Datasets:
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summary-test

Dataset card Data Studio Files
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s-nlp/Mintaka_Sequences_T5-large-ssm
--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: answerEntity dtype: string - name: questionEntity dtype: string - name: groundTruthAnswerEntity dtype: string - name: complexityType dtype: string - name: graph dtype: string - name: correct dtype: bool - name: g2t_sequence dtype: string - name: gap_sequence dtype: string - name: highlighted_g2t_sequence dtype: string - name: no_highlighted_g2t_sequence dtype: string - name: highlighted_gap_sequence dtype: string - name: no_highlighted_gap_sequence dtype: string - name: highlighted_determ_sequence dtype: string - name: no_highlighted_determ_sequence dtype: string splits: - name: train num_bytes: 156273506 num_examples: 54179 - name: validation num_bytes: 31978611 num_examples: 10369 - name: test num_bytes: 44824721 num_examples: 15583 download_size: 41480863 dataset_size: 233076838 --- # Dataset Card for "Mintaka_Sequences_T5-large-ssm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.3_seed_2
--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43783586 num_examples: 18928 - name: epoch_1 num_bytes: 44377250 num_examples: 18928 - name: epoch_2 num_bytes: 44448866 num_examples: 18928 - name: epoch_3 num_bytes: 44483809 num_examples: 18928 - name: epoch_4 num_bytes: 44492320 num_examples: 18928 - name: epoch_5 num_bytes: 44489018 num_examples: 18928 - name: epoch_6 num_bytes: 44475503 num_examples: 18928 - name: epoch_7 num_bytes: 44460141 num_examples: 18928 - name: epoch_8 num_bytes: 44445265 num_examples: 18928 - name: epoch_9 num_bytes: 44441178 num_examples: 18928 - name: epoch_10 num_bytes: 44438339 num_examples: 18928 - name: epoch_11 num_bytes: 44436226 num_examples: 18928 - name: epoch_12 num_bytes: 44434486 num_examples: 18928 - name: epoch_13 num_bytes: 44435475 num_examples: 18928 - name: epoch_14 num_bytes: 44431647 num_examples: 18928 - name: epoch_15 num_bytes: 44432365 num_examples: 18928 - name: epoch_16 num_bytes: 44432856 num_examples: 18928 - name: epoch_17 num_bytes: 44432911 num_examples: 18928 - name: epoch_18 num_bytes: 44429532 num_examples: 18928 - name: epoch_19 num_bytes: 44429380 num_examples: 18928 - name: epoch_20 num_bytes: 44430229 num_examples: 18928 - name: epoch_21 num_bytes: 44430596 num_examples: 18928 - name: epoch_22 num_bytes: 44431243 num_examples: 18928 - name: epoch_23 num_bytes: 44428939 num_examples: 18928 - name: epoch_24 num_bytes: 44432154 num_examples: 18928 - name: epoch_25 num_bytes: 44429301 num_examples: 18928 - name: epoch_26 num_bytes: 44429659 num_examples: 18928 - name: epoch_27 num_bytes: 44431306 num_examples: 18928 - name: epoch_28 num_bytes: 44432280 num_examples: 18928 - name: epoch_29 num_bytes: 44431422 num_examples: 18928 download_size: 701477709 dataset_size: 1332537282 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
VishwanathanR/flowers-dataset
--- dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 347100141.78 num_examples: 8189 download_size: 346573740 dataset_size: 347100141.78 --- # Dataset Card for "flowers-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Indic-Benchmark/gujarati-arc-c-2.5k
--- dataset_info: features: - name: id dtype: string - name: question struct: - name: choices list: - name: label dtype: string - name: text dtype: string - name: stem dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 1759481 num_examples: 2557 download_size: 687997 dataset_size: 1759481 configs: - config_name: default data_files: - split: train path: data/train-* ---
hjawad367/ForestPickle
--- license: mit dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 1064774270.0 num_examples: 369 download_size: 361815484 dataset_size: 1064774270.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
richardr1126/spider-schema
--- language: - en license: - cc-by-4.0 source_datasets: - spider pretty_name: Spider Schema tags: - text-to-sql dataset_info: features: - name: db_id dtype: string - name: Schema (values (type)) dtype: string - name: Primary Keys dtype: string - name: Foreign Keys dtype: string --- # Dataset Card for Spider Schema ### Dataset Summary Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases. This dataset contains the 166 databases used in the Spider dataset. ### Yale Lily Spider Leaderboards The leaderboard can be seen at https://yale-lily.github.io/spider ### Languages The text in the dataset is in English. ### Licensing Information The spider dataset is licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) ### Citation ``` @article{yu2018spider, title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task}, author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others}, journal={arXiv preprint arXiv:1809.08887}, year={2018} } ```
