2023-10-14T22-54-23.964972.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0020973154362416107, "em_stderr": 0.00046850650303681895, "f1": 0.05818162751677859, "f1_stderr": 0.0013245165484434952, "acc": 0.4407302535404773, "acc_stderr": 0.01044050090848239 }, "harness|drop|3": { "em": 0.0020973154362416107, "em_stderr": 0.00046850650303681895, "f1": 0.05818162751677859, "f1_stderr": 0.0013245165484434952 }, "harness|gsm8k|5": { "acc": 0.11902956785443518, "acc_stderr": 0.008919702911161629 }, "harness|winogrande|5": { "acc": 0.7624309392265194, "acc_stderr": 0.011961298905803152 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
axiong/pmc_oa_demo
--- license: openrail ---
open-llm-leaderboard/details_jpquiroga__Mistral_7B_ties_merge_instruct_open_orca_codeninja
--- pretty_name: Evaluation run of jpquiroga/Mistral_7B_ties_merge_instruct_open_orca_codeninja dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jpquiroga/Mistral_7B_ties_merge_instruct_open_orca_codeninja](https://huggingface.co/jpquiroga/Mistral_7B_ties_merge_instruct_open_orca_codeninja)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jpquiroga__Mistral_7B_ties_merge_instruct_open_orca_codeninja\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-15T16:23:04.591753](https://huggingface.co/datasets/open-llm-leaderboard/details_jpquiroga__Mistral_7B_ties_merge_instruct_open_orca_codeninja/blob/main/results_2024-04-15T16-23-04.591753.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.593791278114062,\n\ \ \"acc_stderr\": 0.033151969992860686,\n \"acc_norm\": 0.5976125048945863,\n\ \ \"acc_norm_stderr\": 0.033824061944462476,\n \"mc1\": 0.4222766217870257,\n\ \ \"mc1_stderr\": 0.017290733254248174,\n \"mc2\": 0.5878025444934631,\n\ \ \"mc2_stderr\": 0.015592863615568177\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5674061433447098,\n \"acc_stderr\": 0.014478005694182524,\n\ \ \"acc_norm\": 0.5938566552901023,\n \"acc_norm_stderr\": 0.014351656690097862\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6026687910774746,\n\ \ \"acc_stderr\": 0.004883455188908961,\n \"acc_norm\": 0.7994423421629158,\n\ \ \"acc_norm_stderr\": 0.003995992960088757\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4888888888888889,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.4888888888888889,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849726,\n\ \ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849726\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6458333333333334,\n\ \ \"acc_stderr\": 0.039994111357535424,\n \"acc_norm\": 0.6458333333333334,\n\ \ \"acc_norm_stderr\": 0.039994111357535424\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6011560693641619,\n\ \ \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.6011560693641619,\n\ \ \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5063829787234042,\n \"acc_stderr\": 0.03268335899936336,\n\ \ \"acc_norm\": 0.5063829787234042,\n \"acc_norm_stderr\": 0.03268335899936336\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3968253968253968,\n \"acc_stderr\": 0.02519710107424649,\n \"\ acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.02519710107424649\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\ \ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\ \ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7032258064516129,\n \"acc_stderr\": 0.025988500792411898,\n \"\ acc_norm\": 0.7032258064516129,\n \"acc_norm_stderr\": 0.025988500792411898\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4088669950738916,\n \"acc_stderr\": 0.034590588158832314,\n \"\ acc_norm\": 0.4088669950738916,\n \"acc_norm_stderr\": 0.034590588158832314\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\ : 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.03524390844511781,\n\ \ \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.03524390844511781\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\ acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.02840895362624527,\n\ \ \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.02840895362624527\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5769230769230769,\n \"acc_stderr\": 0.025049197876042345,\n\ \ \"acc_norm\": 0.5769230769230769,\n \"acc_norm_stderr\": 0.025049197876042345\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945273,\n \ \ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945273\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.592436974789916,\n \"acc_stderr\": 0.03191863374478465,\n \ \ \"acc_norm\": 0.592436974789916,\n \"acc_norm_stderr\": 0.03191863374478465\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\ acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7779816513761468,\n \"acc_stderr\": 0.01781884956479664,\n \"\ acc_norm\": 0.7779816513761468,\n \"acc_norm_stderr\": 0.01781884956479664\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7598039215686274,\n \"acc_stderr\": 0.02998373305591361,\n \"\ acc_norm\": 0.7598039215686274,\n \"acc_norm_stderr\": 0.02998373305591361\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7932489451476793,\n \"acc_stderr\": 0.02636165166838909,\n \ \ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.02636165166838909\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\ \ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\ \ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467765,\n\ \ \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467765\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.042365112580946315,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.042365112580946315\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6871165644171779,\n \"acc_stderr\": 0.036429145782924055,\n\ \ \"acc_norm\": 0.6871165644171779,\n \"acc_norm_stderr\": 0.036429145782924055\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281365,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281365\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.014866821664709588,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.014866821664709588\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.638728323699422,\n \"acc_stderr\": 0.025862201852277895,\n\ \ \"acc_norm\": 0.638728323699422,\n \"acc_norm_stderr\": 0.025862201852277895\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2994413407821229,\n\ \ \"acc_stderr\": 0.015318257745976711,\n \"acc_norm\": 0.2994413407821229,\n\ \ \"acc_norm_stderr\": 0.015318257745976711\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.026716118380156844,\n\ \ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.026716118380156844\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6463022508038585,\n\ \ \"acc_stderr\": 0.027155208103200865,\n \"acc_norm\": 0.6463022508038585,\n\ \ \"acc_norm_stderr\": 0.027155208103200865\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6141975308641975,\n \"acc_stderr\": 0.027085401226132143,\n\ \ \"acc_norm\": 0.6141975308641975,\n \"acc_norm_stderr\": 0.027085401226132143\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3971631205673759,\n \"acc_stderr\": 0.02918980567358708,\n \ \ \"acc_norm\": 0.3971631205673759,\n \"acc_norm_stderr\": 0.02918980567358708\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42503259452411996,\n\ \ \"acc_stderr\": 0.012625879884892001,\n \"acc_norm\": 0.42503259452411996,\n\ \ \"acc_norm_stderr\": 0.012625879884892001\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\ \ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5882352941176471,\n \"acc_stderr\": 0.019910377463105932,\n \ \ \"acc_norm\": 0.5882352941176471,\n \"acc_norm_stderr\": 0.019910377463105932\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484378,\n\ \ \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484378\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8009950248756219,\n\ \ \"acc_stderr\": 0.028231365092758406,\n \"acc_norm\": 0.8009950248756219,\n\ \ \"acc_norm_stderr\": 0.028231365092758406\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\ \ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\ \ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.031581495393387324,\n\ \ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.031581495393387324\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4222766217870257,\n\ \ \"mc1_stderr\": 0.017290733254248174,\n \"mc2\": 0.5878025444934631,\n\ \ \"mc2_stderr\": 0.015592863615568177\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224174\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.42608036391205456,\n \ \ \"acc_stderr\": 0.01362114439608671\n }\n}\n```" repo_url: https://huggingface.co/jpquiroga/Mistral_7B_ties_merge_instruct_open_orca_codeninja leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|arc:challenge|25_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-15T16-23-04.591753.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|gsm8k|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hellaswag|10_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-23-04.591753.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-management|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-23-04.591753.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|truthfulqa:mc|0_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-15T16-23-04.591753.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_15T16_23_04.591753 path: - '**/details_harness|winogrande|5_2024-04-15T16-23-04.591753.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-15T16-23-04.591753.parquet' - config_name: results data_files: - split: 2024_04_15T16_23_04.591753 path: - results_2024-04-15T16-23-04.591753.parquet - split: latest path: - results_2024-04-15T16-23-04.591753.parquet --- # Dataset Card for Evaluation run of jpquiroga/Mistral_7B_ties_merge_instruct_open_orca_codeninja <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jpquiroga/Mistral_7B_ties_merge_instruct_open_orca_codeninja](https://huggingface.co/jpquiroga/Mistral_7B_ties_merge_instruct_open_orca_codeninja) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_jpquiroga__Mistral_7B_ties_merge_instruct_open_orca_codeninja", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-15T16:23:04.591753](https://huggingface.co/datasets/open-llm-leaderboard/details_jpquiroga__Mistral_7B_ties_merge_instruct_open_orca_codeninja/blob/main/results_2024-04-15T16-23-04.591753.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.593791278114062, "acc_stderr": 0.033151969992860686, "acc_norm": 0.5976125048945863, "acc_norm_stderr": 0.033824061944462476, "mc1": 0.4222766217870257, "mc1_stderr": 0.017290733254248174, "mc2": 0.5878025444934631, "mc2_stderr": 0.015592863615568177 }, "harness|arc:challenge|25": { "acc": 0.5674061433447098, "acc_stderr": 0.014478005694182524, "acc_norm": 0.5938566552901023, "acc_norm_stderr": 0.014351656690097862 }, "harness|hellaswag|10": { "acc": 0.6026687910774746, "acc_stderr": 0.004883455188908961, "acc_norm": 0.7994423421629158, "acc_norm_stderr": 0.003995992960088757 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4888888888888889, "acc_stderr": 0.04318275491977976, "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.04318275491977976 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6381578947368421, "acc_stderr": 0.03910525752849726, "acc_norm": 0.6381578947368421, "acc_norm_stderr": 0.03910525752849726 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6792452830188679, "acc_stderr": 0.028727502957880267, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.028727502957880267 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6458333333333334, "acc_stderr": 0.039994111357535424, "acc_norm": 0.6458333333333334, "acc_norm_stderr": 0.039994111357535424 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6011560693641619, "acc_stderr": 0.037336266553835096, "acc_norm": 0.6011560693641619, "acc_norm_stderr": 0.037336266553835096 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5063829787234042, "acc_stderr": 0.03268335899936336, "acc_norm": 0.5063829787234042, "acc_norm_stderr": 0.03268335899936336 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.37719298245614036, "acc_stderr": 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0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.689795918367347, "acc_stderr": 0.029613459872484378, "acc_norm": 0.689795918367347, "acc_norm_stderr": 0.029613459872484378 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8009950248756219, "acc_stderr": 0.028231365092758406, "acc_norm": 0.8009950248756219, "acc_norm_stderr": 0.028231365092758406 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-virology|5": { "acc": 0.4759036144578313, "acc_stderr": 0.038879718495972646, "acc_norm": 0.4759036144578313, "acc_norm_stderr": 0.038879718495972646 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.783625730994152, "acc_stderr": 0.031581495393387324, "acc_norm": 0.783625730994152, "acc_norm_stderr": 0.031581495393387324 }, "harness|truthfulqa:mc|0": { "mc1": 0.4222766217870257, "mc1_stderr": 0.017290733254248174, "mc2": 0.5878025444934631, "mc2_stderr": 0.015592863615568177 }, "harness|winogrande|5": { "acc": 0.7592738752959748, "acc_stderr": 0.012015559212224174 }, "harness|gsm8k|5": { "acc": 0.42608036391205456, "acc_stderr": 0.01362114439608671 } } ``` ## 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.). 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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]
JbIPS/stanford-dogs
--- license: mit ---
yiyic/MTG_QG
--- task_categories: - text-generation - question-answering size_categories: - 10K<n<100K ---
huangyt/FINETUNE4_TEST
--- license: openrail ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/1706e1cd
--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1339 dataset_size: 188 --- # Dataset Card for "1706e1cd" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gsstein/25-percent-human-dataset
--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: prompt dtype: string - name: generated dtype: bool splits: - name: train num_bytes: 86221172 num_examples: 15326 - name: test num_bytes: 3062111 num_examples: 576 - name: validation num_bytes: 3258681 num_examples: 576 download_size: 57267649 dataset_size: 92541964 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
galman33/gal_yair_83000_100x100
--- dataset_info: features: - name: lat dtype: float64 - name: lon dtype: float64 - name: country_code dtype: string - name: image dtype: image splits: - name: train num_bytes: 1423239502.0 num_examples: 83000 download_size: 1423108777 dataset_size: 1423239502.0 --- # Dataset Card for "gal_yair_83000_100x100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/hayashimo_kantaicollection
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of hayashimo/早霜/早霜 (Kantai Collection) This is the dataset of hayashimo/早霜/早霜 (Kantai Collection), containing 500 images and their tags. The core tags of this character are `black_hair, long_hair, hair_over_one_eye, ribbon, very_long_hair, hair_ribbon, bow, brown_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 428.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashimo_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 281.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashimo_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1071 | 558.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashimo_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 390.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashimo_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1071 | 723.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayashimo_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/hayashimo_kantaicollection', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, blush, looking_at_viewer, simple_background, sitting, white_background, barefoot, medium_breasts, navel, nude, purple_eyes, smile, bikini, yellow_eyes | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_bra, black_panties, solo, looking_at_viewer, navel, simple_background, small_breasts, underwear_only, blush, white_background, cleavage | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blush, looking_at_viewer, open_shirt, solo, black_panties, large_breasts, open_mouth, purple_eyes, grey_pantyhose, navel, nipples, no_bra, smile, white_shirt, dakimakura_(medium), full_body, on_back, skirt | | 3 | 19 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bowtie, school_uniform, solo, white_shirt, long_sleeves, looking_at_viewer, purple_dress, halterneck, simple_background, white_background, white_ribbon, cowboy_shot | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, bowtie, grey_pantyhose, looking_at_viewer, school_uniform, solo, white_shirt, lace-up_boots, simple_background, long_sleeves, white_background, full_body, skirt, smile | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, bowtie, holding_umbrella, long_sleeves, looking_at_viewer, school_uniform, solo, white_shirt, rain, smile, sleeveless_dress, hydrangea, upper_body | | 6 | 14 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, enmaided, solo, black_dress, maid_headdress, looking_at_viewer, simple_background, maid_apron, smile, white_apron, long_sleeves, white_background, frilled_apron, puffy_sleeves, twitter_username, blush, one-hour_drawing_challenge, breasts, dated, pantyhose, purple_eyes | | 7 | 21 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | playboy_bunny, 1girl, bowtie, detached_collar, fake_animal_ears, rabbit_ears, wrist_cuffs, solo, strapless_leotard, purple_leotard, looking_at_viewer, rabbit_tail, grey_pantyhose, simple_background, fishnet_pantyhose, adapted_costume, small_breasts, blush, cleavage, white_background | | 8 | 7 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1boy, 1girl, blush, hetero, penis, solo_focus, fellatio, medium_breasts, open_mouth, closed_eyes, mosaic_censoring, nipples, shirt, bowtie, breasts_out, cum_on_tongue, facial, male_pubic_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | looking_at_viewer | simple_background | sitting | white_background | barefoot | medium_breasts | navel | nude | purple_eyes | smile | bikini | yellow_eyes | black_bra | black_panties | small_breasts | underwear_only | cleavage | open_shirt | large_breasts | open_mouth | grey_pantyhose | nipples | no_bra | white_shirt | dakimakura_(medium) | full_body | on_back | skirt | bowtie | school_uniform | long_sleeves | purple_dress | halterneck | white_ribbon | cowboy_shot | lace-up_boots | holding_umbrella | rain | sleeveless_dress | hydrangea | upper_body | enmaided | black_dress | maid_headdress | maid_apron | white_apron | frilled_apron | puffy_sleeves | twitter_username | one-hour_drawing_challenge | breasts | dated | pantyhose | playboy_bunny | detached_collar | fake_animal_ears | rabbit_ears | wrist_cuffs | strapless_leotard | purple_leotard | rabbit_tail | fishnet_pantyhose | adapted_costume | 1boy | hetero | penis | solo_focus | fellatio | closed_eyes | mosaic_censoring | shirt | breasts_out | cum_on_tongue | facial | male_pubic_hair | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:--------------------|:----------|:-------------------|:-----------|:-----------------|:--------|:-------|:--------------|:--------|:---------|:--------------|:------------|:----------------|:----------------|:-----------------|:-----------|:-------------|:----------------|:-------------|:-----------------|:----------|:---------|:--------------|:----------------------|:------------|:----------|:--------|:---------|:-----------------|:---------------|:---------------|:-------------|:---------------|:--------------|:----------------|:-------------------|:-------|:-------------------|:------------|:-------------|:-----------|:--------------|:-----------------|:-------------|:--------------|:----------------|:----------------|:-------------------|:-----------------------------|:----------|:--------|:------------|:----------------|:------------------|:-------------------|:--------------|:--------------|:--------------------|:-----------------|:--------------|:--------------------|:------------------|:-------|:---------|:--------|:-------------|:-----------|:--------------|:-------------------|:--------|:--------------|:----------------|:---------|:------------------| | 0 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | | | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | | | | | X | | X | X | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 19 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | X | X | | X | | | | | | X | | | | | | | | | | | X | | | X | | X | | X | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | X | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | X | X | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 14 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | X | X | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 7 | 21 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | X | X | | X | | | | | | | | | | | X | | X | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 8 | 7 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | X | | | | | | X | | | | | | | | | | | | | | X | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
edarchimbaud/perimeter-stocks
--- dataset_info: features: - name: symbol dtype: string - name: security dtype: string - name: gics_sector dtype: string - name: gics_sub_industry dtype: string splits: - name: train num_bytes: 111992 num_examples: 1500 download_size: 44216 dataset_size: 111992 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "perimeter-stocks" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yleo/emerton_dpo_pairs
--- dataset_info: features: - name: system dtype: string - name: question dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 15630325.68343667 num_examples: 5489 download_size: 9101980 dataset_size: 15630325.68343667 configs: - config_name: default data_files: - split: train path: data/train-* --- This dataset is similar to [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) with slightly less entries and replacement of GPT3.5 answer by GPT4 Turbo answers.