nrajsubramanian/usfaq
--- license: mit ---
CYF200127/MolNexTR
--- license: apache-2.0 ---
izumi-lab/wikinews-en-20230728
--- dataset_info: features: - name: text dtype: string - name: title dtype: string - name: url dtype: string splits: - name: train num_bytes: 114757457 num_examples: 43246 download_size: 38557626 dataset_size: 114757457 license: cc-by-2.5 language: - en --- # Dataset Card for "wikinews-en-20230728" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
knguyennguyen/wikipedia_laptop
--- license: mit dataset_info: features: - name: text dtype: string - name: type dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 124410662 num_examples: 14742 download_size: 67555456 dataset_size: 124410662 configs: - config_name: default data_files: - split: train path: data/train-* ---
llama2d/llama2d-synthetic
--- dataset_info: features: - name: input_ids sequence: float32 - name: coords sequence: sequence: float32 - name: labels sequence: float32 - name: attention_mask sequence: float32 splits: - name: train num_bytes: 864288 num_examples: 18 download_size: 84278 dataset_size: 864288 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "llama2d-synthetic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
omarsou/common_voice_16_1_spanish_test_set
--- license: cc0-1.0 --- # Dataset Card for Common Voice Corpus 16 Spanish Dataset ## Table of Contents - [Acknowledgement](#acknowledgement) - [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) - [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) ## Acknowledgement The dataset belongs to COMMON VOICE MOZILLA FOUNDATION. I just uploaded the spanish test set (from HERE : https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1/tree/main) ## Dataset Description - **Homepage:** https://commonvoice.mozilla.org/en/datasets - **Repository:** https://github.com/common-voice/common-voice - **Paper:** https://arxiv.org/abs/1912.06670 - **Leaderboard:** https://paperswithcode.com/dataset/common-voice - **Point of Contact:** [Vaibhav Srivastav](mailto:vaibhav@huggingface.co) ### Dataset Summary The Common Voice dataset consists of a unique MP3 and corresponding text file. ### Languages ``` Spanish ``` ## How to use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the Hindi config, simply specify the corresponding language config name (i.e., "hi" for Hindi): ```python from datasets import load_dataset cv_spanish_test_set = load_dataset("omarsou/common_voice_16_1_spanish_test_set") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset cv_spanish_test_set = load_dataset("omarsou/common_voice_16_1_spanish_test_set", streaming=True) print(next(iter(cv_16))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). ### Local ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler cv_spanish_test_set = load_dataset("omarsou/common_voice_16_1_spanish_test_set") batch_sampler = BatchSampler(RandomSampler(cv_spanish_test_set), batch_size=32, drop_last=False) dataloader = DataLoader(cv_spanish_test_set, batch_sampler=batch_sampler) ``` ### Streaming ```python from datasets import load_dataset from torch.utils.data import DataLoader cv_spanish_test_set = load_dataset("omarsou/common_voice_16_1_spanish_test_set", streaming=True) dataloader = DataLoader(cv_16, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ### Example scripts Train your own CTC or Seq2Seq Automatic Speech Recognition models on Common Voice 16 with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition). ## Dataset Structure ### Data Instances A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`. ```python { 'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5', 'path': 'et/clips/common_voice_et_18318995.mp3', 'audio': { 'path': 'et/clips/common_voice_et_18318995.mp3', 'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), 'sampling_rate': 48000 }, 'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.', 'up_votes': 2, 'down_votes': 0, 'age': 'twenties', 'gender': 'male', 'accent': '', 'locale': 'et', 'segment': '' } ``` ### Data Fields `client_id` (`string`): An id for which client (voice) made the recording `path` (`string`): The path to the audio file `audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. `sentence` (`string`): The sentence the user was prompted to speak `up_votes` (`int64`): How many upvotes the audio file has received from reviewers `down_votes` (`int64`): How many downvotes the audio file has received from reviewers `age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`) `gender` (`string`): The gender of the speaker `accent` (`string`): Accent of the speaker `locale` (`string`): The locale of the speaker `segment` (`string`): Usually an empty field ### Data Splits The speech material available is the test set. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Considerations for Using the Data ### Social Impact of Dataset The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/) ### Citation Information ``` @inproceedings{commonvoice:2020, author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.}, title = {Common Voice: A Massively-Multilingual Speech Corpus}, booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)}, pages = {4211--4215}, year = 2020 } ```
Prabakaran143/Prabakaran-dataset
--- license: openrail ---
thomaslmc/VertexQandA
--- license: apache-2.0 ---
deetsadi/processed_dwi_with_adc_semantic
--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: conditioning_image dtype: image splits: - name: train num_bytes: 35595508.0 num_examples: 200 download_size: 35408470 dataset_size: 35595508.0 --- # Dataset Card for "processed_dwi_with_adc_semantic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BrahimLtr/IRONTVMAX
--- license: afl-3.0 ---
SassyRong/meme-imgflip-small-test-dataset
--- license: cc0-1.0 task_categories: - text-to-image language: - en size_categories: - n<1K ---
hippocrates/medical_meadow_mediqa_train
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 30570668 num_examples: 2208 download_size: 12800020 dataset_size: 30570668 --- # Dataset Card for "medical_meadow_mediqa_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
SJTU-TES/GED
--- license: apache-2.0 ---
ernestum/ppo-Pendulum-v1
--- dataset_info: features: - name: obs sequence: sequence: float32 - name: acts sequence: sequence: float32 - name: infos sequence: string - name: terminal dtype: bool - name: rews sequence: float32 splits: - name: train num_bytes: 2575710 num_examples: 200 download_size: 940375 dataset_size: 2575710 --- # Dataset Card for "ppo-Pendulum-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ColumbiaNLP/FLUTE
--- annotations_creators: - expert-generated language: - en language_creators: - expert-generated - machine-generated - crowdsourced license: - afl-3.0 multilinguality: - monolingual pretty_name: FLUTE size_categories: - 1K<n<10K source_datasets: - original task_categories: - text-classification - text2text-generation task_ids: - natural-language-inference - explanation-generation --- # Dataset Card for FigLang2022SharedTask ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://figlang2022sharedtask.github.io/ - **Repository:** - **Paper:** TBA - **Point of Contact:** tuhin.chakr@cs.columbia.edu ### Dataset Summary Model in the loop approach for fig lang generation and explainability ## 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 TBA ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
Thanmay/xlsum-hi
--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: itv2 hi title dtype: string - name: itv2 hi summary dtype: string - name: itv2 hi text dtype: string splits: - name: test num_bytes: 8004101 num_examples: 1000 - name: validation num_bytes: 8068773 num_examples: 1000 download_size: 6365106 dataset_size: 16072874 configs: - config_name: default data_files: - split: test path: data/test-* - split: validation path: data/validation-* ---
tmnam20/ViPubMed_dedup
--- dataset_info: features: - name: idx dtype: int64 - name: en dtype: string splits: - name: train num_bytes: 24402494216 num_examples: 20032999 download_size: 13770715220 dataset_size: 24402494216 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_dillfrescott__Nous-Hermes-2-SOLAR-10.7B-x2-MoE