valdineiarcenio/oigente
--- license: openrail ---
xhiroga/MiniAlbum
--- license: mit ---
arieg/siamese_clusters
--- dataset_info: features: - name: image dtype: image - name: track_id dtype: class_label: names: '0': '000002' '1': '000005' '2': '000010' '3': '000140' '4': '000141' '5': 000148 '6': 000182 '7': 000190 '8': 000193 '9': 000194 '10': 000197 '11': '000200' '12': '000203' '13': '000204' '14': '000207' '15': '000210' '16': '000211' '17': '000212' '18': '000213' '19': '000255' '20': '000256' '21': 000368 '22': '000424' '23': 000459 '24': '000534' '25': '000540' '26': '000546' '27': '000574' '28': '000602' '29': '000615' '30': '000620' '31': '000621' '32': '000625' '33': '000666' '34': '000667' '35': '000676' '36': 000690 '37': 000694 '38': 000695 '39': '000704' '40': '000705' '41': '000706' '42': '000707' '43': 000708 '44': 000709 '45': '000714' '46': '000715' '47': '000716' '48': 000718 '49': '000777' '50': 000814 '51': 000821 '52': 000822 '53': 000825 '54': 000853 '55': 000890 '56': 000892 '57': 000897 '58': 000993 '59': 000995 '60': 000997 '61': 000998 '62': 001039 '63': '001040' '64': '001066' '65': 001069 '66': '001073' '67': '001075' '68': 001082 '69': 001083 '70': 001087 '71': '001102' '72': 001193 '73': 001195 '74': 001196 '75': 001197 '76': 001249 '77': 001259 '78': '001270' '79': '001276' '80': '001277' '81': 001278 '82': '001417' '83': '001427' '84': '001443' '85': 001482 '86': '001510' '87': '001544' '88': '001642' '89': '001644' '90': 001649 '91': '001661' '92': '001663' '93': '001666' '94': '001673' '95': 001680 '96': 001681 '97': 001682 '98': 001683 '99': 001684 '100': 001685 '101': 001686 '102': 001687 '103': 001688 '104': 001689 '105': '001701' '106': '001702' '107': '001703' '108': '001704' '109': '001706' '110': '001720' '111': '001732' '112': '001733' '113': '001735' '114': '001736' '115': 001883 '116': 001891 '117': 001893 '118': 001924 '119': 001925 '120': 001929 '121': 001930 '122': '002012' '123': 002096 '124': 002097 '125': 002099 '126': '003263' '127': '003264' '128': '003265' '129': '003266' '130': '003270' '131': '003271' '132': '003272' '133': '003273' '134': '003274' '135': 003492 '136': '003532' '137': '003533' '138': '003534' '139': '003535' '140': '003537' '141': 003538 '142': '003573' '143': 003598 '144': '003624' '145': '003707' '146': 003708 '147': '003720' '148': '003721' '149': '003722' '150': '003724' '151': '003725' '152': '003761' '153': '003762' '154': '003763' '155': '003765' '156': '003766' '157': '003775' '158': '003776' '159': '003777' '160': 003778 '161': 003779 '162': 003832 '163': 003833 '164': 003840 '165': 003880 '166': 003895 '167': 003896 '168': 003904 '169': 003905 '170': 003906 '171': 003908 '172': 003909 '173': 003910 '174': 003911 '175': 003912 '176': 003913 '177': 003920 '178': 003921 '179': 003950 '180': '004013' '181': '004017' '182': '004022' '183': '004037' '184': '004066' '185': '004067' '186': 004068 '187': 004069 '188': '004070' '189': '004071' '190': '004072' '191': '004073' '192': '004074' '193': '004075' '194': '004076' '195': '004077' '196': 004078 '197': 004079 '198': 004080 '199': 004091 '200': 004092 '201': 004093 '202': 004094 '203': 004095 '204': 004096 '205': 004097 '206': 004098 '207': 004099 '208': '004100' '209': '004101' '210': '004102' '211': '004103' '212': 004108 '213': '004232' '214': '004233' '215': '004234' '216': '004235' '217': '004236' '218': 004239 '219': '004450' '220': '004507' '221': 004508 '222': 004509 '223': '004510' '224': '004511' '225': 004519 '226': '004520' '227': '004521' '228': '004522' '229': 004682 '230': 004684 '231': 004685 '232': 004688 '233': '004777' '234': 004778 '235': 004779 '236': 004780 '237': 004781 '238': 004782 '239': 004784 '240': 004785 '241': 004786 '242': 004787 '243': 004788 '244': 004799 '245': 004835 '246': 004836 '247': 004838 '248': 004846 '249': 004848 '250': 004849 '251': '005006' '252': '005156' '253': '005157' '254': 005158 '255': 005159 '256': 005169 '257': '005170' '258': '005171' '259': 005191 '260': '005264' '261': 005268 '262': '005376' '263': 005381 '264': '005521' '265': 005879 '266': 005936 '267': 005940 '268': 006329 '269': '006330' '270': '006331' '271': '006332' '272': '006333' '273': '006342' '274': '006354' '275': '006357' '276': 006358 '277': '006360' '278': '006363' '279': '006366' '280': '006367' '281': 006368 '282': '006370' '283': '006372' '284': '006373' '285': '006376' '286': 006379 '287': 006380 '288': 006381 '289': 006382 '290': 006383 '291': 006385 '292': 006387 '293': 006389 '294': 006390 '295': 006393 '296': 006394 '297': 006396 '298': '006406' '299': '006407' '300': 006439 '301': '006440' '302': '006442' '303': '006443' '304': 006448 '305': 006459 '306': '006461' '307': '006463' '308': '006467' '309': 006469 '310': '006517' '311': 006519 '312': '006603' '313': '006605' '314': '006606' '315': '006607' '316': 006608 '317': 006609 '318': '006610' '319': '006611' '320': '006674' '321': '006675' '322': '006677' '323': 006679 '324': 006680 '325': 006684 '326': '006762' '327': '006776' '328': 006778 '329': 006779 '330': 006782 '331': 006783 '332': 006788 '333': 006802 '334': 006803 '335': 006854 '336': 006855 '337': 006856 '338': 006857 '339': '007011' '340': '007373' '341': '007374' '342': '007375' '343': '007376' '344': '007377' '345': 007378 '346': 007379 '347': 007381 '348': 007383 '349': 007385 '350': 007386 '351': 007388 '352': 007391 '353': 007393 '354': 007481 '355': 007482 '356': 007483 '357': 007487 '358': 007488 '359': 007489 '360': 007490 '361': 007491 '362': 007492 '363': 007495 '364': '007526' '365': '007527' '366': 007528 '367': 007529 '368': 007548 '369': '007554' '370': 007709 '371': '007710' '372': '007711' '373': '007712' '374': '007713' '375': 007872 '376': 008056 '377': 008208 '378': 008256 '379': 008259 '380': 008261 '381': 008345 '382': 008357 '383': 008363 '384': 008372 '385': 008416 '386': 009152 '387': 009155 '388': 009307 '389': 009476 '390': 009477 '391': 009491 '392': 009505 '393': 009511 '394': 009512 '395': 009513 '396': 009550 '397': 009553 '398': 009555 '399': 009557 '400': 009559 '401': 009560 '402': 009678 '403': 009721 '404': 009846 '405': 009887 '406': 009888 '407': 009918 '408': 009962 '409': 010186 '410': 010192 '411': '010250' '412': '010374' '413': '010375' '414': '010376' '415': '010377' '416': 010381 '417': 010382 '418': 010383 '419': 010384 '420': 010385 '421': 010386 '422': 010387 '423': 010388 '424': 010389 '425': '010435' '426': 010438 '427': 010439 '428': '010440' '429': '010441' '430': '010442' '431': '010443' '432': '010444' '433': '010447' '434': 010458 '435': 010480 '436': 010481 '437': 010485 '438': '010521' '439': '010527' '440': '010535' '441': '010541' '442': '010575' '443': '010577' '444': 010668 '445': 010669 '446': '010670' '447': '010671' '448': '010672' '449': '010673' '450': '010674' '451': '010675' '452': '010676' '453': '010677' '454': 010678 '455': 010679 '456': 010682 '457': 010684 '458': 010693 '459': 010694 '460': 010695 '461': 010696 '462': 010697 '463': 010698 '464': 010699 '465': 010805 '466': 010806 '467': 010807 '468': 010808 '469': 010809 '470': 010810 '471': 010983 '472': 010992 '473': 010993 '474': 011019 '475': '011020' '476': 011059 '477': 011198 '478': 011199 '479': '011200' '480': '011204' '481': '011206' '482': '011234' '483': '011237' '484': 011239 '485': '011242' '486': '011261' '487': '011262' '488': '011264' '489': 011268 '490': 011298 '491': 011299 '492': '011306' '493': '011333' '494': '011334' '495': '011503' '496': '011504' '497': '011505' '498': 011508 '499': '011544' '500': 011638 '501': '011671' '502': '011672' '503': '011673' '504': '011674' '505': '011675' '506': '011677' '507': 011679 '508': 011681 '509': 011682 '510': 011683 '511': '011763' '512': '011764' '513': '011765' '514': '011766' '515': '011767' '516': 011768 '517': 011769 '518': '011770' '519': '011771' '520': '011772' '521': '011773' '522': '011774' '523': '011775' '524': '011776' '525': '011777' '526': 011778 '527': 011779 '528': 011780 '529': 011781 '530': 011782 '531': 011783 '532': 011784 '533': 011785 '534': 011786 '535': 011787 '536': 011788 '537': 011789 '538': 011790 '539': 011791 '540': 011792 '541': 011793 '542': 011794 '543': 011795 '544': 011803 '545': 011818 '546': 011839 '547': 011861 '548': 011862 '549': 011867 '550': 011868 '551': 011916 '552': 011917 '553': 011918 '554': 011919 '555': 011920 '556': 011921 '557': 011922 '558': 011933 '559': 011937 '560': 011942 '561': 011946 '562': 011947 '563': 011951 '564': '012045' '565': '012046' '566': '012047' '567': 012048 '568': 012049 '569': '012050' '570': '012051' '571': '012052' '572': '012053' '573': 012058 '574': 012059 '575': '012060' '576': '012061' '577': '012062' '578': '012064' '579': '012065' '580': '012066' '581': '012067' '582': 012109 '583': '012146' '584': '012147' '585': '012173' '586': '012174' '587': 012179 '588': 012188 '589': 012189 '590': '012346' '591': 012348 '592': 012349 '593': '012350' '594': '012351' '595': '012352' '596': '012353' '597': '012355' '598': '012376' '599': 012387 '600': 012390 '601': 012394 '602': 012481 '603': 012482 '604': 012484 '605': 012485 '606': 012486 '607': 012487 '608': 012488 '609': 012489 '610': 012490 '611': 012508 '612': '012513' '613': '012514' '614': 012518 '615': '012521' '616': '012526' '617': '012527' '618': '012530' '619': '012531' '620': '012532' '621': '012537' '622': '012551' '623': '012552' '624': '012654' '625': 012690 '626': 012691 '627': 012692 '628': '012737' '629': 012985 '630': 012986 '631': 013191 '632': 013197 '633': 013199 '634': '013201' '635': 013218 '636': '013220' '637': '013325' '638': 013328 '639': '013362' '640': 013378 '641': '013474' '642': '013537' '643': 013538 '644': 013539 '645': '013540' '646': '013556' '647': '013561' '648': '013562' '649': '013566' '650': '013571' '651': 013578 '652': 013591 '653': 013596 '654': '013666' '655': 013668 '656': '013670' '657': '013706' '658': '013707' '659': 013708 '660': 013709 '661': '013710' '662': '013711' '663': '013735' '664': '013747' '665': 013748 '666': 013749 '667': '013767' '668': 013768 '669': 013804 '670': 013927 '671': 013928 '672': 013929 '673': 013930 '674': '014063' '675': 014208 '676': '014315' '677': '014316' '678': '014317' '679': 014318 '680': 014319 '681': '014320' '682': '014344' '683': 014358 '684': '014363' '685': '014365' '686': 014386 '687': 014391 '688': 014538 '689': 014539 '690': '014541' '691': '014542' '692': 014568 '693': 014569 '694': '014570' '695': '014571' '696': '014572' '697': '014576' '698': '014577' '699': 014578 '700': 014579 '701': 014580 '702': 014581 '703': 014583 '704': 014584 '705': 014585 '706': 014586 '707': 014588 '708': 014589 '709': 014590 '710': '014601' '711': '014602' '712': '014603' '713': '014604' '714': '014653' '715': '014661' '716': '014663' '717': 014684 '718': 014690 '719': 014693 '720': '014733' '721': '014734' '722': '014735' '723': '014736' '724': '014737' '725': 014738 '726': 014739 '727': '014740' '728': '014741' '729': '014742' '730': '014743' '731': '014744' '732': '014745' '733': 014809 '734': 014869 '735': 015094 '736': '015210' '737': '015464' '738': 015469 '739': '015471' '740': '015475' '741': '015476' '742': 015487 '743': 015488 '744': '015540' '745': '015541' '746': '015542' '747': '015543' '748': '015625' '749': 015769 '750': '015770' '751': '015771' '752': '015772' '753': '015773' '754': 015880 '755': 016095 '756': '016155' '757': 016158 '758': '016162' '759': '016163' '760': '016334' '761': '016337' '762': 016338 '763': 016339 '764': '016340' '765': '016354' '766': '016743' '767': '016744' '768': '016745' '769': '016747' '770': 016819 '771': 016820 '772': 016821 '773': 016822 '774': 016878 '775': 016879 '776': 016880 '777': 016895 '778': 016994 '779': 016995 '780': 016997 '781': '017132' '782': '017344' '783': '017345' '784': '017462' '785': 017491 '786': 017496 '787': 017499 '788': '017500' '789': '017573' '790': 017588 '791': '017605' '792': '017606' '793': '017607' '794': 017608 '795': 017609 '796': '017610' '797': '017611' '798': '017631' '799': '017632' '800': '017633' '801': '017634' '802': '017635' '803': '017636' '804': '017637' '805': '017644' '806': '017735' '807': 017782 '808': 017884 '809': 017906 '810': 018031 '811': 018032 '812': 018033 '813': 018034 '814': 018037 '815': 018038 '816': 018039 '817': 018043 '818': 018044 '819': 018112 '820': 018124 '821': 018144 '822': 018145 '823': 018146 '824': 018159 '825': 018197 '826': 018350 '827': 018607 '828': 018611 '829': 018876 '830': 018877 '831': 018887 '832': 019073 '833': 019074 '834': 019179 '835': 019184 '836': 019187 '837': 019192 '838': 019412 '839': 019413 '840': 019415 '841': 019416 '842': 019417 '843': 019418 '844': 019420 '845': 019422 '846': 019423 '847': 019425 '848': 019438 '849': 019439 '850': 019441 '851': 019442 '852': 019459 '853': 019673 '854': 019674 '855': 019685 '856': 019689 '857': 019707 '858': 019708 '859': 019729 '860': 019758 '861': 019759 '862': 019760 '863': 019889 '864': 019890 '865': 019891 '866': '020050' '867': 020296 '868': '020361' '869': '020362' '870': '020364' '871': '020365' '872': '020366' '873': 020369 '874': '020372' '875': '020373' '876': '020374' '877': '020375' '878': '020376' '879': '020424' '880': '020432' '881': 020469 '882': '020667' '883': '020704' '884': 020818 '885': 021058 '886': 021085 '887': 021087 '888': '021167' '889': 021228 '890': '021231' '891': '021232' '892': '021400' '893': '021401' '894': '021402' '895': '021403' '896': '021404' '897': 021409 '898': '021422' '899': '021565' '900': 021587 '901': '021657' '902': '021672' '903': '021676' '904': '021677' '905': '021707' '906': '021774' '907': 021842 '908': 021859 '909': 021860 '910': 021891 '911': 021895 '912': 021995 '913': 021996 '914': 021997 '915': 021998 '916': 021999 '917': '022000' '918': '022001' '919': 022088 '920': 022091 '921': 022093 '922': 022094 '923': 022095 '924': 022097 '925': '022150' '926': 022295 '927': 022296 '928': '022315' '929': 022348 '930': '022472' '931': '022473' '932': '022474' '933': '022475' '934': '022476' '935': '022477' '936': 022478 '937': 022479 '938': 022480 '939': 022481 '940': '023010' '941': '023013' '942': '023014' '943': '023015' '944': '023016' '945': '023037' '946': 023039 '947': '023041' '948': '023063' '949': '023155' '950': '023156' '951': '023172' '952': 023329 '953': '023353' '954': '023355' '955': '023371' '956': '023372' '957': '023505' '958': 023862 '959': '024216' '960': '024217' '961': 024218 '962': '024362' '963': '024363' '964': '024364' '965': '024365' '966': '024366' '967': '024367' '968': 024368 '969': 024369 '970': '024370' '971': '024371' '972': 024418 '973': '024420' '974': '024421' '975': '024422' '976': '024423' '977': '024424' '978': '024425' '979': '024426' '980': '024427' '981': 024428 '982': 024429 '983': '024430' '984': '024431' '985': '024432' '986': '024512' '987': '024515' '988': '024521' '989': '024524' '990': 024698 '991': 024699 '992': '024700' '993': '024701' '994': '024702' '995': '024717' '996': '024720' '997': 024739 '998': '024741' '999': '024742' '1000': '024745' '1001': '024746' '1002': '024747' '1003': 024748 '1004': 024749 '1005': 024842 '1006': 024898 '1007': 024899 '1008': 024901 '1009': 024912 '1010': 024915 '1011': 024917 '1012': 024963 '1013': 024975 '1014': 024983 '1015': 025028 '1016': 025029 '1017': '025030' '1018': '025031' '1019': '025032' '1020': '025033' '1021': '025055' '1022': '025063' '1023': '025066' '1024': '025104' '1025': '025124' '1026': '025215' '1027': '025216' '1028': '025227' '1029': '025232' '1030': '025233' '1031': '025234' '1032': '025235' '1033': '025324' '1034': 025378 '1035': '025601' '1036': '025603' '1037': '025605' '1038': '025606' '1039': 025608 '1040': 025609 '1041': 025668 '1042': 025669 '1043': '025670' '1044': 025795 '1045': 025796 '1046': 025797 '1047': 025802 '1048': 025804 '1049': '026007' '1050': 026008 '1051': '026010' '1052': '026011' '1053': '026012' '1054': '026013' '1055': '026014' '1056': '026016' '1057': '026017' '1058': '026020' '1059': '026021' '1060': '026022' '1061': '026025' '1062': '026026' '1063': '026034' '1064': '026035' '1065': '026036' '1066': 026169 '1067': '026174' '1068': 026298 '1069': '026301' '1070': '026302' '1071': '026307' '1072': '026322' '1073': '026464' '1074': '026465' '1075': '026466' '1076': 026583 '1077': '026600' '1078': '026605' '1079': 026629 '1080': 026638 '1081': 026639 '1082': '026640' '1083': '026641' '1084': '026642' '1085': '026643' '1086': '026651' '1087': '026652' '1088': '026653' '1089': '026654' '1090': '026655' '1091': '026656' '1092': '026657' '1093': 026658 '1094': 026659 '1095': '026674' '1096': 026681 '1097': '026754' '1098': '026765' '1099': 026859 '1100': 026861 '1101': 026902 '1102': 026904 '1103': 026905 '1104': 026906 '1105': '027164' '1106': '027177' '1107': 027194 '1108': 027195 '1109': 027197 '1110': 027198 '1111': 027258 '1112': '027406' '1113': '027454' '1114': '027455' '1115': '027456' '1116': '027547' '1117': 027548 '1118': 027549 '1119': '027550' '1120': '027551' '1121': '027552' '1122': 027609 '1123': '027610' '1124': '027611' '1125': '027612' '1126': '027613' '1127': '027667' '1128': '027673' '1129': 027797 '1130': 027798 '1131': 027799 '1132': 027802 '1133': 027803 '1134': 027804 '1135': 027805 '1136': 027855 '1137': 027856 '1138': 027866 '1139': 027945 '1140': 027953 '1141': 027975 '1142': 027978 '1143': 027981 '1144': 027987 '1145': 028070 '1146': 028072 '1147': 028179 '1148': 028241 '1149': 028260 '1150': 028266 '1151': 028274 '1152': 028375 '1153': 028376 '1154': 028477 '1155': 028478 '1156': 028479 '1157': 028480 '1158': 028481 '1159': 028482 '1160': 028483 '1161': 028484 '1162': 028485 '1163': 028546 '1164': 028548 '1165': 028553 '1166': 028571 '1167': 028608 '1168': 028692 '1169': 028802 '1170': 029037 '1171': 029039 '1172': 029040 '1173': 029041 '1174': 029042 '1175': 029043 '1176': 029044 '1177': 029045 '1178': 029128 '1179': 029180 '1180': 029243 '1181': 029245 '1182': 029255 '1183': 029271 '1184': 029272 '1185': 029350 '1186': 029351 '1187': 029355 '1188': 029465 '1189': 029480 '1190': 029526 '1191': 029528 '1192': 029530 '1193': 029587 '1194': 