--- pretty_name: Evaluation run of dillfrescott/Nous-Hermes-2-SOLAR-10.7B-x2-MoE dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [dillfrescott/Nous-Hermes-2-SOLAR-10.7B-x2-MoE](https://huggingface.co/dillfrescott/Nous-Hermes-2-SOLAR-10.7B-x2-MoE)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dillfrescott__Nous-Hermes-2-SOLAR-10.7B-x2-MoE\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-05T03:01:59.242688](https://huggingface.co/datasets/open-llm-leaderboard/details_dillfrescott__Nous-Hermes-2-SOLAR-10.7B-x2-MoE/blob/main/results_2024-01-05T03-01-59.242688.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\": 0.6675321374781713,\n\ \ \"acc_stderr\": 0.03146967963091572,\n \"acc_norm\": 0.6683730894298693,\n\ \ \"acc_norm_stderr\": 0.03211553610160914,\n \"mc1\": 0.39657282741738065,\n\ \ \"mc1_stderr\": 0.017124930942023518,\n \"mc2\": 0.5585119677423217,\n\ \ \"mc2_stderr\": 0.015328900928932843\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.01413117676013117,\n\ \ \"acc_norm\": 0.6715017064846417,\n \"acc_norm_stderr\": 0.0137249784655373\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6571400119498108,\n\ \ \"acc_stderr\": 0.004736950810617788,\n \"acc_norm\": 0.8483369846644094,\n\ \ \"acc_norm_stderr\": 0.0035796087435066063\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810536,\n\ \ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810536\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.03716177437566018,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.03716177437566018\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\ : 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\ \ \"acc_stderr\": 0.03669072477416906,\n \"acc_norm\": 0.6358381502890174,\n\ \ \"acc_norm_stderr\": 0.03669072477416906\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\":\ \ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6085106382978723,\n \"acc_stderr\": 0.03190701242326812,\n\ \ \"acc_norm\": 0.6085106382978723,\n \"acc_norm_stderr\": 0.03190701242326812\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.543859649122807,\n\ \ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.543859649122807,\n\ \ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.48148148148148145,\n \"acc_stderr\": 0.025733641991838987,\n \"\ acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.025733641991838987\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8,\n\ \ \"acc_stderr\": 0.022755204959542943,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.022755204959542943\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n\ \ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.02931118867498311,\n\ \ \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.02931118867498311\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8838383838383839,\n \"acc_stderr\": 0.022828881775249377,\n \"\ acc_norm\": 0.8838383838383839,\n \"acc_norm_stderr\": 0.022828881775249377\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\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857396,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857396\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887037,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887037\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\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\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.03395322726375798,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.03395322726375798\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n \"\ acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\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\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\ \ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.020588491316092365,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.020588491316092365\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n\ \ \"acc_stderr\": 0.013468201614066297,\n \"acc_norm\": 0.8288633461047255,\n\ \ \"acc_norm_stderr\": 0.013468201614066297\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.02353292543104429,\n\ \ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.02353292543104429\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3418994413407821,\n\ \ \"acc_stderr\": 0.015864506461604644,\n \"acc_norm\": 0.3418994413407821,\n\ \ \"acc_norm_stderr\": 0.015864506461604644\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7843137254901961,\n \"acc_stderr\": 0.02355083135199509,\n\ \ \"acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.02355083135199509\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n\ \ \"acc_stderr\": 0.02521804037341063,\n \"acc_norm\": 0.729903536977492,\n\ \ \"acc_norm_stderr\": 0.02521804037341063\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7746913580246914,\n \"acc_stderr\": 0.02324620264781975,\n\ \ \"acc_norm\": 0.7746913580246914,\n \"acc_norm_stderr\": 0.02324620264781975\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5212765957446809,\n \"acc_stderr\": 0.029800481645628693,\n \ \ \"acc_norm\": 0.5212765957446809,\n \"acc_norm_stderr\": 0.029800481645628693\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.500651890482399,\n\ \ \"acc_stderr\": 0.012770225252255563,\n \"acc_norm\": 0.500651890482399,\n\ \ \"acc_norm_stderr\": 0.012770225252255563\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7683823529411765,\n \"acc_stderr\": 0.025626533803777562,\n\ \ \"acc_norm\": 0.7683823529411765,\n \"acc_norm_stderr\": 0.025626533803777562\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.684640522875817,\n \"acc_stderr\": 0.018798086284886883,\n \ \ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.018798086284886883\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866764,\n\ \ \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866764\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466108,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466108\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.39657282741738065,\n\ \ \"mc1_stderr\": 0.017124930942023518,\n \"mc2\": 0.5585119677423217,\n\ \ \"mc2_stderr\": 0.015328900928932843\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.010529981411838881\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6899166034874905,\n \ \ \"acc_stderr\": 0.01274030571737627\n }\n}\n```" repo_url: https://huggingface.co/dillfrescott/Nous-Hermes-2-SOLAR-10.7B-x2-MoE leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|arc:challenge|25_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-05T03-01-59.242688.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|gsm8k|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hellaswag|10_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-01-59.242688.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T03-01-59.242688.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T03-01-59.242688.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_05T03_01_59.242688 path: - '**/details_harness|winogrande|5_2024-01-05T03-01-59.242688.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-05T03-01-59.242688.parquet' - config_name: results data_files: - split: 2024_01_05T03_01_59.242688 path: - results_2024-01-05T03-01-59.242688.parquet - split: latest path: - results_2024-01-05T03-01-59.242688.parquet --- # Dataset Card for Evaluation run of dillfrescott/Nous-Hermes-2-SOLAR-10.7B-x2-MoE <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [dillfrescott/Nous-Hermes-2-SOLAR-10.7B-x2-MoE](https://huggingface.co/dillfrescott/Nous-Hermes-2-SOLAR-10.7B-x2-MoE) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_dillfrescott__Nous-Hermes-2-SOLAR-10.7B-x2-MoE", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-05T03:01:59.242688](https://huggingface.co/datasets/open-llm-leaderboard/details_dillfrescott__Nous-Hermes-2-SOLAR-10.7B-x2-MoE/blob/main/results_2024-01-05T03-01-59.242688.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": 0.6675321374781713, "acc_stderr": 0.03146967963091572, "acc_norm": 0.6683730894298693, "acc_norm_stderr": 0.03211553610160914, "mc1": 0.39657282741738065, "mc1_stderr": 0.017124930942023518, "mc2": 0.5585119677423217, "mc2_stderr": 0.015328900928932843 }, "harness|arc:challenge|25": { "acc": 0.6271331058020477, "acc_stderr": 0.01413117676013117, "acc_norm": 0.6715017064846417, "acc_norm_stderr": 0.0137249784655373 }, "harness|hellaswag|10": { "acc": 0.6571400119498108, "acc_stderr": 0.004736950810617788, "acc_norm": 0.8483369846644094, "acc_norm_stderr": 0.0035796087435066063 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.562962962962963, "acc_stderr": 0.04284958639753401, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.04284958639753401 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7631578947368421, "acc_stderr": 0.03459777606810536, "acc_norm": 0.7631578947368421, "acc_norm_stderr": 0.03459777606810536 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.02815283794249386, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.02815283794249386 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566018, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566018 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.049888765156985884, "acc_norm": 0.44, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6358381502890174, "acc_stderr": 0.03669072477416906, "acc_norm": 0.6358381502890174, "acc_norm_stderr": 