029602 '1195': 029673 '1196': 029718 '1197': 029719 '1198': 029720 '1199': 029721 '1200': 029738 '1201': 029739 '1202': 029740 '1203': 029741 '1204': 029742 '1205': 029744 '1206': 029745 '1207': 029746 '1208': 029747 '1209': 029750 '1210': 029752 '1211': 029807 '1212': 029813 '1213': 029816 '1214': 029961 '1215': 029971 '1216': '030041' '1217': '030043' '1218': '030050' '1219': '030056' '1220': 030058 '1221': 030059 '1222': 030090 '1223': 030095 '1224': '030120' '1225': 030196 '1226': 030198 '1227': '030230' '1228': '030316' '1229': 030486 '1230': 030487 '1231': 030488 '1232': 030519 '1233': '030520' '1234': '030521' '1235': '030522' '1236': '030636' '1237': 030682 '1238': 030690 '1239': '030702' '1240': '030740' '1241': 030895 '1242': '031040' '1243': '031041' '1244': '031042' '1245': '031043' '1246': '031044' '1247': '031165' '1248': '031356' '1249': 031389 '1250': 031390 '1251': 031391 '1252': 031392 '1253': 031568 '1254': 031807 '1255': 031887 '1256': 031888 '1257': 031889 '1258': 031999 '1259': '032001' '1260': '032021' '1261': '032075' '1262': 032081 '1263': 032218 '1264': '032325' '1265': '032326' '1266': '032327' '1267': 032328 '1268': 032329 '1269': '032330' '1270': '032331' '1271': '032332' '1272': '032333' '1273': '032334' '1274': '032335' '1275': '032336' '1276': '032337' '1277': 032338 '1278': 032339 '1279': '032340' '1280': '032433' '1281': '032435' '1282': '032437' '1283': 032438 '1284': 032439 '1285': '032525' '1286': 032686 '1287': 032687 '1288': 032689 '1289': 032693 '1290': 032694 '1291': 032695 '1292': '032755' '1293': '032756' '1294': 032759 '1295': '032760' '1296': 032800 '1297': 032882 '1298': '033020' '1299': 033049 '1300': '033050' '1301': '033064' '1302': '033067' '1303': 033068 '1304': 033069 '1305': '033070' '1306': '033071' '1307': '033072' '1308': '033123' '1309': '033124' '1310': '033203' '1311': '033216' '1312': '033221' '1313': 033278 '1314': '033415' '1315': '033422' '1316': '033424' '1317': '033426' '1318': '033446' '1319': 033459 '1320': '033460' '1321': '033461' '1322': '033465' '1323': '033477' '1324': 033486 '1325': 033538 '1326': 033992 '1327': '034003' '1328': '034147' '1329': '034167' '1330': '034257' '1331': 034258 '1332': '034263' '1333': 034484 '1334': '034510' '1335': '034511' '1336': 034994 '1337': 034996 '1338': '035007' '1339': 035008 '1340': 035182 '1341': 035184 '1342': 035198 '1343': 035199 '1344': '035204' '1345': 035296 '1346': 035299 '1347': '035443' '1348': '035444' '1349': '035462' '1350': '035527' '1351': '035534' '1352': '035535' '1353': '035537' '1354': 035539 '1355': '035541' '1356': '035543' '1357': '035544' '1358': '035545' '1359': 035549 '1360': '035550' '1361': 035569 '1362': '035571' '1363': 035608 '1364': '035734' '1365': 036096 '1366': 036097 '1367': 036099 '1368': '036143' '1369': '036144' '1370': '036145' '1371': '036146' '1372': '036147' '1373': '036245' '1374': '036257' '1375': 036258 '1376': '036261' '1377': '036272' '1378': '036273' '1379': '036275' '1380': '036277' '1381': '036302' '1382': '036304' '1383': '036322' '1384': '036333' '1385': '036371' '1386': 036380 '1387': 036388 '1388': 036428 '1389': '036435' '1390': 036481 '1391': '036526' '1392': '036560' '1393': '036567' '1394': '036614' '1395': '036615' '1396': '036616' '1397': 036618 '1398': '036643' '1399': 036659 '1400': 036799 '1401': 036959 '1402': 036961 '1403': 036965 '1404': 036966 '1405': 036983 '1406': 036984 '1407': 036985 '1408': 036986 '1409': 036987 '1410': 036988 '1411': 036990 '1412': 036992 '1413': 036994 '1414': 036997 '1415': 036998 '1416': 036999 '1417': '037041' '1418': '037111' '1419': '037113' '1420': 037119 '1421': '037121' '1422': '037131' '1423': '037136' '1424': '037141' '1425': '037147' '1426': '037324' '1427': '037325' '1428': 037368 '1429': 037369 '1430': '037416' '1431': '037417' '1432': '037423' '1433': 037538 '1434': 037592 '1435': '037725' '1436': '037727' '1437': '037730' '1438': '037731' '1439': 037779 '1440': 037781 '1441': 037784 '1442': 037859 '1443': 037911 '1444': 037920 '1445': 038312 '1446': 038321 '1447': 038323 '1448': 038326 '1449': 038351 '1450': 038352 '1451': 038353 '1452': 038354 '1453': 038361 '1454': 038362 '1455': 038363 '1456': 038365 '1457': 038399 '1458': 038435 '1459': 038450 '1460': 038522 '1461': 038557 '1462': 038560 '1463': 038775 '1464': 038776 '1465': 038777 '1466': 038778 '1467': 038779 '1468': 038780 '1469': 038781 '1470': 038782 '1471': 038783 '1472': 038784 '1473': 038785 '1474': 038817 '1475': 038818 '1476': 038819 '1477': 038820 '1478': 038821 '1479': 038822 '1480': 038823 '1481': 038824 '1482': 038825 '1483': 038826 '1484': 038827 '1485': 038828 '1486': 038829 '1487': 038830 '1488': 038833 '1489': 038834 '1490': 038847 '1491': 038859 '1492': 038878 '1493': 038879 '1494': 038880 '1495': 038881 '1496': 038882 '1497': 038884 '1498': 038886 '1499': 038887 '1500': 038888 '1501': 038890 '1502': 038891 '1503': 038892 '1504': 038893 '1505': 038894 '1506': 038895 '1507': 038896 '1508': 038898 '1509': 038899 '1510': 038900 '1511': 038901 '1512': 038902 '1513': 038904 '1514': 038905 '1515': 038906 '1516': 038907 '1517': 038908 '1518': 038910 '1519': 038911 '1520': 038912 '1521': 038914 '1522': 038955 '1523': 038961 '1524': 038964 '1525': 038965 '1526': 038966 '1527': 038967 '1528': 039188 '1529': 039259 '1530': 039278 '1531': 039291 '1532': 039298 '1533': 039316 '1534': 039317 '1535': 039318 '1536': 039357 '1537': 039359 '1538': 039378 '1539': 039484 '1540': 039488 '1541': 039530 '1542': 039605 '1543': 039607 '1544': 039658 '1545': 039659 '1546': 039660 '1547': 039661 '1548': 039662 '1549': 039663 '1550': 039664 '1551': 039665 '1552': 039666 '1553': 039667 '1554': 039875 '1555': 039900 '1556': 039904 '1557': '040121' '1558': '040122' '1559': '040123' '1560': '040133' '1561': '040134' '1562': 040139 '1563': '040141' '1564': '040147' '1565': '040161' '1566': 040180 '1567': 040182 '1568': 040229 '1569': '040230' '1570': '040231' '1571': '040232' '1572': '040233' '1573': '040234' '1574': '040235' '1575': '040236' '1576': '040237' '1577': 040238 '1578': 040239 '1579': '040240' '1580': '040241' '1581': '040242' '1582': '040243' '1583': '040244' '1584': '040245' '1585': '040250' '1586': 040509 '1587': '040525' '1588': '040541' '1589': '040542' '1590': 040598 '1591': '040654' '1592': '040655' '1593': '040656' '1594': '040657' '1595': 040658 '1596': 040659 '1597': '040660' '1598': 040683 '1599': '040725' '1600': 040842 '1601': 040843 '1602': 040844 '1603': 040845 '1604': 040851 '1605': 040903 '1606': 040908 '1607': 040909 '1608': 040938 '1609': 040940 '1610': 040984 '1611': 040985 '1612': 040986 '1613': 041018 '1614': 041019 '1615': '041020' '1616': '041054' '1617': 041095 '1618': '041147' '1619': 041191 '1620': 041192 '1621': '041310' '1622': 041381 '1623': 041568 '1624': '041570' '1625': '041573' '1626': '041605' '1627': 041709 '1628': '041714' '1629': 041812 '1630': 041819 '1631': 041820 '1632': 041825 '1633': 041961 '1634': 041962 '1635': 041965 '1636': 041971 '1637': 041983 '1638': '042014' '1639': '042016' '1640': '042017' '1641': 042018 '1642': 042019 '1643': '042020' '1644': '042023' '1645': '042025' '1646': 042029 '1647': '042030' '1648': '042031' '1649': '042040' '1650': '042044' '1651': '042045' '1652': '042046' '1653': 042048 '1654': 042119 '1655': '042126' '1656': 042129 '1657': '042135' '1658': 042138 '1659': 042139 '1660': '042141' '1661': '042146' '1662': '042234' '1663': '042235' '1664': '042236' '1665': 042238 '1666': '042240' '1667': '042241' '1668': '042243' '1669': '042245' '1670': '042247' '1671': '042310' '1672': '042372' '1673': '042373' '1674': '042374' '1675': '042375' '1676': '042376' '1677': '042377' '1678': '042442' '1679': '042463' '1680': '042475' '1681': 042648 '1682': 042659 '1683': '042751' '1684': '042761' '1685': 042789 '1686': 042844 '1687': 042851 '1688': 042911 '1689': 042914 '1690': 042915 '1691': 042966 '1692': 042984 '1693': '043016' '1694': 043018 '1695': 043019 '1696': '043020' '1697': '043021' '1698': '043022' '1699': '043023' '1700': '043024' '1701': '043025' '1702': '043026' '1703': '043027' '1704': 043028 '1705': 043029 '1706': '043030' '1707': '043063' '1708': '043172' '1709': '043173' '1710': '043516' '1711': '043517' '1712': 043518 '1713': 043519 '1714': '043520' '1715': '043521' '1716': '043533' '1717': '043534' '1718': '043535' '1719': '043536' '1720': 043585 '1721': 043586 '1722': 043587 '1723': 043588 '1724': 043589 '1725': 043590 '1726': 043592 '1727': 043593 '1728': 043594 '1729': 043595 '1730': 043596 '1731': 043598 '1732': 043599 '1733': '043600' '1734': 043608 '1735': '043621' '1736': '043623' '1737': 043691 '1738': 043695 '1739': 043696 '1740': 043697 '1741': 043698 '1742': 043699 '1743': '043761' '1744': '043765' '1745': '043766' '1746': '043767' '1747': 043768 '1748': '043773' '1749': 043796 '1750': 043842 '1751': 043843 '1752': 043844 '1753': 043856 '1754': 043857 '1755': 043858 '1756': 043859 '1757': 043860 '1758': 043861 '1759': 043863 '1760': 043865 '1761': 043866 '1762': 043867 '1763': 043868 '1764': 043869 '1765': 043883 '1766': 043886 '1767': 043899 '1768': 043911 '1769': 043962 '1770': 043965 '1771': 044092 '1772': '044110' '1773': 044169 '1774': '044236' '1775': '044342' '1776': '044347' '1777': '044354' '1778': '044355' '1779': '044777' '1780': 044778 '1781': 044779 '1782': 044780 '1783': 044781 '1784': 044782 '1785': 044791 '1786': 044792 '1787': 044793 '1788': 044794 '1789': 044795 '1790': 044796 '1791': 044797 '1792': 044798 '1793': 044799 '1794': 044800 '1795': 044801 '1796': 044802 '1797': 044803 '1798': 044804 '1799': 044805 '1800': 044806 '1801': 044809 '1802': 044820 '1803': 044821 '1804': 044822 '1805': 044823 '1806': 044848 '1807': 044849 '1808': 044850 '1809': 044851 '1810': 044853 '1811': 044854 '1812': 044917 '1813': 044918 '1814': 044946 '1815': 044947 '1816': 044948 '1817': 044949 '1818': 044950 '1819': 044951 '1820': 044952 '1821': '045055' '1822': 045099 '1823': '045100' '1824': '045101' '1825': '045102' '1826': '045103' '1827': 045119 '1828': '045122' '1829': '045125' '1830': '045126' '1831': '045127' '1832': 045128 '1833': 045149 '1834': '045150' '1835': '045151' '1836': '045152' '1837': '045153' '1838': '045154' '1839': '045335' '1840': 045387 '1841': 045388 '1842': 045389 '1843': 045390 '1844': 045391 '1845': 045392 '1846': 045393 '1847': '045474' '1848': '045475' '1849': 045508 '1850': '045513' '1851': '045514' '1852': '045515' '1853': '045516' '1854': '045517' '1855': 045518 '1856': 045519 '1857': '045520' '1858': '045521' '1859': '045522' '1860': '045523' '1861': 045934 '1862': 045941 '1863': '046024' '1864': '046043' '1865': 046058 '1866': 046068 '1867': 046078 '1868': 046079 '1869': '046157' '1870': 046158 '1871': 046159 '1872': '046160' '1873': '046161' '1874': '046162' '1875': 046238 '1876': '046241' '1877': '046525' '1878': '046611' '1879': '046711' '1880': '046717' '1881': 046718 '1882': '046720' '1883': '046726' '1884': '046732' '1885': '046733' '1886': '046736' '1887': 046839 '1888': 046840 '1889': 046841 '1890': 046842 '1891': 046844 '1892': 046846 '1893': 046854 '1894': 046855 '1895': 046928 '1896': 046930 '1897': '047032' '1898': 047068 '1899': 047069 '1900': '047070' '1901': '047071' '1902': '047072' '1903': '047073' '1904': '047074' '1905': '047075' '1906': '047076' '1907': '047077' '1908': '047100' '1909': 047192 '1910': 047193 '1911': 047194 '1912': 047195 '1913': 047196 '1914': 047197 '1915': 047198 '1916': 047199 '1917': '047200' '1918': '047201' '1919': '047202' '1920': '047260' '1921': '047471' '1922': '047506' '1923': '047510' '1924': '047526' '1925': 047628 '1926': '047657' '1927': 047658 '1928': 047659 '1929': '047660' '1930': '047661' '1931': '047662' '1932': '047663' '1933': '047665' '1934': '047666' '1935': '047670' '1936': '047671' '1937': '047707' '1938': 047826 '1939': 047835 '1940': 047865 '1941': 047868 '1942': 047894 '1943': 047895 '1944': 047896 '1945': 047897 '1946': 047916 '1947': 047921 '1948': 048015 '1949': 048042 '1950': 048043 '1951': 048044 '1952': 048046 '1953': 048269 '1954': 048293 '1955': 048307 '1956': 048317 '1957': 048367 '1958': 048368 '1959': 048369 '1960': 048437 '1961': 048439 '1962': 048440 '1963': 048442 '1964': 048443 '1965': 048444 '1966': 048446 '1967': 048450 '1968': 048452 '1969': 048453 '1970': 048454 '1971': 048456 '1972': 048457 '1973': 048462 '1974': 048463 '1975': 048464 '1976': 048465 '1977': 048466 '1978': 048488 '1979': 048489 '1980': 048491 '1981': 048492 '1982': 048493 '1983': 048494 '1984': 048763 '1985': 048808 '1986': 048815 '1987': 048861 '1988': 048862 '1989': 048863 '1990': 048864 '1991': 048865 '1992': 048931 '1993': 048990 '1994': 048999 '1995': 049029 '1996': 049030 '1997': 049039 '1998': 049061 '1999': 049062 '2000': 049064 '2001': 049066 '2002': 049067 '2003': 049068 '2004': 049070 '2005': 049071 '2006': 049072 '2007': 049073 '2008': 049394 '2009': 049401 '2010': 049407 '2011': 049408 '2012': 049441 '2013': 049473 '2014': 049476 '2015': 049477 '2016': 049478 '2017': 049479 '2018': 049812 '2019': 049817 '2020': 049842 '2021': 049843 '2022': 049844 '2023': 049845 '2024': 049846 '2025': 049847 '2026': 049848 '2027': 049849 '2028': 049856 '2029': 049857 '2030': '050264' '2031': '050272' '2032': '050276' '2033': 050283 '2034': '050323' '2035': '050444' '2036': '050445' '2037': '050446' '2038': '050447' '2039': 050448 '2040': 050449 '2041': 050539 '2042': '050543' '2043': '050752' '2044': '050753' '2045': '050754' '2046': 050836 '2047': 050952 '2048': 050955 '2049': 050956 '2050': '051004' '2051': '051005' '2052': '051006' '2053': '051111' '2054': '051112' '2055': '051113' '2056': '051114' '2057': '051115' '2058': '051117' '2059': 051118 '2060': '051120' '2061': '051157' '2062': 051158 '2063': '051203' '2064': '051260' '2065': '051261' '2066': '051262' '2067': '051263' '2068': '051265' '2069': '051267' '2070': 051268 '2071': 051269 '2072': '051271' '2073': '051272' '2074': '051273' '2075': '051274' '2076': '051275' '2077': '051276' '2078': 051278 '2079': 051291 '2080': 051292 '2081': '051301' '2082': '051305' '2083': '051333' '2084': 051479 '2085': '051655' '2086': 051659 '2087': '051661' '2088': '051776' '2089': 051784 '2090': 051785 '2091': 051918 '2092': 051919 '2093': 051923 '2094': 051954 '2095': 051991 '2096': 051992 '2097': 051998 '2098': 051999 '2099': '052000' '2100': '052001' '2101': '052034' '2102': '052035' '2103': '052036' '2104': '052037' '2105': 052039 '2106': '052040' '2107': '052041' '2108': '052042' '2109': '052044' '2110': '052045' '2111': 052118 '2112': 052119 '2113': '052120' '2114': '052121' '2115': '052122' '2116': '052123' '2117': '052124' '2118': '052125' '2119': '052126' '2120': '052127' '2121': 052128 '2122': 052129 '2123': '052141' '2124': '052375' '2125': 052380 '2126': 052389 '2127': 052393 '2128': 052409 '2129': '052446' '2130': '052447' '2131': 052448 '2132': 052449 '2133': '052451' '2134': '052452' '2135': '052500' '2136': '052501' '2137': '052502' '2138': 052508 '2139': '052522' '2140': 052579 '2141': 052628 '2142': 052629 '2143': '052630' '2144': '052631' '2145': '052632' '2146': '052633' '2147': '052634' '2148': '052635' '2149': '052636' '2150': '052637' '2151': 052638 '2152': 052639 '2153': '052641' '2154': '052642' '2155': '052644' '2156': '052645' '2157': '052646' '2158': '052647' '2159': 052648 '2160': 052649 '2161': '052650' '2162': 052859 '2163': 052860 '2164': 052861 '2165': 052862 '2166': 052945 '2167': 052946 '2168': 052947 '2169': 052948 '2170': 052950 '2171': 052951 '2172': 052953 '2173': 052954 '2174': 052955 '2175': '053152' '2176': '053154' '2177': '053156' '2178': '053157' '2179': 053158 '2180': 053159 '2181': '053160' '2182': 053228 '2183': 053229 '2184': 053299 '2185': '053300' '2186': '053301' '2187': '053302' '2188': 053379 '2189': 053381 '2190': '053457' '2191': 053496 '2192': '053576' '2193': 053578 '2194': 053586 '2195': 053587 '2196': 053588 '2197': 053589 '2198': 053591 '2199': 053592 '2200': '053675' '2201': '053723' '2202': '053724' '2203': '053725' '2204': '053726' '2205': '053727' '2206': 053728 '2207': 053729 '2208': 053807 '2209': 053862 '2210': 053863 '2211': 053937 '2212': 054019 '2213': '054031' '2214': '054032' '2215': '054033' '2216': '054034' '2217': '054037' '2218': 054039 '2219': '054061' '2220': '054062' '2221': '054063' '2222': '054064' '2223': 054149 '2224': '054150' '2225': '054151' '2226': '054152' '2227': '054153' '2228': '054154' '2229': '054155' '2230': '054156' '2231': 054158 '2232': 054159 '2233': '054160' '2234': '054163' '2235': '054234' '2236': '054235' '2237': '054236' '2238': '054237' '2239': 054297 '2240': '054335' '2241': '054365' '2242': '054376' '2243': '054433' '2244': '054436' '2245': '054437' '2246': 054438 '2247': '054442' '2248': '054443' '2249': '054463' '2250': '054464' '2251': '054465' '2252': '054466' '2253': '054467' '2254': 054468 '2255': 054469 '2256': '054470' '2257': '054475' '2258': '054476' '2259': 054479 '2260': 054480 '2261': 054481 '2262': 054482 '2263': 054496 '2264': '054554' '2265': 054568 '2266': '054570' '2267': '054576' '2268': 054578 '2269': 054580 '2270': '054621' '2271': '054623' '2272': '054624' '2273': '054625' '2274': '054626' '2275': '054662' '2276': '054664' '2277': '054665' '2278': '054666' '2279': '054667' '2280': '054703' '2281': 054719 '2282': '054735' '2283': '054753' '2284': 054874 '2285': 054942 '2286': '055076' '2287': 055097 '2288': '055100' '2289': '055101' '2290': '055102' '2291': '055113' '2292': 055119 '2293': '055120' '2294': '055121' '2295': '055122' '2296': '055123' '2297': '055124' '2298': 055149 '2299': 055183 '2300': 055186 '2301': '055231' '2302': '055232' '2303': '055233' '2304': '055234' '2305': '055235' '2306': '055236' '2307': '055237' '2308': 055238 '2309': '055240' '2310': '055241' '2311': '055242' '2312': 055285 '2313': 055286 '2314': 055287 '2315': 055288 '2316': 055289 '2317': 055290 '2318': 055291 '2319': 055292 '2320': 055293 '2321': 055294 '2322': 055295 '2323': '055402' '2324': '055430' '2325': '055436' '2326': '055437' '2327': 055480 '2328': 055481 '2329': 055549 '2330': '055572' '2331': 055709 '2332': '055710' '2333': '055711' '2334': '055712' '2335': '055713' '2336': '055714' '2337': '055715' '2338': '055716' '2339': '055717' '2340': 055718 '2341': 055719 '2342': 055782 '2343': 055783 '2344': 055786 '2345': 055807 '2346': 055808 '2347': 055809 '2348': 055810 '2349': 055811 '2350': 055826 '2351': 055827 '2352': 055828 '2353': 055830 '2354': 055831 '2355': 055832 '2356': 055833 '2357': 055900 '2358': '056010' '2359': '056015' '2360': '056020' '2361': 056028 '2362': 056029 '2363': '056030' '2364': '056031' '2365': '056033' '2366': '056034' '2367': '056036' '2368': '056247' '2369': 056248 '2370': 056249 '2371': '056273' '2372': '056274' '2373': '056275' '2374': '056460' '2375': '056465' '2376': '056466' '2377': '056467' '2378': 056468 '2379': 056469 '2380': '056470' '2381': '056471' '2382': '056472' '2383': '056474' '2384': 056493 '2385': 056495 '2386': 056496 '2387': 056497 '2388': 056498 '2389': 056499 '2390': '056516' '2391': '056517' '2392': 056518 '2393': 056519 '2394': '056520' '2395': '056521' '2396': '056523' '2397': '056552' '2398': 056559 '2399': 056639 '2400': '056640' '2401': '056641' '2402': '056645' '2403': '056646' '2404': 056648 '2405': 056649 '2406': '056650' '2407': '056651' '2408': 056686 '2409': 056687 '2410': 056688 '2411': 056689 '2412': 056690 '2413': 056691 '2414': 056692 '2415': 056693 '2416': 056694 '2417': 056695 '2418': 056696 '2419': 056795 '2420': 056796 '2421': 056797 '2422': 056798 '2423': 056799 '2424': 056800 '2425': 056801 '2426': 056802 '2427': 056803 '2428': 056804 '2429': 056805 '2430': 056874 '2431': 056888 '2432': 056895 '2433': 056929 '2434': 057078 '2435': '057164' '2436': '057175' '2437': '057176' '2438': '057177' '2439': 057178 '2440': 057179 '2441': 057180 '2442': '057271' '2443': '057272' '2444': '057273' '2445': '057274' '2446': '057344' '2447': '057360' '2448': '057371' '2449': '057417' '2450': 057418 '2451': '057435' '2452': '057437' '2453': 057439 '2454': '057440' '2455': '057442' '2456': '057500' '2457': '057540' '2458': 057569 '2459': '057626' '2460': '057627' '2461': 057628 '2462': 057629 '2463': '057630' '2464': 057639 '2465': '057640' '2466': 057648 '2467': 057658 '2468': '057661' '2469': '057662' '2470': '057663' '2471': '057665' '2472': 057691 '2473': 057697 '2474': 057819 '2475': 057820 '2476': 057821 '2477': 057822 '2478': 057823 '2479': 057891 '2480': 057892 '2481': 057936 '2482': 057937 '2483': 057938 '2484': 057939 '2485': 057943 '2486': 057968 '2487': 058052 '2488': 058053 '2489': 058054 '2490': 058060 '2491': 058061 '2492': 058063 '2493': 058068 '2494': 058070 '2495': 058115 '2496': 058116 '2497': 058117 '2498': 058135 '2499': 058140 '2500': 058161 '2501': 058162 '2502': 058164 '2503': 058166 '2504': 058169 '2505': 058170 '2506': 058173 '2507': 058174 '2508': 058207 '2509': 058212 '2510': 058213 '2511': 058215 '2512': 058221 '2513': 058225 '2514': 058333 '2515': 058334 '2516': 058341 '2517': 058474 '2518': 058539 '2519': 058540 '2520': 058541 '2521': 058542 '2522': 058543 '2523': 059078 '2524': 059373 '2525': 059374 '2526': 059443 '2527': 059445 '2528': 059446 '2529': 059448 '2530': 059449 '2531': 059451 '2532': 059454 '2533': 059561 '2534': 059562 '2535': 059581 '2536': 059653 '2537': 059654 '2538': 059656 '2539': 059657 '2540': 059658 '2541': 059659 '2542': 059660 '2543': 059663 '2544': 059664 '2545': 059666 '2546': 059667 '2547': 059669 '2548': 059671 '2549': 059673 '2550': 059675 '2551': 059676 '2552': 059677 '2553': 059678 '2554': 059679 '2555': 059680 '2556': 059681 '2557': 059682 '2558': 059683 '2559': 059684 '2560': 059685 '2561': 059686 '2562': 059687 '2563': 059688 '2564': 059695 '2565': 059702 '2566': 059706 '2567': 059707 '2568': 059708 '2569': 059709 '2570': 059710 '2571': 059711 '2572': 059718 '2573': 059719 '2574': 059720 '2575': 059721 '2576': 059723 '2577': 059724 '2578': 059725 '2579': 059726 '2580': 059727 '2581': 059823 '2582': 059876 '2583': 059930 '2584': '060037' '2585': 060038 '2586': '060041' '2587': '060042' '2588': '060045' '2589': 060048 '2590': '060074' '2591': '060143' '2592': '060144' '2593': '060145' '2594': '060146' '2595': '060170' '2596': '060317' '2597': '060331' '2598': '060472' '2599': '060474' '2600': '060476' '2601': '060477' '2602': 060478 '2603': '060510' '2604': '060533' '2605': '060534' '2606': '060535' '2607': '060536' '2608': '060537' '2609': '060544' '2610': '060547' '2611': 060548 '2612': 060549 '2613': '060736' '2614': '060753' '2615': '060754' '2616': '060755' '2617': '060756' '2618': '060757' '2619': 060758 '2620': '060775' '2621': '060776' '2622': '060777' '2623': 060857 '2624': 060864 '2625': 060865 '2626': 060871 '2627': 060872 '2628': 060873 '2629': 060874 '2630': 060875 '2631': 060994 '2632': '061006' '2633': '061007' '2634': 061008 '2635': '061010' '2636': '061011' '2637': '061012' '2638': '061013' '2639': 061159 '2640': '061160' '2641': '061161' '2642': '061172' '2643': '061174' '2644': '061175' '2645': '061452' '2646': '061453' '2647': 061491 '2648': 061492 '2649': 061493 '2650': 061587 '2651': 061589 '2652': 061591 '2653': 061592 '2654': 061668 '2655': '061670' '2656': 061679 '2657': '061734' '2658': '061736' '2659': '061742' '2660': 061814 '2661': 061820 '2662': 061821 '2663': 061884 '2664': '062001' '2665': '062003' '2666': '062005' '2667': '062007' '2668': '062163' '2669': '062164' '2670': '062165' '2671': 062180 '2672': 062183 '2673': 062184 '2674': 062185 '2675': 062186 '2676': 062187 '2677': 062188 '2678': 062189 '2679': 062190 '2680': 062191 '2681': 062192 '2682': 062193 '2683': 062194 '2684': 062195 '2685': 062196 '2686': '062337' '2687': '062426' '2688': '062436' '2689': '062445' '2690': '062446' '2691': 062448 '2692': 062449 '2693': '062450' '2694': '062452' '2695': 062458 '2696': '062525' '2697': '062526' '2698': '062527' '2699': 062528 '2700': 062529 '2701': '062531' '2702': '062532' '2703': '062533' '2704': '062534' '2705': 062586 '2706': 062589 '2707': 062591 '2708': 062592 '2709': 062594 '2710': 062595 '2711': 062596 '2712': '062655' '2713': '062671' '2714': '062742' '2715': 062748 '2716': 062749 '2717': '062750' '2718': '062751' '2719': '062753' '2720': '063043' '2721': '063044' '2722': '063045' '2723': '063064' '2724': '063065' '2725': '063117' '2726': 063149 '2727': 063159 '2728': '063161' '2729': 063191 '2730': 063208 '2731': '063224' '2732': '063226' '2733': '063250' '2734': '063251' '2735': '063252' '2736': '063253' '2737': '063255' '2738': '063257' '2739': 063258 '2740': 063287 '2741': 063289 '2742': 063290 '2743': 063291 '2744': 063292 '2745': '063456' '2746': '063457' '2747': '063470' '2748': '063471' '2749': '063472' '2750': '063626' '2751': '063655' '2752': '063733' '2753': '063747' '2754': '063755' '2755': '063757' '2756': '063770' '2757': 063789 '2758': 063803 '2759': 063804 '2760': 063805 '2761': 063874 '2762': 063900 '2763': 063908 '2764': 063922 '2765': 063936 '2766': 063999 '2767': '064005' '2768': '064006' '2769': '064007' '2770': 064008 '2771': 064009 '2772': '064035' '2773': 064078 '2774': 064079 '2775': 064091 '2776': 064093 '2777': '064247' '2778': 064248 '2779': 064249 '2780': '064252' '2781': '064253' '2782': '064331' '2783': '064332' '2784': '064333' '2785': '064334' '2786': 064338 '2787': '064364' '2788': '064365' '2789': '064366' '2790': '064407' '2791': 064408 '2792': 064409 '2793': '064410' '2794': '064515' '2795': '064516' '2796': '064517' '2797': 064519 '2798': '064520' '2799': '064521' '2800': '064522' '2801': '064523' '2802': '064535' '2803': '064536' '2804': '064537' '2805': 064538 '2806': '064542' '2807': '064553' '2808': '064556' '2809': '064567' '2810': 064590 '2811': 064591 '2812': 064592 '2813': 064593 '2814': 064594 '2815': '064601' '2816': '064604' '2817': 064618 '2818': '064625' '2819': '064626' '2820': '064627' '2821': 064628 '2822': 064629 '2823': '064630' '2824': '064631' '2825': 064659 '2826': 064787 '2827': 064788 '2828': 064789 '2829': 064796 '2830': 064809 '2831': 064834 '2832': 064840 '2833': 064841 '2834': 064854 '2835': 064855 '2836': 064856 '2837': 064857 '2838': 064858 '2839': 064859 '2840': 064860 '2841': 064861 '2842': 064862 '2843': 064863 '2844': 064864 '2845': 064865 '2846': 064866 '2847': 064893 '2848': 064895 '2849': 064896 '2850': 064918 '2851': 064919 '2852': 064988 '2853': 064989 '2854': 064990 '2855': 064991 '2856': 064992 '2857': 064993 '2858': 064994 '2859': 064995 '2860': '065037' '2861': 065038 '2862': 065039 '2863': '065040' '2864': '065063' '2865': '065064' '2866': '065073' '2867': '065076' '2868': '065077' '2869': 065090 '2870': '065234' '2871': '065265' '2872': 065488 '2873': 065619 '2874': 065683 '2875': 065685 '2876': '065745' '2877': '065752' '2878': '065755' '2879': '065756' '2880': '065777' '2881': 065779 '2882': 065780 '2883': 065893 '2884': 066058 '2885': '066073' '2886': '066074' '2887': '066075' '2888': '066076' '2889': 066180 '2890': 066187 '2891': 066390 '2892': 066394 '2893': '066405' '2894': 066469 '2895': 066482 '2896': 066483 '2897': '066525' '2898': '066534' '2899': '066535' '2900': '066536' '2901': '066537' '2902': 066538 '2903': 066539 '2904': '066636' '2905': '066637' '2906': 066638 '2907': '066641' '2908': '066643' '2909': '066644' '2910': '066646' '2911': 066648 '2912': 066649 '2913': '066650' '2914': 066689 '2915': 066690 '2916': '066717' '2917': '066757' '2918': 066782 '2919': 066783 '2920': '067007' '2921': '067010' '2922': '067011' '2923': '067016' '2924': '067017' '2925': '067121' '2926': '067163' '2927': '067232' '2928': '067233' '2929': '067235' '2930': '067237' '2931': 067308 '2932': '067330' '2933': '067331' '2934': '067332' '2935': '067333' '2936': '067334' '2937': '067336' '2938': '067357' '2939': 067358 '2940': 067359 '2941': '067360' '2942': '067361' '2943': '067362' '2944': '067363' '2945': '067364' '2946': '067365' '2947': '067366' '2948': '067367' '2949': 067368 '2950': '067412' '2951': '067457' '2952': '067470' '2953': '067500' '2954': '067553' '2955': '067556' '2956': '067557' '2957': 067558 '2958': 067597 '2959': 067598 '2960': '067600' '2961': '067637' '2962': 067638 '2963': 067639 '2964': '067640' '2965': '067660' '2966': '067661' '2967': '067673' '2968': '067707' '2969': '067760' '2970': '067763' '2971': '067764' '2972': '067765' '2973': '067766' '2974': 067784 '2975': 067793 '2976': 067829 '2977': 068353 '2978': 068354 '2979': 068355 '2980': 068356 '2981': 068404 '2982': 068407 '2983': 068410 '2984': 068444 '2985': 068531 '2986': 068536 '2987': 068537 '2988': 068538 '2989': 068539 '2990': 068540 '2991': 068541 '2992': 068543 '2993': 068549 '2994': 068551 '2995': 068573 '2996': 068579 '2997': 068582 '2998': 068587 '2999': 068592 '3000': 068600 '3001': 068601 '3002': 068680 '3003': 068682 '3004': 068683 '3005': 068820 '3006': 068821 '3007': 068837 '3008': 068838 '3009': 068839 '3010': 068840 '3011': 068841 '3012': 068842 '3013': 068843 '3014': 068844 '3015': 068851 '3016': 068852 '3017': 068853 '3018': 068854 '3019': 068860 '3020': 068861 '3021': 068862 '3022': 068869 '3023': 068872 '3024': 068875 '3025': 068891 '3026': 068892 '3027': 068893 '3028': 068894 '3029': 068895 '3030': 068896 '3031': 068897 '3032': 068898 '3033': 068899 '3034': 068909 '3035': 069001 '3036': 069002 '3037': 069170 '3038': 069181 '3039': 069182 '3040': 069188 '3041': 069193 '3042': 069194 '3043': 069195 '3044': 069196 '3045': 069197 '3046': 069198 '3047': 069199 '3048': 069200 '3049': 069201 '3050': 069202 '3051': 069203 '3052': 069204 '3053': 069205 '3054': 069206 '3055': 069207 '3056': 069208 '3057': 069209 '3058': 069210 '3059': 069211 '3060': 069221 '3061': 069222 '3062': 069223 '3063': 069303 '3064': 069554 '3065': 069555 '3066': 069561 '3067': 069563 '3068': 069564 '3069': 069567 '3070': 069682 '3071': 069723 '3072': 069726 '3073': 069727 '3074': 069732 '3075': 069744 '3076': 069745 '3077': 069746 '3078': 069747 '3079': 069761 '3080': 069762 '3081': 069763 '3082': 069764 '3083': 069765 '3084': 069766 '3085': 069767 '3086': 069768 '3087': 069781 '3088': 069784 '3089': 069785 '3090': 069787 '3091': 069788 '3092': 069789 '3093': 069791 '3094': 069792 '3095': 069793 '3096': 069798 '3097': 069822 '3098': 069823 '3099': 069824 '3100': 069825 '3101': 069826 '3102': 069827 '3103': 069828 '3104': 069830 '3105': 069833 '3106': 069904 '3107': 069947 '3108': 069949 '3109': 069985 '3110': '070002' '3111': '070005' '3112': '070174' '3113': '070206' '3114': '070207' '3115': 070208 '3116': 070299 '3117': '070300' '3118': '070301' '3119': '070302' '3120': '070303' '3121': '070402' '3122': '070403' '3123': 070409 '3124': '070423' '3125': '070424' '3126': '070425' '3127': '070426' '3128': '070654' '3129': '070655' '3130': '070657' '3131': '070660' '3132': 070768 '3133': '070770' '3134': '070772' '3135': '070773' '3136': '070774' '3137': '070775' '3138': 070813 '3139': 070873 '3140': 070875 '3141': 070878 '3142': 070879 '3143': 071096 '3144': '071133' '3145': '071157' '3146': 071158 '3147': '071172' '3148': '071173' '3149': '071174' '3150': '071175' '3151': '071216' '3152': '071225' '3153': 071228 '3154': '071230' '3155': '071231' '3156': '071240' '3157': '071241' '3158': '071242' '3159': '071243' '3160': '071244' '3161': '071245' '3162': '071246' '3163': '071247' '3164': 071248 '3165': 071249 '3166': '071250' '3167': '071251' '3168': '071252' '3169': '071253' '3170': '071254' '3171': '071255' '3172': '071276' '3173': '071303' '3174': '071304' '3175': '071371' '3176': '071372' '3177': '071420' '3178': '071503' '3179': '071506' '3180': '071507' '3181': 071508 '3182': 071509 '3183': '071510' '3184': '071511' '3185': '071512' '3186': '071513' '3187': '071514' '3188': '071515' '3189': '071516' '3190': '071617' '3191': '071620' '3192': '071622' '3193': 071690 '3194': 071691 '3195': 071692 '3196': 071693 '3197': 071694 '3198': 071695 '3199': 071709 '3200': '071711' '3201': '071714' '3202': '071715' '3203': 071719 '3204': '071721' '3205': '071722' '3206': 071822 '3207': 071884 '3208': 071885 '3209': 071937 '3210': 071938 '3211': '072046' '3212': '072047' '3213': '072050' '3214': '072056' '3215': 072058 '3216': 072059 '3217': '072064' '3218': '072067' '3219': 072068 '3220': 072069 '3221': '072070' '3222': '072071' '3223': '072072' '3224': '072073' '3225': '072074' '3226': '072075' '3227': '072076' '3228': 072129 '3229': '072130' '3230': '072131' '3231': '072134' '3232': '072135' '3233': '072136' '3234': '072146' '3235': 072149 '3236': '072200' '3237': '072206' '3238': '072210' '3239': '072215' '3240': '072232' '3241': '072233' '3242': '072234' '3243': 072287 '3244': 072288 '3245': 072289 '3246': 072290 '3247': '072456' '3248': 072468 '3249': '072476' '3250': '072477' '3251': '072513' '3252': '072514' '3253': '072562' '3254': '072565' '3255': '072570' '3256': '072604' '3257': '072605' '3258': '072607' '3259': '072612' '3260': 072738 '3261': 072781 '3262': 072782 '3263': 072783 '3264': 072784 '3265': 072785 '3266': 072786 '3267': 072787 '3268': 072788 '3269': 072789 '3270': 072790 '3271': 072926 '3272': 072927 '3273': 072928 '3274': 072930 '3275': 073087 '3276': 073099 '3277': '073100' '3278': '073123' '3279': '073124' '3280': '073125' '3281': 073169 '3282': '073170' '3283': '073171' '3284': '073172' '3285': '073174' '3286': '073175' '3287': 073192 '3288': 073193 '3289': '073306' '3290': 073309 '3291': 073318 '3292': '073335' '3293': '073340' '3294': '073341' '3295': '073342' '3296': '073343' '3297': '073344' '3298': '073363' '3299': '073365' '3300': '073366' '3301': '073367' '3302': 073368 '3303': 073369 '3304': '073370' '3305': '073371' '3306': '073372' '3307': '073465' '3308': '073466' '3309': '073467' '3310': 073468 '3311': 073469 '3312': 073486 '3313': 073494 '3314': 073495 '3315': 073519 '3316': '073520' '3317': '073521' '3318': '073522' '3319': '073550' '3320': '073551' '3321': '073560' '3322': '073561' '3323': '073564' '3324': '073565' '3325': '073566' '3326': 073568 '3327': '073572' '3328': '073573' '3329': 073580 '3330': 073584 '3331': 073585 '3332': 073587 '3333': 073658 '3334': '073675' '3335': '073760' '3336': '073761' '3337': '073762' '3338': '073763' '3339': '073764' '3340': '073765' '3341': '073766' '3342': '073767' '3343': 073768 '3344': 073769 '3345': '073770' '3346': '073771' '3347': '073772' '3348': '073773' '3349': '073774' '3350': '073775' '3351': '073776' '3352': '073777' '3353': 073778 '3354': 073779 '3355': 073792 '3356': 073797 '3357': 073819 '3358': 073820 '3359': 073821 '3360': 073822 '3361': 073921 '3362': '074002' '3363': '074302' '3364': '074347' '3365': 074348 '3366': '074362' '3367': '074365' '3368': '074370' '3369': '074371' '3370': '074372' '3371': '074373' '3372': '074374' '3373': '074375' '3374': '074376' '3375': '074377' '3376': 074378 '3377': 074380 '3378': 074381 '3379': 074382 '3380': 074383 '3381': 074384 '3382': 074385 '3383': 074386 '3384': 074387 '3385': 074388 '3386': 074389 '3387': 074390 '3388': 074391 '3389': 074392 '3390': 074393 '3391': '074421' '3392': '074445' '3393': '074546' '3394': 074669 '3395': '074671' '3396': '074706' '3397': 074908 '3398': 074937 '3399': 074942 '3400': 074945 '3401': 074954 '3402': 074955 '3403': 074959 '3404': 074960 '3405': 075194 '3406': '075211' '3407': '075221' '3408': '075230' '3409': '075304' '3410': '075310' '3411': '075314' '3412': '075317' '3413': '075371' '3414': '075372' '3415': '075373' '3416': '075374' '3417': '075375' '3418': '075376' '3419': '075377' '3420': 075378 '3421': 075379 '3422': 075380 '3423': 075381 '3424': 075383 '3425': 075386 '3426': 075389 '3427': 075390 '3428': 075391 '3429': 075393 '3430': 075395 '3431': 075396 '3432': 075398 '3433': 075399 '3434': '075401' '3435': '075403' '3436': '075412' '3437': '075415' '3438': '075417' '3439': 075418 '3440': 075419 '3441': '075420' '3442': '075425' '3443': '075427' '3444': 075428 '3445': 075429 '3446': '075430' '3447': '075431' '3448': '075432' '3449': '075433' '3450': '075434' '3451': '075435' '3452': '075436' '3453': 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098622 '4446': 098623 '4447': 098624 '4448': 098625 '4449': 098626 '4450': 098627 '4451': 098628 '4452': 098655 '4453': 098656 '4454': 098657 '4455': 098666 '4456': 098667 '4457': 098668 '4458': 098669 '4459': 098670 '4460': 098671 '4461': 098680 '4462': 098681 '4463': 098701 '4464': 098770 '4465': 098838 '4466': 099041 '4467': 099093 '4468': 099095 '4469': 099096 '4470': 099135 '4471': 099214 '4472': 099260 '4473': 099261 '4474': 099274 '4475': 099311 '4476': 099313 '4477': 099345 '4478': 099361 '4479': 099362 '4480': 099363 '4481': 099364 '4482': 099368 '4483': 099369 '4484': 099370 '4485': 099371 '4486': 099372 '4487': 099373 '4488': 099374 '4489': 099375 '4490': 099389 '4491': 099390 '4492': 099391 '4493': 099392 '4494': 099393 '4495': 099394 '4496': 099395 '4497': 099411 '4498': 099419 '4499': 099436 '4500': 099437 '4501': 099438 '4502': 099439 '4503': 099440 '4504': 099441 '4505': 099442 '4506': 099501 '4507': 099703 '4508': 099704 '4509': 099707 '4510': '100478' '4511': '100479' '4512': '100480' '4513': '100497' '4514': '100522' '4515': '100535' '4516': '100536' '4517': '100544' '4518': '100549' '4519': '100550' '4520': '100552' '4521': '100745' '4522': '100799' '4523': '100802' '4524': '100835' '4525': '100949' '4526': '100958' '4527': '100959' '4528': '100972' '4529': '100973' '4530': '100975' '4531': '100976' '4532': '101111' '4533': '101112' '4534': '101116' '4535': '101118' '4536': '101119' '4537': '101864' '4538': '101868' '4539': '101873' '4540': '101893' '4541': '101951' '4542': '102092' '4543': '102112' '4544': '102114' '4545': '102195' '4546': '103518' '4547': '103519' '4548': '103520' '4549': '103521' '4550': '103522' '4551': '103523' '4552': '103600' '4553': '103800' '4554': '103808' '4555': '104008' '4556': '104009' '4557': '104010' '4558': '104062' '4559': '104063' '4560': '104064' '4561': '104065' '4562': '104066' '4563': '104067' '4564': '104068' '4565': '104086' '4566': '104227' '4567': '104276' '4568': '104277' '4569': '104278' '4570': '104279' '4571': '104282' '4572': '104283' '4573': '104284' '4574': '104356' '4575': '104357' '4576': '104434' '4577': '104625' '4578': '104668' '4579': '104724' '4580': '104725' '4581': '104779' '4582': '104780' '4583': '105022' '4584': '105119' '4585': '105141' '4586': '105142' '4587': '105144' '4588': '105145' '4589': '105196' '4590': '105408' '4591': '105411' '4592': '105412' '4593': '105413' '4594': '105414' '4595': '105443' '4596': '105450' '4597': '105451' '4598': '105662' '4599': '105664' '4600': '105670' '4601': '105671' '4602': '105672' '4603': '105673' '4604': '105674' '4605': '105682' '4606': '105683' '4607': '105685' '4608': '105712' '4609': '105713' '4610': '105714' '4611': '105715' '4612': '105716' '4613': '105717' '4614': '105718' '4615': '105719' '4616': '105720' '4617': '105722' '4618': '105824' '4619': '105825' '4620': '105826' '4621': '105827' '4622': '105887' '4623': '105890' '4624': '105912' '4625': '105914' '4626': '105915' '4627': '105916' '4628': '105917' '4629': '105918' '4630': '105919' '4631': '105920' '4632': '106274' '4633': '106277' '4634': '106339' '4635': '106342' '4636': '106343' '4637': '106456' '4638': '106457' '4639': '106458' '4640': '106463' '4641': '106465' '4642': '106502' '4643': '106522' '4644': '106562' '4645': '106563' '4646': '106564' '4647': '106566' '4648': '106567' '4649': '106568' '4650': '106569' '4651': '106570' '4652': '106571' '4653': '106629' '4654': '106872' '4655': '106876' '4656': '106877' '4657': '106937' '4658': '106948' '4659': '106951' '4660': '106952' '4661': '106953' '4662': '106954' '4663': '106955' '4664': '106956' '4665': '107020' '4666': '107021' '4667': '107025' '4668': '107027' '4669': '107028' '4670': '107029' '4671': '107030' '4672': '107031' '4673': '107046' '4674': '107047' '4675': '107048' '4676': '107049' '4677': '107050' '4678': '107101' '4679': '107125' '4680': '107126' '4681': '107127' '4682': '107128' '4683': '107129' '4684': '107178' '4685': '107179' '4686': '107180' '4687': '107181' '4688': '107182' '4689': '107183' '4690': '107184' '4691': '107185' '4692': '107186' '4693': '107187' '4694': '107188' '4695': '107189' '4696': '107248' '4697': '107249' '4698': '107250' '4699': '107251' '4700': '107256' '4701': '107257' '4702': '107388' '4703': '107389' '4704': '107390' '4705': '107391' '4706': '107425' '4707': '107426' '4708': '107427' '4709': '107429' '4710': '107432' '4711': '107433' '4712': '107434' '4713': '107435' '4714': '107476' '4715': '107506' '4716': '107531' '4717': '107532' '4718': '107533' '4719': '107534' '4720': '107535' '4721': '107567' '4722': '107569' '4723': '107571' '4724': '107574' '4725': '107577' '4726': '107578' '4727': '107579' '4728': '107583' '4729': '107584' '4730': '107588' '4731': '107589' '4732': '107590' '4733': '107591' '4734': '107592' '4735': '107593' '4736': '107594' '4737': '107595' '4738': '107596' '4739': '107597' '4740': '107598' '4741': '107613' '4742': '107616' '4743': '107617' '4744': '107659' '4745': '107799' '4746': '107804' '4747': '107805' '4748': '107809' '4749': '107810' '4750': '107850' '4751': '107851' '4752': '107852' '4753': '107908' '4754': '107909' '4755': '107910' '4756': '107911' '4757': '107912' '4758': '107913' '4759': '107949' '4760': '107950' '4761': '107951' '4762': '107952' '4763': '107953' '4764': '107954' '4765': '107955' '4766': '107956' '4767': '107957' '4768': '108012' '4769': '108014' '4770': '108015' '4771': '108016' '4772': '108017' '4773': '108018' '4774': '108019' '4775': '108020' '4776': '108021' '4777': '108022' '4778': '108023' '4779': '108024' '4780': '108025' '4781': '108026' '4782': '108027' '4783': '108031' '4784': '108036' '4785': '108037' '4786': '108038' '4787': '108049' '4788': '108050' '4789': '108059' '4790': '108060' '4791': '108079' '4792': '108155' '4793': '108230' '4794': '108290' '4795': '108297' '4796': '108298' '4797': '108299' '4798': '108300' '4799': '108301' '4800': '108302' '4801': '108303' '4802': '108304' '4803': '108305' '4804': '108306' '4805': '108307' '4806': '108308' '4807': '108313' '4808': '108314' '4809': '108318' '4810': '108319' '4811': '108339' '4812': '108341' '4813': '108342' '4814': '108343' '4815': '108415' '4816': '108416' '4817': '108418' '4818': '108420' '4819': '108421' '4820': '108422' '4821': '108423' '4822': '108425' '4823': '108426' '4824': '108427' '4825': '108428' '4826': '108429' '4827': '108456' '4828': '108457' '4829': '108459' '4830': '108460' '4831': '108461' '4832': '108464' '4833': '108471' '4834': '108472' '4835': '108473' '4836': '108474' '4837': '108475' '4838': '108476' '4839': '108477' '4840': '108478' '4841': '108487' '4842': '108488' '4843': '108489' '4844': '108490' '4845': '108491' '4846': '108492' '4847': '108493' '4848': '108494' '4849': '108495' '4850': '108496' '4851': '108497' '4852': '108498' '4853': '108499' '4854': '108500' '4855': '108501' '4856': '108502' '4857': '108503' '4858': '108504' '4859': '108505' '4860': '108524' '4861': '108525' '4862': '108526' '4863': '108527' '4864': '108528' '4865': '108529' '4866': '108530' '4867': '108531' '4868': '108532' '4869': '108533' '4870': '108745' '4871': '108774' '4872': '108799' '4873': '108808' '4874': '108809' '4875': '108812' '4876': '108836' '4877': '108837' '4878': '108838' '4879': '108839' '4880': '108840' '4881': '108841' '4882': '108842' '4883': '108843' '4884': '108845' '4885': '108846' '4886': '108847' '4887': '108863' '4888': '108864' '4889': '108865' '4890': '108866' '4891': '108867' '4892': '108868' '4893': '108878' '4894': '108879' '4895': '108880' '4896': '108881' '4897': '108882' '4898': '108883' '4899': '108884' '4900': '108885' '4901': '108906' '4902': '108957' '4903': '108961' '4904': '108962' '4905': '108967' '4906': '108968' '4907': '108969' '4908': '108970' '4909': '108992' '4910': '109068' '4911': '109071' '4912': '109072' '4913': '109106' '4914': '109144' '4915': '109189' '4916': '109191' '4917': '109203' '4918': '109235' '4919': '109276' '4920': '109349' '4921': '109350' '4922': '109355' '4923': '109356' '4924': '109357' '4925': '109445' '4926': '109446' '4927': '109447' '4928': '109448' '4929': '109449' '4930': '109450' '4931': '109468' '4932': '109480' '4933': '109481' '4934': '109497' '4935': '109535' '4936': '109537' '4937': '109538' '4938': '109542' '4939': '109543' '4940': '109548' '4941': '109670' '4942': '109681' '4943': '109684' '4944': '109685' '4945': '109686' '4946': '109687' '4947': '109711' '4948': '109712' '4949': '109896' '4950': '109900' '4951': '109901' '4952': '109902' '4953': '109903' '4954': '109904' '4955': '109905' '4956': '109906' '4957': '109925' '4958': '109957' '4959': '109958' '4960': '109960' '4961': '109962' '4962': '109963' '4963': '109971' '4964': '109972' '4965': '109973' '4966': '109974' '4967': '109975' '4968': '109976' '4969': '109977' '4970': '109978' '4971': '110070' '4972': '110082' '4973': '110084' '4974': '110085' '4975': '110086' '4976': '110102' '4977': '110103' '4978': '110104' '4979': '110105' '4980': '110106' '4981': '110107' '4982': '110108' '4983': '110109' '4984': '110110' '4985': '110111' '4986': '110166' '4987': '110167' '4988': '110171' '4989': '110172' '4990': '110204' '4991': '110205' '4992': '110206' '4993': '110207' '4994': '110208' '4995': '110209' '4996': '110230' '4997': '110259' '4998': '110260' '4999': '110261' '5000': '110262' '5001': '110263' '5002': '110264' '5003': '110265' '5004': '110266' '5005': '110267' '5006': '110274' '5007': '110384' '5008': '110410' '5009': '110417' '5010': '110436' '5011': '110437' '5012': '110438' '5013': '110439' '5014': '110440' '5015': '110441' '5016': '110447' '5017': '110448' '5018': '110449' '5019': '110450' '5020': '110451' '5021': '110452' '5022': '110546' '5023': '110610' '5024': '110611' '5025': '110623' '5026': '110629' '5027': '110630' '5028': '110634' '5029': '110636' '5030': '110637' '5031': '110647' '5032': '110648' '5033': '110649' '5034': '110650' '5035': '110651' '5036': '110652' '5037': '110653' '5038': '110654' '5039': '110681' '5040': '110684' '5041': '110687' '5042': '110688' '5043': '110689' '5044': '110690' '5045': '110691' '5046': '110711' '5047': '110735' '5048': '110736' '5049': '110743' '5050': '110744' '5051': '110756' '5052': '110764' '5053': '110765' '5054': '110768' '5055': '110771' '5056': '110772' '5057': '110774' '5058': '110775' '5059': '110776' '5060': '110777' '5061': '110778' '5062': '110779' '5063': '110923' '5064': '110927' '5065': '110928' '5066': '110980' '5067': '110982' '5068': '110983' '5069': '110985' '5070': '111015' '5071': '111146' '5072': '111147' '5073': '111148' '5074': '111149' '5075': '111150' '5076': '111151' '5077': '111153' '5078': '111154' '5079': '111182' '5080': '111186' '5081': '111187' '5082': '111188' '5083': '111216' '5084': '111220' '5085': '111221' '5086': '111222' '5087': '111223' '5088': '111224' '5089': '111225' '5090': '111226' '5091': '111227' '5092': '111228' '5093': '111229' '5094': '111230' '5095': '111306' '5096': '111311' '5097': '111335' '5098': '111367' '5099': '111368' '5100': '111371' '5101': '111372' '5102': '111375' '5103': '111376' '5104': '111377' '5105': '111378' '5106': '111379' '5107': '111382' '5108': '111385' '5109': '111386' '5110': '111387' '5111': '111388' '5112': '111389' '5113': '111390' '5114': '111391' '5115': '111392' '5116': '111393' '5117': '111394' '5118': '111395' '5119': '111396' '5120': '111397' '5121': '111398' '5122': '111399' '5123': '111400' '5124': '111401' '5125': '111402' '5126': '111413' '5127': '111416' '5128': '111460' '5129': '111579' '5130': '111658' '5131': '111747' '5132': '111793' '5133': '111819' '5134': '111871' '5135': '111872' '5136': '111873' '5137': '111911' '5138': '111933' '5139': '111934' '5140': '111935' '5141': '111936' '5142': '111937' '5143': '111938' '5144': '111974' '5145': '111982' '5146': '111994' '5147': '112000' '5148': '112001' '5149': '112020' '5150': '112065' '5151': '112066' '5152': '112088' '5153': '112133' '5154': '112196' '5155': '112197' '5156': '112198' '5157': '112199' '5158': '112209' '5159': '112210' '5160': '112211' '5161': '112215' '5162': '112252' '5163': '112314' '5164': '112315' '5165': '112316' '5166': '112317' '5167': '112318' '5168': '112468' '5169': '112481' '5170': '112483' '5171': '112484' '5172': '112485' '5173': '112486' '5174': '112487' '5175': '112488' '5176': '112490' '5177': '112526' '5178': '112527' '5179': '112528' '5180': '112529' '5181': '112583' '5182': '112584' '5183': '112585' '5184': '112586' '5185': '112587' '5186': '112588' '5187': '112668' '5188': '112733' '5189': '112734' '5190': '112735' '5191': '112767' '5192': '112768' '5193': '112769' '5194': '112770' '5195': '112780' '5196': '112781' '5197': '112785' '5198': '112788' '5199': '112789' '5200': '112790' '5201': '112821' '5202': '112975' '5203': '112976' '5204': '112977' '5205': '112978' '5206': '113016' '5207': '113017' '5208': '113018' '5209': '113019' '5210': '113020' '5211': '113021' '5212': '113022' '5213': '113023' '5214': '113024' '5215': '113025' '5216': '113026' '5217': '113027' '5218': '113028' '5219': '113030' '5220': '113031' '5221': '113032' '5222': '113033' '5223': '113034' '5224': '113035' '5225': '113036' '5226': '113037' '5227': '113063' '5228': '113110' '5229': '113164' '5230': '113165' '5231': '113166' '5232': '113167' '5233': '113203' '5234': '113259' '5235': '113260' '5236': '113261' '5237': '113262' '5238': '113263' '5239': '113264' '5240': '113265' '5241': '113266' '5242': '113267' '5243': '113268' '5244': '113269' '5245': '113270' '5246': '113271' '5247': '113272' '5248': '113273' '5249': '113274' '5250': '113275' '5251': '113276' '5252': '113277' '5253': '113278' '5254': '113279' '5255': '113280' '5256': '113281' '5257': '113282' '5258': '113284' '5259': '113294' '5260': '113303' '5261': '113304' '5262': '113305' '5263': '113311' '5264': '113334' '5265': '113335' '5266': '113336' '5267': '113342' '5268': '113343' '5269': '113344' '5270': '113357' '5271': '113359' '5272': '113360' '5273': '113453' '5274': '113511' '5275': '113512' '5276': '113513' '5277': '113530' '5278': '113558' '5279': '113564' '5280': '113574' '5281': '113696' '5282': '113697' '5283': '113698' '5284': '113699' '5285': '113700' '5286': '113701' '5287': '113702' '5288': '113787' '5289': '113788' '5290': '113789' '5291': '113790' '5292': '113808' '5293': '113809' '5294': '113810' '5295': '113822' '5296': '113932' '5297': '113933' '5298': '113934' '5299': '113935' '5300': '113946' '5301': '113949' '5302': '113950' '5303': '113969' '5304': '113970' '5305': '113971' '5306': '113972' '5307': '113973' '5308': '114006' '5309': '114007' '5310': '114036' '5311': '114037' '5312': '114040' '5313': '114041' '5314': '114042' '5315': '114044' '5316': '114045' '5317': '114047' '5318': '114048' '5319': '114049' '5320': '114050' '5321': '114051' '5322': '114061' '5323': '114062' '5324': '114063' '5325': '114064' '5326': '114065' '5327': '114066' '5328': '114067' '5329': '114069' '5330': '114070' '5331': '114072' '5332': '114073' '5333': '114074' '5334': '114076' '5335': '114077' '5336': '114198' '5337': '114199' '5338': '114200' '5339': '114201' '5340': '114212' '5341': '114222' '5342': '114223' '5343': '114231' '5344': '114232' '5345': '114233' '5346': '114234' '5347': '114235' '5348': '114236' '5349': '114237' '5350': '114238' '5351': '114239' '5352': '114242' '5353': '114245' '5354': '114265' '5355': '114266' '5356': '114268' '5357': '114272' '5358': '114274' '5359': '114275' '5360': '114279' '5361': '114282' '5362': '114283' '5363': '114289' '5364': '114290' '5365': '114291' '5366': '114292' '5367': '114293' '5368': '114294' '5369': '114295' '5370': '114296' '5371': '114297' '5372': '114298' '5373': '114371' '5374': '114372' '5375': '114373' '5376': '114374' '5377': '114375' '5378': '114384' '5379': '114385' '5380': '114386' '5381': '114387' '5382': '114388' '5383': '114389' '5384': '114390' '5385': '114391' '5386': '114392' '5387': '114393' '5388': '114395' '5389': '114396' '5390': '114397' '5391': '114398' '5392': '114399' '5393': '114400' '5394': '114401' '5395': '114402' '5396': '114403' '5397': '114404' '5398': '114405' '5399': '114406' '5400': '114408' '5401': '114409' '5402': '114410' '5403': '114411' '5404': '114412' '5405': '114413' '5406': '114414' '5407': '114415' '5408': '114416' '5409': '114430' '5410': '114532' '5411': '114533' '5412': '114534' '5413': '114535' '5414': '114536' '5415': '114538' '5416': '114539' '5417': '114541' '5418': '114544' '5419': '114545' '5420': '114556' '5421': '114558' '5422': '114559' '5423': '114879' '5424': '114880' '5425': '114884' '5426': '114936' '5427': '114937' '5428': '114938' '5429': '114939' '5430': '114940' '5431': '114941' '5432': '114942' '5433': '114943' '5434': '114974' '5435': '114976' '5436': '115002' '5437': '115011' '5438': '115125' '5439': '115176' '5440': '115262' '5441': '115263' '5442': '115267' '5443': '115268' '5444': '115269' '5445': '115271' '5446': '115272' '5447': '115273' '5448': '115288' '5449': '115289' '5450': '115290' '5451': '115292' '5452': '115293' '5453': '115294' '5454': '115321' '5455': '115339' '5456': '115391' '5457': '115392' '5458': '115470' '5459': '115471' '5460': '115472' '5461': '115473' '5462': '115474' '5463': '115475' '5464': '115591' '5465': '115592' '5466': '115597' '5467': '115697' '5468': '115698' '5469': '115699' '5470': '115700' '5471': '115721' '5472': '115722' '5473': '115723' '5474': '115724' '5475': '115735' '5476': '115761' '5477': '115762' '5478': '115764' '5479': '115765' '5480': '115766' '5481': '115767' '5482': '115768' '5483': '115769' '5484': '115771' '5485': '115772' '5486': '115773' '5487': '115774' '5488': '115775' '5489': '115811' '5490': '115812' '5491': '115813' '5492': '115814' '5493': '115815' '5494': '115816' '5495': '115817' '5496': '115849' '5497': '115850' '5498': '115852' '5499': '115888' '5500': '115891' '5501': '115892' '5502': '115922' '5503': '115923' '5504': '115925' '5505': '115926' '5506': '115927' '5507': '115930' '5508': '115932' '5509': '115935' '5510': '115944' '5511': '115948' '5512': '116029' '5513': '116068' '5514': '116098' '5515': '116099' '5516': '116101' '5517': '116116' '5518': '116119' '5519': '116175' '5520': '116176' '5521': '116177' '5522': '116235' '5523': '116236' '5524': '116237' '5525': '116238' '5526': '116239' '5527': '116240' '5528': '116241' '5529': '116242' '5530': '116243' '5531': '116261' '5532': '116344' '5533': '116345' '5534': '116372' '5535': '116383' '5536': '116388' '5537': '116389' '5538': '116390' '5539': '116407' '5540': '116446' '5541': '116447' '5542': '116448' '5543': '116449' '5544': '116451' '5545': '116452' '5546': '116453' '5547': '116454' '5548': '116455' '5549': '116456' '5550': '116457' '5551': '116458' '5552': '116464' '5553': '116465' '5554': '116466' '5555': '116467' '5556': '116468' '5557': '116487' '5558': '116488' '5559': '116489' '5560': '116490' '5561': '116491' '5562': '116514' '5563': '116517' '5564': '116525' '5565': '116526' '5566': '116527' '5567': '116528' '5568': '116547' '5569': '116549' '5570': '116586' '5571': '116587' '5572': '116704' '5573': '116706' '5574': '116707' '5575': '116709' '5576': '116733' '5577': '116735' '5578': '116736' '5579': '116753' '5580': '116755' '5581': '116756' '5582': '116757' '5583': '116758' '5584': '116759' '5585': '116760' '5586': '116833' '5587': '116868' '5588': '116869' '5589': '116870' '5590': '116871' '5591': '116872' '5592': '116873' '5593': '116874' '5594': '116876' '5595': '116877' '5596': '116878' '5597': '116879' '5598': '116880' '5599': '116881' '5600': '116882' '5601': '116883' '5602': '117057' '5603': '117159' '5604': '117160' '5605': '117161' '5606': '117169' '5607': '117170' '5608': '117171' '5609': '117172' '5610': '117173' '5611': '117251' '5612': '117252' '5613': '117253' '5614': '117287' '5615': '117288' '5616': '117450' '5617': '117472' '5618': '117473' '5619': '117609' '5620': '117610' '5621': '117611' '5622': '117612' '5623': '117613' '5624': '117614' '5625': '117626' '5626': '117627' '5627': '117628' '5628': '117629' '5629': '117630' '5630': '117631' '5631': '117632' '5632': '117666' '5633': '117667' '5634': '117668' '5635': '117669' '5636': '117670' '5637': '117846' '5638': '117883' '5639': '117884' '5640': '117885' '5641': '117886' '5642': '117887' '5643': '117942' '5644': '117943' '5645': '117944' '5646': '117945' '5647': '117946' '5648': '117961' '5649': '117966' '5650': '117967' '5651': '117970' '5652': '117991' '5653': '118000' '5654': '118012' '5655': '118058' '5656': '118059' '5657': '118060' '5658': '118061' '5659': '118062' '5660': '118063' '5661': '118068' '5662': '118070' '5663': '118084' '5664': '118085' '5665': '118087' '5666': '118195' '5667': '118196' '5668': '118222' '5669': '118223' '5670': '118257' '5671': '118276' '5672': '118277' '5673': '118279' '5674': '118327' '5675': '118384' '5676': '118478' '5677': '118484' '5678': '118489' '5679': '118496' '5680': '118498' '5681': '118499' '5682': '118500' '5683': '118502' '5684': '118503' '5685': '118504' '5686': '118505' '5687': '118507' '5688': '118569' '5689': '118618' '5690': '118629' '5691': '118670' '5692': '118671' '5693': '118672' '5694': '118674' '5695': '118734' '5696': '118735' '5697': '118738' '5698': '118739' '5699': '118886' '5700': '118891' '5701': '118920' '5702': '118921' '5703': '118922' '5704': '118923' '5705': '118950' '5706': '118951' '5707': '118952' '5708': '118953' '5709': '118954' '5710': '118955' '5711': '118957' '5712': '118958' '5713': '118972' '5714': '118986' '5715': '118987' '5716': '118988' '5717': '119025' '5718': '119026' '5719': '119027' '5720': '119063' '5721': '119086' '5722': '119095' '5723': '119097' '5724': '119118' '5725': '119134' '5726': '119187' '5727': '119193' '5728': '119257' '5729': '119369' '5730': '119379' '5731': '119413' '5732': '119545' '5733': '119569' '5734': '119571' '5735': '119574' '5736': '119575' '5737': '119578' '5738': '119579' '5739': '119580' '5740': '119582' '5741': '119583' '5742': '119584' '5743': '119592' '5744': '119715' '5745': '119719' '5746': '119725' '5747': '119726' '5748': '119727' '5749': '119745' '5750': '119828' '5751': '119830' '5752': '119831' '5753': '119893' '5754': '119894' '5755': '119895' '5756': '119896' '5757': '119897' '5758': '119898' '5759': '119899' '5760': '119900' '5761': '119901' '5762': '119922' '5763': '119938' '5764': '119939' '5765': '119940' '5766': '119941' '5767': '119942' '5768': '119979' '5769': '119985' '5770': '119988' '5771': '119991' '5772': '119992' '5773': '119993' '5774': '119994' '5775': '120099' '5776': '120105' '5777': '120109' '5778': '120111' '5779': '120112' '5780': '120150' '5781': '120160' '5782': '120161' '5783': '120171' '5784': '120172' '5785': '120177' '5786': '120178' '5787': '120179' '5788': '120183' '5789': '120184' '5790': '120188' '5791': '120189' '5792': '120194' '5793': '120196' '5794': '120199' '5795': '120200' '5796': '120201' '5797': '120203' '5798': '120206' '5799': '120207' '5800': '120208' '5801': '120296' '5802': '120297' '5803': '120298' '5804': '120299' '5805': '120300' '5806': '120302' '5807': '120303' '5808': '120304' '5809': '120305' '5810': '120306' '5811': '120307' '5812': '120308' '5813': '120309' '5814': '120310' '5815': '120312' '5816': '120313' '5817': '120314' '5818': '120315' '5819': '120316' '5820': '120317' '5821': '120318' '5822': '120319' '5823': '120320' '5824': '120321' '5825': '120322' '5826': '120323' '5827': '120324' '5828': '120325' '5829': '120326' '5830': '120327' '5831': '120328' '5832': '120329' '5833': '120330' '5834': '120331' '5835': '120332' '5836': '120333' '5837': '120462' '5838': '120466' '5839': '120467' '5840': '120468' '5841': '120469' '5842': '120470' '5843': '120471' '5844': '120504' '5845': '120513' '5846': '120514' '5847': '120515' '5848': '120518' '5849': '120769' '5850': '120770' '5851': '120771' '5852': '120772' '5853': '120773' '5854': '120774' '5855': '120775' '5856': '120776' '5857': '120777' '5858': '120778' '5859': '120779' '5860': '120782' '5861': '121251' '5862': '121256' '5863': '121257' '5864': '121273' '5865': '121288' '5866': '121312' '5867': '121313' '5868': '121314' '5869': '121315' '5870': '121316' '5871': '121317' '5872': '121318' '5873': '121319' '5874': '121320' '5875': '121321' '5876': '121322' '5877': '121323' '5878': '121346' '5879': '121366' '5880': '121415' '5881': '121449' '5882': '121450' '5883': '121451' '5884': '121452' '5885': '121453' '5886': '121454' '5887': '121472' '5888': '121473' '5889': '121474' '5890': '121475' '5891': '121570' '5892': '121589' '5893': '121590' '5894': '121591' '5895': '121592' '5896': '121593' '5897': '121594' '5898': '121595' '5899': '121651' '5900': '121652' '5901': '121653' '5902': '121654' '5903': '121655' '5904': '121656' '5905': '121657' '5906': '121658' '5907': '121659' '5908': '121660' '5909': '121661' '5910': '121662' '5911': '121663' '5912': '121664' '5913': '121665' '5914': '121666' '5915': '121734' '5916': '121735' '5917': '121736' '5918': '121737' '5919': '121738' '5920': '121739' '5921': '121740' '5922': '121813' '5923': '121866' '5924': '121867' '5925': '121869' '5926': '121913' '5927': '121915' '5928': '121922' '5929': '121926' '5930': '121929' '5931': '121930' '5932': '121976' '5933': '121985' '5934': '121987' '5935': '121998' '5936': '122001' '5937': '122003' '5938': '122004' '5939': '122066' '5940': '122077' '5941': '122079' '5942': '122080' '5943': '122081' '5944': '122082' '5945': '122083' '5946': '122084' '5947': '122085' '5948': '122086' '5949': '122087' '5950': '122088' '5951': '122106' '5952': '122107' '5953': '122132' '5954': '122143' '5955': '122153' '5956': '122155' '5957': '122166' '5958': '122168' '5959': '122190' '5960': '122199' '5961': '122201' '5962': '122204' '5963': '122247' '5964': '122261' '5965': '122352' '5966': '122353' '5967': '122354' '5968': '122355' '5969': '122356' '5970': '122357' '5971': '122358' '5972': '122359' '5973': '122360' '5974': '122362' '5975': '122363' '5976': '122364' '5977': '122365' '5978': '122395' '5979': '122397' '5980': '122398' '5981': '122399' '5982': '122400' '5983': '122456' '5984': '122457' '5985': '122472' '5986': '122473' '5987': '122474' '5988': '122475' '5989': '122498' '5990': '122499' '5991': '122500' '5992': '122503' '5993': '122504' '5994': '122510' '5995': '122511' '5996': '122533' '5997': '122534' '5998': '122578' '5999': '122579' '6000': '122620' '6001': '122621' '6002': '122622' '6003': '122623' '6004': '122624' '6005': '122625' '6006': '122626' '6007': '122627' '6008': '122628' '6009': '122630' '6010': '122631' '6011': '122632' '6012': '122633' '6013': '122634' '6014': '122635' '6015': '122644' '6016': '122645' '6017': '122646' '6018': '122647' '6019': '122648' '6020': '122649' '6021': '122650' '6022': '122651' '6023': '122654' '6024': '122671' '6025': '122673' '6026': '122675' '6027': '122683' '6028': '122685' '6029': '122686' '6030': '122798' '6031': '122799' '6032': '122800' '6033': '122803' '6034': '122804' '6035': '122805' '6036': '122806' '6037': '122807' '6038': '122808' '6039': '122809' '6040': '122810' '6041': '122832' '6042': '122901' '6043': '122910' '6044': '122911' '6045': '122932' '6046': '122934' '6047': '122935' '6048': '122936' '6049': '122959' '6050': '122999' '6051': '123000' '6052': '123001' '6053': '123002' '6054': '123003' '6055': '123004' '6056': '123094' '6057': '123096' '6058': '123097' '6059': '123099' '6060': '123147' '6061': '123273' '6062': '123278' '6063': '123333' '6064': '123342' '6065': '123427' '6066': '123438' '6067': '123439' '6068': '123440' '6069': '123441' '6070': '123442' '6071': '123458' '6072': '123461' '6073': '123467' '6074': '123468' '6075': '123474' '6076': '123484' '6077': '123485' '6078': '123486' '6079': '123487' '6080': '123488' '6081': '123490' '6082': '123494' '6083': '123501' '6084': '123502' '6085': '123503' '6086': '123504' '6087': '123505' '6088': '123506' '6089': '123509' '6090': '123523' '6091': '123614' '6092': '123641' '6093': '123645' '6094': '123647' '6095': '123760' '6096': '123761' '6097': '123762' '6098': '123763' '6099': '123764' '6100': '123821' '6101': '123825' '6102': '123832' '6103': '123834' '6104': '123835' '6105': '123866' '6106': '123867' '6107': '123868' '6108': '123899' '6109': '123932' '6110': '123933' '6111': '123934' '6112': '123935' '6113': '123936' '6114': '123937' '6115': '123938' '6116': '123964' '6117': '123965' '6118': '123966' '6119': '123968' '6120': '123969' '6121': '123970' '6122': '123971' '6123': '123972' '6124': '123973' '6125': '123974' '6126': '123975' '6127': '123976' '6128': '123977' '6129': '123978' '6130': '123979' '6131': '123980' '6132': '123981' '6133': '123986' '6134': '124154' '6135': '124175' '6136': '124176' '6137': '124177' '6138': '124178' '6139': '124179' '6140': '124180' '6141': '124181' '6142': '124183' '6143': '124184' '6144': '124185' '6145': '124186' '6146': '124201' '6147': '124231' '6148': '124391' '6149': '124392' '6150': '124393' '6151': '124394' '6152': '124409' '6153': '124411' '6154': '124424' '6155': '124425' '6156': '124426' '6157': '124460' '6158': '124461' '6159': '124470' '6160': '124474' '6161': '124477' '6162': '124479' '6163': '124480' '6164': '124481' '6165': '124482' '6166': '124483' '6167': '124484' '6168': '124485' '6169': '124509' '6170': '124517' '6171': '124518' '6172': '124519' '6173': '124554' '6174': '124555' '6175': '124702' '6176': '124752' '6177': '124753' '6178': '124754' '6179': '124755' '6180': '124756' '6181': '124870' '6182': '124872' '6183': '124873' '6184': '124874' '6185': '124875' '6186': '124876' '6187': '124877' '6188': '124891' '6189': '124892' '6190': '124912' '6191': '124913' '6192': '124915' '6193': '124916' '6194': '124917' '6195': '124918' '6196': '124971' '6197': '124992' '6198': '124996' '6199': '125001' '6200': '125002' '6201': '125003' '6202': '125004' '6203': '125154' '6204': '125156' '6205': '125157' '6206': '125158' '6207': '125159' '6208': '125160' '6209': '125161' '6210': '125182' '6211': '125183' '6212': '125185' '6213': '125186' '6214': '125187' '6215': '125188' '6216': '125189' '6217': '125190' '6218': '125191' '6219': '125192' '6220': '125193' '6221': '125194' '6222': '125195' '6223': '125196' '6224': '125237' '6225': '125238' '6226': '125239' '6227': '125240' '6228': '125286' '6229': '125287' '6230': '125288' '6231': '125289' '6232': '125291' '6233': '125293' '6234': '125298' '6235': '125299' '6236': '125312' '6237': '125313' '6238': '125314' '6239': '125315' '6240': '125333' '6241': '125337' '6242': '125375' '6243': '125377' '6244': '125432' '6245': '125551' '6246': '125612' '6247': '125614' '6248': '125616' '6249': '125617' '6250': '125618' '6251': '125620' '6252': '125621' '6253': '125622' '6254': '125657' '6255': '125659' '6256': '125680' '6257': '125681' '6258': '125721' '6259': '125722' '6260': '125723' '6261': '125774' '6262': '125776' '6263': '125777' '6264': '125778' '6265': '125779' '6266': '125809' '6267': '125812' '6268': '125813' '6269': '125814' '6270': '125815' '6271': '125816' '6272': '125817' '6273': '125818' '6274': '125819' '6275': '125820' '6276': '125821' '6277': '125822' '6278': '125823' '6279': '125824' '6280': '125825' '6281': '125826' '6282': '125827' '6283': '125999' '6284': '126014' '6285': '126015' '6286': '126016' '6287': '126017' '6288': '126018' '6289': '126047' '6290': '126055' '6291': '126102' '6292': '126103' '6293': '126104' '6294': '126105' '6295': '126180' '6296': '126181' '6297': '126182' '6298': '126183' '6299': '126185' '6300': '126186' '6301': '126187' '6302': '126188' '6303': '126189' '6304': '126214' '6305': '126215' '6306': '126216' '6307': '126217' '6308': '126218' '6309': '126219' '6310': '126220' '6311': '126221' '6312': '126223' '6313': '126224' '6314': '126225' '6315': '126226' '6316': '126227' '6317': '126229' '6318': '126230' '6319': '126231' '6320': '126232' '6321': '126233' '6322': '126234' '6323': '126240' '6324': '126241' '6325': '126242' '6326': '126243' '6327': '126276' '6328': '126283' '6329': '126289' '6330': '126290' '6331': '126291' '6332': '126292' '6333': '126294' '6334': '126295' '6335': '126297' '6336': '126300' '6337': '126316' '6338': '126317' '6339': '126318' '6340': '126319' '6341': '126320' '6342': '126321' '6343': '126354' '6344': '126357' '6345': '126362' '6346': '126398' '6347': '126400' '6348': '126401' '6349': '126402' '6350': '126403' '6351': '126404' '6352': '126405' '6353': '126406' '6354': '126407' '6355': '126408' '6356': '126409' '6357': '126410' '6358': '126411' '6359': '126412' '6360': '126413' '6361': '126414' '6362': '126415' '6363': '126416' '6364': '126417' '6365': '126425' '6366': '126426' '6367': '126427' '6368': '126428' '6369': '126429' '6370': '126430' '6371': '126431' '6372': '126455' '6373': '126489' '6374': '126490' '6375': '126491' '6376': '126505' '6377': '126506' '6378': '126507' '6379': '126508' '6380': '126510' '6381': '126512' '6382': '126516' '6383': '126519' '6384': '126520' '6385': '126521' '6386': '126522' '6387': '126550' '6388': '126557' '6389': '126559' '6390': '126584' '6391': '126585' '6392': '126586' '6393': '126587' '6394': '126588' '6395': '126589' '6396': '126598' '6397': '126600' '6398': '126601' '6399': '126602' '6400': '126603' '6401': '126605' '6402': '126606' '6403': '126607' '6404': '126608' '6405': '126646' '6406': '126666' '6407': '126667' '6408': '126668' '6409': '126669' '6410': '126670' '6411': '126671' '6412': '126672' '6413': '126673' '6414': '126674' '6415': '126675' '6416': '126676' '6417': '126716' '6418': '126717' '6419': '126718' '6420': '126719' '6421': '126720' '6422': '126743' '6423': '126746' '6424': '126747' '6425': '126748' '6426': '126749' '6427': '126773' '6428': '126778' '6429': '126781' '6430': '126782' '6431': '126786' '6432': '126789' '6433': '126790' '6434': '126882' '6435': '126883' '6436': '126884' '6437': '126885' '6438': '126886' '6439': '126887' '6440': '126899' '6441': '126900' '6442': '126944' '6443': '126979' '6444': '127036' '6445': '127037' '6446': '127062' '6447': '127066' '6448': '127155' '6449': '127159' '6450': '127180' '6451': '127181' '6452': '127182' '6453': '127183' '6454': '127184' '6455': '127185' '6456': '127186' '6457': '127187' '6458': '127188' '6459': '127189' '6460': '127190' '6461': '127191' '6462': '127192' '6463': '127193' '6464': '127194' '6465': '127203' '6466': '127204' '6467': '127205' '6468': '127206' '6469': '127207' '6470': '127208' '6471': '127209' '6472': '127210' '6473': '127211' '6474': '127212' '6475': '127263' '6476': '127265' '6477': '127266' '6478': '127267' '6479': '127268' '6480': '127269' '6481': '127271' '6482': '127273' '6483': '127274' '6484': '127275' '6485': '127276' '6486': '127277' '6487': '127278' '6488': '127279' '6489': '127280' '6490': '127281' '6491': '127285' '6492': '127286' '6493': '127287' '6494': '127288' '6495': '127289' '6496': '127290' '6497': '127294' '6498': '127295' '6499': '127296' '6500': '127297' '6501': '127298' '6502': '127299' '6503': '127300' '6504': '127301' '6505': '127302' '6506': '127303' '6507': '127330' '6508': '127331' '6509': '127339' '6510': '127343' '6511': '127349' '6512': '127350' '6513': '127356' '6514': '127357' '6515': '127358' '6516': '127359' '6517': '127360' '6518': '127402' '6519': '127422' '6520': '127469' '6521': '127484' '6522': '127494' '6523': '127495' '6524': '127496' '6525': '127497' '6526': '127498' '6527': '127499' '6528': '127519' '6529': '127520' '6530': '127532' '6531': '127541' '6532': '127542' '6533': '127559' '6534': '127620' '6535': '127623' '6536': '127648' '6537': '127660' '6538': '127661' '6539': '127662' '6540': '127663' '6541': '127720' '6542': '127722' '6543': '127726' '6544': '127798' '6545': '127804' '6546': '127806' '6547': '127865' '6548': '127866' '6549': '127867' '6550': '127868' '6551': '127869' '6552': '127870' '6553': '127871' '6554': '127878' '6555': '127908' '6556': '127909' '6557': '127910' '6558': '127911' '6559': '127912' '6560': '127913' '6561': '127914' '6562': '127915' '6563': '127916' '6564': '127936' '6565': '127996' '6566': '128441' '6567': '128443' '6568': '128448' '6569': '128469' '6570': '128470' '6571': '128471' '6572': '128472' '6573': '128473' '6574': '128476' '6575': '128477' '6576': '128482' '6577': '128484' '6578': '128494' '6579': '128500' '6580': '128504' '6581': '128619' '6582': '128666' '6583': '128668' '6584': '128699' '6585': '128709' '6586': '128710' '6587': '128711' '6588': '128758' '6589': '128759' '6590': '128760' '6591': '128799' '6592': '128811' '6593': '128812' '6594': '128813' '6595': '128814' '6596': '128815' '6597': '128816' '6598': '128825' '6599': '128827' '6600': '128828' '6601': '128835' '6602': '128845' '6603': '128878' '6604': '128879' '6605': '128880' '6606': '128881' '6607': '128882' '6608': '128885' '6609': '128886' '6610': '128887' '6611': '128888' '6612': '128927' '6613': '128992' '6614': '129039' '6615': '129040' '6616': '129042' '6617': '129043' '6618': '129044' '6619': '129046' '6620': '129048' '6621': '129049' '6622': '129051' '6623': '129052' '6624': '129053' '6625': '129054' '6626': '129055' '6627': '129056' '6628': '129088' '6629': '129089' '6630': '129090' '6631': '129091' '6632': '129092' '6633': '129093' '6634': '129094' '6635': '129095' '6636': '129096' '6637': '129097' '6638': '129098' '6639': '129184' '6640': '129185' '6641': '129186' '6642': '129187' '6643': '129188' '6644': '129189' '6645': '129190' '6646': '129268' '6647': '129362' '6648': '129372' '6649': '129374' '6650': '129375' '6651': '129391' '6652': '129392' '6653': '129393' '6654': '129395' '6655': '129396' '6656': '129397' '6657': '129398' '6658': '129399' '6659': '129400' '6660': '129401' '6661': '129402' '6662': '129403' '6663': '129404' '6664': '129405' '6665': '129406' '6666': '129407' '6667': '129439' '6668': '129442' '6669': '129444' '6670': '129620' '6671': '129622' '6672': '129624' '6673': '129674' '6674': '129675' '6675': '129683' '6676': '129694' '6677': '129695' '6678': '129696' '6679': '129742' '6680': '129806' '6681': '129807' '6682': '129808' '6683': '129816' '6684': '129874' '6685': '129875' '6686': '129876' '6687': '129879' '6688': '129880' '6689': '129882' '6690': '129883' '6691': '129884' '6692': '129885' '6693': '129886' '6694': '129887' '6695': '129889' '6696': '129904' '6697': '129910' '6698': '129914' '6699': '129915' '6700': '129918' '6701': '129919' '6702': '129920' '6703': '129922' '6704': '129923' '6705': '129924' '6706': '129925' '6707': '129926' '6708': '129927' '6709': '129962' '6710': '129968' '6711': '129969' '6712': '129970' '6713': '129972' '6714': '129973' '6715': '129997' '6716': '130016' '6717': '130084' '6718': '130129' '6719': '130130' '6720': '130131' '6721': '130132' '6722': '130133' '6723': '130134' '6724': '130135' '6725': '130136' '6726': '130137' '6727': '130168' '6728': '130170' '6729': '130218' '6730': '130265' '6731': '130347' '6732': '130349' '6733': '130367' '6734': '130368' '6735': '130369' '6736': '130370' '6737': '130371' '6738': '130372' '6739': '130440' '6740': '130454' '6741': '130456' '6742': '130650' '6743': '130667' '6744': '130682' '6745': '130683' '6746': '130689' '6747': '130691' '6748': '130692' '6749': '130693' '6750': '130702' '6751': '130709' '6752': '130710' '6753': '130711' '6754': '130752' '6755': '130758' '6756': '130920' '6757': '130921' '6758': '130922' '6759': '130923' '6760': '130927' '6761': '130929' '6762': '130930' '6763': '130931' '6764': '130932' '6765': '130933' '6766': '130934' '6767': '130937' '6768': '130940' '6769': '130944' '6770': '130945' '6771': '130948' '6772': '130950' '6773': '130951' '6774': '130952' '6775': '130953' '6776': '130954' '6777': '130955' '6778': '130956' '6779': '130963' '6780': '130964' '6781': '130986' '6782': '130988' '6783': '130989' '6784': '130990' '6785': '130991' '6786': '130992' '6787': '130993' '6788': '131016' '6789': '131019' '6790': '131020' '6791': '131021' '6792': '131024' '6793': '131166' '6794': '131292' '6795': '131323' '6796': '131324' '6797': '131325' '6798': '131326' '6799': '131327' '6800': '131385' '6801': '131410' '6802': '131422' '6803': '131425' '6804': '131426' '6805': '131436' '6806': '131439' '6807': '131444' '6808': '131446' '6809': '131448' '6810': '131449' '6811': '131451' '6812': '131452' '6813': '131453' '6814': '131454' '6815': '131476' '6816': '131536' '6817': '131540' '6818': '131552' '6819': '131553' '6820': '131554' '6821': '131567' '6822': '131624' '6823': '131656' '6824': '131657' '6825': '131658' '6826': '131764' '6827': '131767' '6828': '131770' '6829': '131771' '6830': '131772' '6831': '131773' '6832': '131774' '6833': '131787' '6834': '131789' '6835': '131791' '6836': '131792' '6837': '131794' '6838': '131795' '6839': '131796' '6840': '131797' '6841': '131837' '6842': '131897' '6843': '131899' '6844': '131900' '6845': '131901' '6846': '131902' '6847': '131903' '6848': '131904' '6849': '131911' '6850': '131912' '6851': '131913' '6852': '131914' '6853': '131917' '6854': '131918' '6855': '131919' '6856': '131922' '6857': '131923' '6858': '131924' '6859': '131925' '6860': '131932' '6861': '131933' '6862': '131934' '6863': '131935' '6864': '131936' '6865': '131938' '6866': '131939' '6867': '131940' '6868': '131941' '6869': '131942' '6870': '131950' '6871': '131951' '6872': '131952' '6873': '131953' '6874': '131978' '6875': '131979' '6876': '131980' '6877': '131982' '6878': '131983' '6879': '131984' '6880': '131985' '6881': '131986' '6882': '132019' '6883': '132040' '6884': '132041' '6885': '132042' '6886': '132045' '6887': '132117' '6888': '132118' '6889': '132122' '6890': '132134' '6891': '132138' '6892': '132139' '6893': '132140' '6894': '132141' '6895': '132142' '6896': '132171' '6897': '132272' '6898': '132310' '6899': '132420' '6900': '132424' '6901': '132434' '6902': '132436' '6903': '132448' '6904': '132449' '6905': '132453' '6906': '132454' '6907': '132455' '6908': '132456' '6909': '132561' '6910': '132566' '6911': '132567' '6912': '132568' '6913': '132589' '6914': '132675' '6915': '132677' '6916': '132678' '6917': '132679' '6918': '132773' '6919': '132774' '6920': '132775' '6921': '132778' '6922': '132779' '6923': '132781' '6924': '132784' '6925': '132786' '6926': '132787' '6927': '132788' '6928': '132789' '6929': '132790' '6930': '132791' '6931': '132792' '6932': '132793' '6933': '132794' '6934': '132795' '6935': '132914' '6936': '132954' '6937': '132961' '6938': '132962' '6939': '132963' '6940': '132964' '6941': '132965' '6942': '133015' '6943': '133016' '6944': '133019' '6945': '133020' '6946': '133022' '6947': '133023' '6948': '133024' '6949': '133025' '6950': '133026' '6951': '133027' '6952': '133028' '6953': '133029' '6954': '133100' '6955': '133102' '6956': '133272' '6957': '133273' '6958': '133274' '6959': '133275' '6960': '133276' '6961': '133293' '6962': '133294' '6963': '133332' '6964': '133333' '6965': '133431' '6966': '133432' '6967': '133433' '6968': '133434' '6969': '133435' '6970': '133436' '6971': '133437' '6972': '133438' '6973': '133439' '6974': '133440' '6975': '133441' '6976': '133442' '6977': '133443' '6978': '133444' '6979': '133445' '6980': '133446' '6981': '133447' '6982': '133448' '6983': '133449' '6984': '133450' '6985': '133451' '6986': '133452' '6987': '133453' '6988': '133454' '6989': '133455' '6990': '133456' '6991': '133457' '6992': '133459' '6993': '133479' '6994': '133535' '6995': '133537' '6996': '133538' '6997': '133544' '6998': '133545' '6999': '133546' '7000': '133551' '7001': '133553' '7002': '133560' '7003': '133561' '7004': '133562' '7005': '133563' '7006': '133564' '7007': '133567' '7008': '133571' '7009': '133572' '7010': '133573' '7011': '133574' '7012': '133576' '7013': '133579' '7014': '133580' '7015': '133632' '7016': '133638' '7017': '133639' '7018': '133681' '7019': '133729' '7020': '133731' '7021': '133770' '7022': '133772' '7023': '133780' '7024': '133781' '7025': '133788' '7026': '133793' '7027': '133798' '7028': '133802' '7029': '133803' '7030': '133833' '7031': '133835' '7032': '133836' '7033': '133837' '7034': '133838' '7035': '133916' '7036': '133942' '7037': '133943' '7038': '133967' '7039': '133968' '7040': '133969' '7041': '133970' '7042': '133971' '7043': '133972' '7044': '133973' '7045': '133974' '7046': '133975' '7047': '133976' '7048': '133977' '7049': '133978' '7050': '134034' '7051': '134052' '7052': '134053' '7053': '134054' '7054': '134073' '7055': '134077' '7056': '134084' '7057': '134094' '7058': '134359' '7059': '134384' '7060': '134385' '7061': '134388' '7062': '134389' '7063': '134443' '7064': '134444' '7065': '134445' '7066': '134446' '7067': '134447' '7068': '134448' '7069': '134449' '7070': '134452' '7071': '134453' '7072': '134454' '7073': '134455' '7074': '134486' '7075': '134509' '7076': '134510' '7077': '134580' '7078': '134586' '7079': '134594' '7080': '134610' '7081': '134631' '7082': '134643' '7083': '134790' '7084': '134791' '7085': '134792' '7086': '134793' '7087': '134794' '7088': '134795' '7089': '134796' '7090': '134797' '7091': '134801' '7092': '134823' '7093': '134824' '7094': '134825' '7095': '134826' '7096': '134827' '7097': '134918' '7098': '134919' '7099': '134922' '7100': '134923' '7101': '134928' '7102': '134929' '7103': '134930' '7104': '134931' '7105': '134932' '7106': '134933' '7107': '134934' '7108': '134935' '7109': '134936' '7110': '134937' '7111': '134938' '7112': '134939' '7113': '134940' '7114': '134941' '7115': '134942' '7116': '134943' '7117': '134947' '7118': '134948' '7119': '134949' '7120': '134950' '7121': '134951' '7122': '134952' '7123': '134956' '7124': '134959' '7125': '134962' '7126': '134979' '7127': '134981' '7128': '135010' '7129': '135028' '7130': '135039' '7131': '135043' '7132': '135044' '7133': '135054' '7134': '135089' '7135': '135091' '7136': '135092' '7137': '135219' '7138': '135220' '7139': '135221' '7140': '135222' '7141': '135223' '7142': '135224' '7143': '135225' '7144': '135226' '7145': '135227' '7146': '135228' '7147': '135229' '7148': '135336' '7149': '135337' '7150': '135338' '7151': '135339' '7152': '135340' '7153': '135341' '7154': '135342' '7155': '135363' '7156': '135364' '7157': '135365' '7158': '135368' '7159': '135369' '7160': '135370' '7161': '135371' '7162': '135372' '7163': '135373' '7164': '135374' '7165': '135375' '7166': '135986' '7167': '135989' '7168': '135990' '7169': '136054' '7170': '136091' '7171': '136094' '7172': '136134' '7173': '136137' '7174': '136138' '7175': '136275' '7176': '136276' '7177': '136320' '7178': '136321' '7179': '136322' '7180': '136323' '7181': '136324' '7182': '136331' '7183': '136404' '7184': '136424' '7185': '136449' '7186': '136465' '7187': '136466' '7188': '136467' '7189': '136468' '7190': '136469' '7191': '136705' '7192': '136706' '7193': '136707' '7194': '136708' '7195': '136709' '7196': '136928' '7197': '136994' '7198': '136995' '7199': '137054' '7200': '137151' '7201': '137152' '7202': '137166' '7203': '137167' '7204': '137168' '7205': '137169' '7206': '137170' '7207': '137171' '7208': '137172' '7209': '137173' '7210': '137174' '7211': '137175' '7212': '137176' '7213': '137211' '7214': '137212' '7215': '137213' '7216': '137214' '7217': '137356' '7218': '137417' '7219': '137418' '7220': '137419' '7221': '137423' '7222': '137424' '7223': '137425' '7224': '137426' '7225': '137462' '7226': '137463' '7227': '137484' '7228': '137500' '7229': '137551' '7230': '137561' '7231': '137563' '7232': '137567' '7233': '137593' '7234': '137605' '7235': '137624' '7236': '137627' '7237': '137630' '7238': '137631' '7239': '137632' '7240': '137715' '7241': '137716' '7242': '137717' '7243': '137719' '7244': '137720' '7245': '137721' '7246': '137722' '7247': '137723' '7248': '137724' '7249': '137725' '7250': '137740' '7251': '137895' '7252': '137896' '7253': '137898' '7254': '137899' '7255': '137900' '7256': '137901' '7257': '137907' '7258': '137935' '7259': '137990' '7260': '137998' '7261': '138010' '7262': '138015' '7263': '138016' '7264': '138017' '7265': '138018' '7266': '138019' '7267': '138020' '7268': '138021' '7269': '138022' '7270': '138023' '7271': '138024' '7272': '138025' '7273': '138026' '7274': '138038' '7275': '138039' '7276': '138040' '7277': '138041' '7278': '138053' '7279': '138060' '7280': '138061' '7281': '138062' '7282': '138063' '7283': '138064' '7284': '138065' '7285': '138066' '7286': '138067' '7287': '138068' '7288': '138069' '7289': '138070' '7290': '138071' '7291': '138207' '7292': '138210' '7293': '138211' '7294': '138212' '7295': '138213' '7296': '138215' '7297': '138216' '7298': '138217' '7299': '138218' '7300': '138256' '7301': '138282' '7302': '138306' '7303': '138311' '7304': '138317' '7305': '138318' '7306': '138319' '7307': '138320' '7308': '138351' '7309': '138355' '7310': '138406' '7311': '138410' '7312': '138413' '7313': '138414' '7314': '138415' '7315': '138416' '7316': '138578' '7317': '138579' '7318': '138580' '7319': '138581' '7320': '139003' '7321': '139043' '7322': '139110' '7323': '139112' '7324': '139117' '7325': '139123' '7326': '139226' '7327': '139329' '7328': '139330' '7329': '139461' '7330': '139485' '7331': '139491' '7332': '139520' '7333': '139521' '7334': '139522' '7335': '139523' '7336': '139524' '7337': '139532' '7338': '139534' '7339': '139536' '7340': '139537' '7341': '139637' '7342': '139638' '7343': '139663' '7344': '139681' '7345': '139687' '7346': '139688' '7347': '139769' '7348': '139770' '7349': '139771' '7350': '139772' '7351': '139773' '7352': '139774' '7353': '139775' '7354': '139776' '7355': '139777' '7356': '139804' '7357': '139862' '7358': '139876' '7359': '139933' '7360': '139934' '7361': '139935' '7362': '139936' '7363': '139937' '7364': '139954' '7365': '139990' '7366': '139991' '7367': '139992' '7368': '139993' '7369': '139994' '7370': '139995' '7371': '140043' '7372': '140134' '7373': '140135' '7374': '140258' '7375': '140259' '7376': '140260' '7377': '140261' '7378': '140262' '7379': '140263' '7380': '140266' '7381': '140316' '7382': '140344' '7383': '140421' '7384': '140564' '7385': '140565' '7386': '140566' '7387': '140576' '7388': '140583' '7389': '140584' '7390': '140609' '7391': '140620' '7392': '140621' '7393': '140623' '7394': '140625' '7395': '140626' '7396': '140788' '7397': '140789' '7398': '140790' '7399': '140791' '7400': '140794' '7401': '140871' '7402': '140872' '7403': '140873' '7404': '140874' '7405': '140875' '7406': '140922' '7407': '140923' '7408': '140924' '7409': '140925' '7410': '140926' '7411': '140933' '7412': '140934' '7413': '140935' '7414': '140939' '7415': '141074' '7416': '141137' '7417': '141139' '7418': '141141' '7419': '141143' '7420': '141144' '7421': '141164' '7422': '141166' '7423': '141167' '7424': '141168' '7425': '141173' '7426': '141179' '7427': '141180' '7428': '141181' '7429': '141182' '7430': '141264' '7431': '141282' '7432': '141283' '7433': '141284' '7434': '141285' '7435': '141286' '7436': '141287' '7437': '141288' '7438': '141289' '7439': '141290' '7440': '141291' '7441': '141292' '7442': '141293' '7443': '141295' '7444': '141296' '7445': '141297' '7446': '141299' '7447': '141300' '7448': '141303' '7449': '141304' '7450': '141310' '7451': '141375' '7452': '141561' '7453': '141562' '7454': '141564' '7455': '141566' '7456': '141567' '7457': '141568' '7458': '141569' '7459': '141590' '7460': '141591' '7461': '141592' '7462': '141593' '7463': '141594' '7464': '141616' '7465': '141617' '7466': '141618' '7467': '141619' '7468': '141735' '7469': '141873' '7470': '141874' '7471': '141875' '7472': '141876' '7473': '141877' '7474': '141878' '7475': '141894' '7476': '141901' '7477': '141902' '7478': '141903' '7479': '141972' '7480': '142078' '7481': '142079' '7482': '142080' '7483': '142081' '7484': '142082' '7485': '142083' '7486': '142084' '7487': '142085' '7488': '142086' '7489': '142087' '7490': '142088' '7491': '142089' '7492': '142091' '7493': '142092' '7494': '142093' '7495': '142094' '7496': '142096' '7497': '142097' '7498': '142098' '7499': '142128' '7500': '142129' '7501': '142132' '7502': '142133' '7503': '142358' '7504': '142359' '7505': '142360' '7506': '142361' '7507': '142362' '7508': '142381' '7509': '142402' '7510': '142418' '7511': '142433' '7512': '142511' '7513': '142516' '7514': '142517' '7515': '142519' '7516': '142528' '7517': '142529' '7518': '142530' '7519': '142531' '7520': '142532' '7521': '142533' '7522': '142534' '7523': '142535' '7524': '142536' '7525': '142537' '7526': '142538' '7527': '142539' '7528': '142549' '7529': '142550' '7530': '142551' '7531': '142552' '7532': '142553' '7533': '142563' '7534': '142564' '7535': '142565' '7536': '142566' '7537': '142567' '7538': '142568' '7539': '142569' '7540': '142570' '7541': '142571' '7542': '142572' '7543': '142573' '7544': '142574' '7545': '142575' '7546': '142576' '7547': '142577' '7548': '142579' '7549': '142641' '7550': '142666' '7551': '142668' '7552': '142669' '7553': '142670' '7554': '142671' '7555': '142672' '7556': '142947' '7557': '142948' '7558': '142949' '7559': '142950' '7560': '143039' '7561': '143046' '7562': '143055' '7563': '143056' '7564': '143057' '7565': '143058' '7566': '143059' '7567': '143060' '7568': '143061' '7569': '143095' '7570': '143097' '7571': '143098' '7572': '143099' '7573': '143106' '7574': '143186' '7575': '143214' '7576': '143215' '7577': '143216' '7578': '143217' '7579': '143218' '7580': '143219' '7581': '143220' '7582': '143221' '7583': '143237' '7584': '143239' '7585': '143290' '7586': '143295' '7587': '143296' '7588': '143299' '7589': '143300' '7590': '143303' '7591': '143304' '7592': '143305' '7593': '143306' '7594': '143307' '7595': '143308' '7596': '143309' '7597': '143318' '7598': '143319' '7599': '143532' '7600': '143941' '7601': '143989' '7602': '143995' '7603': '144170' '7604': '144171' '7605': '144172' '7606': '144173' '7607': '144179' '7608': '144180' '7609': '144181' '7610': '144182' '7611': '144212' '7612': '144213' '7613': '144214' '7614': '144215' '7615': '144216' '7616': '144423' '7617': '144424' '7618': '144454' '7619': '144465' '7620': '144466' '7621': '144467' '7622': '144468' '7623': '144469' '7624': '144470' '7625': '144471' '7626': '144472' '7627': '144473' '7628': '144474' '7629': '144475' '7630': '144476' '7631': '144477' '7632': '144487' '7633': '144492' '7634': '144542' '7635': '144543' '7636': '144544' '7637': '144545' '7638': '144546' '7639': '144547' '7640': '144548' '7641': '144549' '7642': '144550' '7643': '144551' '7644': '144552' '7645': '144587' '7646': '144592' '7647': '144600' '7648': '144733' '7649': '144740' '7650': '144741' '7651': '144801' '7652': '144809' '7653': '144810' '7654': '144933' '7655': '144934' '7656': '144935' '7657': '144936' '7658': '144937' '7659': '144938' '7660': '144939' '7661': '144940' '7662': '144941' '7663': '144942' '7664': '144943' '7665': '144944' '7666': '144945' '7667': '144946' '7668': '145002' '7669': '145003' '7670': '145004' 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'150268' '7964': '150287' '7965': '150288' '7966': '151404' '7967': '152103' '7968': '152253' '7969': '152254' '7970': '152258' '7971': '152261' '7972': '152262' '7973': '152324' '7974': '152418' '7975': '152425' '7976': '152480' '7977': '152543' '7978': '152545' '7979': '152568' '7980': '152569' '7981': '152570' '7982': '153337' '7983': '153383' '7984': '153452' '7985': '153946' '7986': '153955' '7987': '153956' '7988': '154303' '7989': '154305' '7990': '154306' '7991': '154307' '7992': '154308' '7993': '154309' '7994': '154413' '7995': '154414' '7996': '155066' - name: siam_cluster dtype: int64 splits: - name: train num_bytes: 432493657.0 num_examples: 7997 download_size: 432737205 dataset_size: 432493657.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Vinisf/voz2
--- license: openrail ---
Sangeetha/Kaggle-LLM-Science-Exam
--- license: apache-2.0 --- # Dataset Card for [LLM Science Exam Kaggle Competition] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary https://www.kaggle.com/competitions/kaggle-llm-science-exam/data ### Languages [en, de, tl, it, es, fr, pt, id, pl, ro, so, ca, da, sw, hu, no, nl, et, af, hr, lv, sl] ## Dataset Structure Columns prompt - the text of the question being asked A - option A; if this option is correct, then answer will be A B - option B; if this option is correct, then answer will be B C - option C; if this option is correct, then answer will be C D - option D; if this option is correct, then answer will be D E - option E; if this option is correct, then answer will be E answer - the most correct answer, as defined by the generating LLM (one of A, B, C, D, or E). ### Data Fields [Prompt, Options, Answer] ### Data Splits Train: 6684 rowa ## Dataset Creation All credits to Competition organizers. To answer difficult science-based questions written by a Large Language Model. #### Who are the source language producers? https://www.kaggle.com/competitions/kaggle-llm-science-exam/overview gpt3.5 clocks in at 175 billion parameters generated dataset ### Citation Information All credist to: https://www.kaggle.com/competitions/kaggle-llm-science-exam/overview and competiton participants who posted the curation dataset ### Contributions Kaggle - LLM Science Exam Contributors
delphiclinic/flaggedImages
--- language: - en license: apache-2.0 size_categories: - 1K<n<10K task_categories: - zero-shot-classification pretty_name: consult configs: - config_name: default data_files: - split: train path: data.csv tags: - medical ---
NativeFunction/housing
--- license: mit dataset_info: features: - name: longitude dtype: float64 - name: latitude dtype: float64 - name: housing_median_age dtype: float64 - name: total_rooms dtype: float64 - name: total_bedrooms dtype: float64 - name: population dtype: float64 - name: households dtype: float64 - name: median_income dtype: float64 - name: median_house_value dtype: float64 - name: ocean_proximity dtype: string splits: - name: train num_bytes: 1737680 num_examples: 20640 download_size: 824144 dataset_size: 1737680 configs: - config_name: default data_files: - split: train path: data/train-* ---
workitos/SD_Anime_Characters_Repository
--- license: unknown ---
premio-ai/TheArabicPile_Conversational
--- language: - ar license: cc-by-nc-4.0 task_categories: - text-generation dataset_info: - config_name: dedup features: - name: text dtype: string splits: - name: train num_bytes: 2074285191 num_examples: 1189978 download_size: 1106103903 dataset_size: 2074285191 - config_name: default features: - name: text dtype: string splits: - name: original num_bytes: 2180193661 num_examples: 1303453 download_size: 1168365713 dataset_size: 2180193661 configs: - config_name: dedup data_files: - split: train path: dedup/train-* - config_name: default data_files: - split: original path: data/train-* --- # The Arabic Pile ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64da0fd923557cdce3e514c3/J0oY67lVvecV75SOlWpjc.png) ## Introduction: The Arabic Pile is a comprehensive dataset meticulously designed to parallel the structure of The Pile and The Nordic Pile. Focused on the Arabic language, the dataset encompasses a vast array of linguistic nuances, incorporating both Modern Standard Arabic (MSA) and various Levantine, North African, and Egyptian dialects. Tailored for the training and fine-tuning of large language models, the dataset consists of 13 subsets, each uniquely crafted to cater to different linguistic domains. ## The Conversational Subset: This dataset has a collection of conversation-based content in Arabic. ## Other Subsets: 1. premio-ai/TheArabicPile 2. premio-ai/TheArabicPile_Web 3. premio-ai/TheArabicPile_Lyrics 4. premio-ai/TheArabicPile_Reviews 5. premio-ai/TheArabicPile_Dialects 6. premio-ai/TheArabicPile_Mathematics 7. premio-ai/TheArabicPile_Conversational 8. premio-ai/TheArabicPile_Articles 9. premio-ai/TheArabicPile_Poetry 10. premio-ai/TheArabicPile_Medical 11. premio-ai/TheArabicPile_Miscellaneous 12. premio-ai/TheArabicPile_SocialMedia 13. premio-ai/TheArabicPile_Translations 14. premio-ai/TheArabicPile_Books These subsets serve distinct purposes, ranging from mathematical content to conversational dialogue, medical texts, and more. Notably, there's a dedicated subset, "premio-ai/TheArabicPile_SocialMedia," emphasizing the inclusion of language commonly found in social media contexts. ## Dataset Description * Curated by: Premio.AI team * Language(s) (NLP): Arabic, multiple languages on the translation dataset. * License: CC BY-NC 4.0 Deed - Non Commercial. * For any commercial uses or licensing, please contact mo@premio.ai. ## Data Structure The datasets are divided into two main subsets: 1. Original Subset: The raw data as collected from sources, without modifications. 2. Deduplication Subset: A filtered and cleaned version, enhancing usability for large language models by reducing redundancy and noise. The Arabic Pile extends an invitation not only for training and fine-tuning large language models but also for diverse applications across linguistic domains. Whether for research, analysis, or other linguistic endeavors, The Arabic Pile stands as a rich resource for the exploration of Arabic language intricacies. ## Data Collection Please refer to the paper for more details on our data collection procedures. ## Data Format The dataset has one single column called text. The text should contain the required meta data and the body combined. This was done to make sure that it will be a good fit for direct training or fine-tuning of large language models. Please note that the meta data might require to be repeated if your training context window won’t fit the entire body of text. ## Potential Bias As with any large-scale dataset, The Arabic Pile is not immune to potential biases that may influence the training and performance of language models. It's crucial to transparently address these biases to ensure responsible usage and interpretation of the dataset. Here are some considerations: 1. Dialectal Imbalance: The dataset incorporates various Arabic dialects, with a focus on Levantine, North African, and Egyptian variants. However, there might be variations in the representation of these dialects, potentially leading to an imbalance in the training data. 2. Source Influence: Bias may arise from the sources of the original data. The dataset collects information from diverse platforms and domains, and biases inherent in those sources could transfer to the dataset. 3. Social Media Context: Some of our datasets have language from social media platforms and online platforms. This subset may introduce biases inherent in online discourse, such as informal language, colloquial expressions, and potential subjectivity in politics, religion or culture. 4. Genre and Domain Bias: Different subsets cater to distinct linguistic domains, such as medical texts, poetry, reviews, and more. Each domain carries its own linguistic characteristics, potentially leading to biases based on the genres represented. ## License Information for The Arabic Pile: No Commercial Use The Arabic Pile is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). This license is designed to facilitate the open sharing and collaboration of the dataset while ensuring responsible and non-commercial usage. Key Points of the License: * Attribution (BY): Users are free to share, adapt, and build upon the dataset, even commercially, as long as they provide appropriate attribution to the dataset creators. * Non-Commercial (NC): The dataset may not be used for commercial purposes. Any use for commercial gain requires explicit permission from the dataset creators. * No Additional Restrictions: The license allows for maximum freedom of use, provided the terms of attribution and non-commercial use are adhered to. How to Cite: When using The Arabic Pile in your work, please include a proper citation to acknowledge the dataset creators. A recommended citation can be found in the model card for easy reference. License Deed: For a comprehensive understanding of the terms and conditions, please refer to the CC BY-NC 4.0 License Deed. By adopting this license, we aim to foster a collaborative and open environment for the exploration and advancement of Arabic language understanding and natural language processing. ## Citation When utilizing The Arabic Pile in your research, development, or other projects, we kindly request that you cite the dataset using the following format: @article{alrefaie2024arabicpile, author = {Mohamed Taher Alrefaie, Mahmoud Ibrahim Barbary, Ahmed Yasser Hassanein, Shiref Khaled Elhalawany, Karim Ashraf Elsayed, Ahmed Yasser }, title = {The Arabic Pile: A Large Scale Dataset of Diverse Text for Large Language Modeling}, year = {2024}, url = {https://huggingface.co/datasets/premio-ai/TheArabicPile} }
ilaria-oneofftech/ikitracs_mitigation
--- dataset_info: features: - name: country_code dtype: string - name: country dtype: string - name: type_of_document dtype: string - name: version_number dtype: string - name: url dtype: string - name: paragraph dtype: string - name: lang dtype: string - name: parameter dtype: string - name: quote dtype: string - name: asi dtype: string - name: category dtype: string - name: high_level_category dtype: string - name: indicator dtype: string - name: paragraph_translated dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 48699276 num_examples: 82524 download_size: 16756391 dataset_size: 48699276 --- # Dataset Card for "ikitracs_mitigation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nojiyoon/pagoda-text-and-image-dataset-small
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 4264403783.0 num_examples: 862 download_size: 4254098145 dataset_size: 4264403783.0 --- # Dataset Card for "pagoda-text-and-image-dataset-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ChuckMcSneed/NeoEvalPlusN_benchmark
--- license: wtfpl tags: - leaderboard - benchmark --- Since automatic open source benchmark leaderboard got flooded with incoherent overtrained cheater meme models, I decided to take the matters in my own hands and create my own set of proprietary tests. The aim of these tests is not to see how smart the model is, but to see how good it is at execution of commands and creative writing in a reasonably quantifiable way. All tests are executed with temperature and top P≈0 and rep. penalty=1 in koboldcpp. Model-appropriate format is used, unless it doesn't work. Currently I have the following tests: ## B-test: This test is designed to establish the baseline of the model. It consists of a main task and a bunch of text, which model has to ignore while still executing the task. If the model refuses or fails to comply in a logical way immediately, it fails(0/3). After the initial request question it will get bombarded with text, it gets 1 point for reaching the first checkpoint(1/3). It will get another point for passing the test fully(2/3) and a final point for exiting the test successfully(3/3) ## C-test: Like B-test, but the task is simpler and the distracting text is way more annoying. Since the task is much simpler there are fewer points to gain. Model gets 1 point for passing main distractions and another point for successfully exiting the task. Model gets penalized for writing more than necessary, eg (Note: as an AI language model...). ## D-test: This test is designed around breaking expectations. It consists of a common math trick, but with a twist. The twist is that there is no math involved, just reading. It also has an extensive section at the end to guide the model into breaking the overtrained conditioning. Models will get 1 point for getting the answer right and up to 2 points for the right reasoning. ## P-test: Poems. Model passes each poem test for writing coherently and in rhyme. 1 point for each poem. 6 in total. After seeing Miqu-120b succeed at positive writing and fail miserably at negative, I decided to revise the test a little bit by adjusting the ratios. Assume that all models prior and including Miqu-120b were run on old set, and newer ones will be run on the revised set. ## S-test: Stylized writing. Models are asked to explain a concept in a distinct writing style or as if they are a character. Up to 1 point for each style. Models are penalized for failing to explain the concept or to keep the style all the way through the explaination. 8 in total. **Note:** not very reliable due to large human factor(±1). Take with a grain of salt. # What does each of the tests measure I dont understand111!!!11! BCD=following commands PS=creative writing # RESULTS ![This table shows the results](llm-results.png) In the table above you can see the results visiualized. You can find pure data in file [LLM-test.csv](LLM-test.csv) What they show is quite interesting: - If a model can't pass any of the BCD tests, it is most likely braindead or very filtered(kinda same lol) - If SP score of the model is very low it's writing style is dry - Creative parent(Euryale) + creative parent(Xwin)=creative child(Goliath) - Creative parent(Euryale) + dry parent(Nous-Hermes) + drier parent(SynthIA)=dry-ish child(Venus) - Dry parent(Nous-Hermes) + creative parent(Xwin) + creative parent(Mythospice)=creative child(lzlv) - Cheater meme model(una-cybertron) was somewhat creative, but braindead - Base model self-merge(Dicephal-123B) increased creativity, but didn't add extra prompt compliance - All my attempts to extend the context of XWin and Llama by using [Yukang's](https://huggingface.co/Yukang) loras have led to drastic decrease in creativity and coherence of the models :( - Miqu is currently the best 32k model according to this benchmark - Miqu-120b is the second model after ChatGPT that has 100% passed S-test! # More tests? Feel free to suggest more models for testing by opening new discussion. Mention model name, size and why do you want to test it. # Limitations - All tests were only done once. - Human factor plays a huge role in SP tests. After redoing some of the tests I noticed ±1 variation for S-test and ±0.5 variation for P-test. (Xwin is likely underrated and Spicyboros is likely overrated in S-test.) - Be critical of my own models! Since I have access to the benchmark, I can game it and rig it all I want and NOBODY can stop me. # Can it be rigged/gamed? Not sure. I've tried to game it by merging, but didn't succeed. You can check out my first attempt [here](https://huggingface.co/ChuckMcSneed/BenchmaxxxerPS-v1-123b). If my questions somehow get leaked and the models are trained on them specifically, then definitely. Update: I made [this RP model](https://huggingface.co/ChuckMcSneed/Gembo-v1-70b) while using this benchmark as a guideline for right/wrong merging. It has a ridiculously high score: 19.75/22! It's not bad, in fact, it is quite interesting in practice, but still far from ChatGPT(or maybe not, I haven't used in a while. Maybe they've lobotomized it to hell).
yardeny/processed_t5_small_context_len_128
--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 11400746112.0 num_examples: 17593744 download_size: 4372291284 dataset_size: 11400746112.0 --- # Dataset Card for "processed_t5_small_context_len_128" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
b2ktortechnik/productdata
--- license: unknown ---
irds/mmarco_v2_id
--- pretty_name: '`mmarco/v2/id`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `mmarco/v2/id` The `mmarco/v2/id` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/id). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=8,841,823 This dataset is used by: [`mmarco_v2_id_dev`](https://huggingface.co/datasets/irds/mmarco_v2_id_dev), [`mmarco_v2_id_train`](https://huggingface.co/datasets/irds/mmarco_v2_id_train) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/mmarco_v2_id', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Bonifacio2021MMarco, title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, journal={arXiv:2108.13897} } ```
maghwa/OpenHermes-2-AR-10K-50-940k-950k
--- dataset_info: features: - name: category dtype: 'null' - name: conversations dtype: string - name: custom_instruction dtype: 'null' - name: hash sequence: int64 - name: language dtype: 'null' - name: skip_prompt_formatting dtype: 'null' - name: avatarUrl dtype: string - name: model_name dtype: 'null' - name: idx dtype: 'null' - name: id dtype: string - name: title dtype: string - name: topic dtype: 'null' - name: model dtype: string - name: views dtype: float64 - name: system_prompt dtype: 'null' - name: source dtype: string splits: - name: train num_bytes: 23317721 num_examples: 10001 download_size: 9348902 dataset_size: 23317721 configs: - config_name: default data_files: - split: train path: data/train-* ---
brainer/Pill-Embeddings
--- dataset_info: features: - name: embedding sequence: sequence: sequence: sequence: float32 - name: label dtype: int64 splits: - name: train num_bytes: 9628715200 num_examples: 20620 download_size: 428533143 dataset_size: 9628715200 configs: - config_name: default data_files: - split: train path: data/train-* ---