0.03669072477416906 }, "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": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6085106382978723, "acc_stderr": 0.03190701242326812, "acc_norm": 0.6085106382978723, "acc_norm_stderr": 0.03190701242326812 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.543859649122807, "acc_stderr": 0.04685473041907789, "acc_norm": 0.543859649122807, "acc_norm_stderr": 0.04685473041907789 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.48148148148148145, "acc_stderr": 0.025733641991838987, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.025733641991838987 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4365079365079365, "acc_stderr": 0.04435932892851466, "acc_norm": 0.4365079365079365, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8, "acc_stderr": 0.022755204959542943, "acc_norm": 0.8, "acc_norm_stderr": 0.022755204959542943 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8303030303030303, "acc_stderr": 0.02931118867498311, "acc_norm": 0.8303030303030303, "acc_norm_stderr": 0.02931118867498311 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8838383838383839, "acc_stderr": 0.022828881775249377, "acc_norm": 0.8838383838383839, "acc_norm_stderr": 0.022828881775249377 }, "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": 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"harness|hendrycksTest-prehistory|5": { "acc": 0.7746913580246914, "acc_stderr": 0.02324620264781975, "acc_norm": 0.7746913580246914, "acc_norm_stderr": 0.02324620264781975 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5212765957446809, "acc_stderr": 0.029800481645628693, "acc_norm": 0.5212765957446809, "acc_norm_stderr": 0.029800481645628693 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.500651890482399, "acc_stderr": 0.012770225252255563, "acc_norm": 0.500651890482399, "acc_norm_stderr": 0.012770225252255563 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7683823529411765, "acc_stderr": 0.025626533803777562, "acc_norm": 0.7683823529411765, "acc_norm_stderr": 0.025626533803777562 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.684640522875817, "acc_stderr": 0.018798086284886883, "acc_norm": 0.684640522875817, "acc_norm_stderr": 0.018798086284886883 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7877551020408163, "acc_stderr": 0.026176967197866764, "acc_norm": 0.7877551020408163, "acc_norm_stderr": 0.026176967197866764 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.028762349126466108, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466108 }, "harness|hendrycksTest-virology|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.39657282741738065, "mc1_stderr": 0.017124930942023518, "mc2": 0.5585119677423217, "mc2_stderr": 0.015328900928932843 }, "harness|winogrande|5": { "acc": 0.8310970797158642, "acc_stderr": 0.010529981411838881 }, "harness|gsm8k|5": { "acc": 0.6899166034874905, "acc_stderr": 0.01274030571737627 } } ``` ## 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]
adityarra07/train_ds_noise
--- dataset_info: features: - name: audio struct: - name: array sequence: float32 - name: path dtype: 'null' - name: sampling_rate dtype: int64 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 5052608063.049213 num_examples: 22152 - name: test num_bytes: 114044060.65026213 num_examples: 500 download_size: 5191539498 dataset_size: 5166652123.699475 --- # Dataset Card for "train_ds_noise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Poreya/azil
--- license: mit ---
IvanD2002/Task_Dataset_Instruct_Format
--- license: apache-2.0 ---
Falah/tilt_shift_photography_prompts
--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 62449 num_examples: 1000 download_size: 1523 dataset_size: 62449 --- # Dataset Card for "tilt_shift_photography_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PCA-Bench/PCA-Bench-V1
--- dataset_info: - config_name: Autonomous Driving features: - name: domain dtype: string - name: image dtype: image - name: question dtype: string - name: actions sequence: string - name: answer_index dtype: int64 - name: reason dtype: string - name: key_concept sequence: string - name: question_prompt dtype: string - name: answer_with_reason dtype: string - name: full_meta_data_json dtype: string splits: - name: test_open num_bytes: 134659773 num_examples: 100 - name: test_closed num_bytes: 67549223 num_examples: 150 download_size: 270416985 dataset_size: 202208996 - config_name: Domestic Robot features: - name: domain dtype: string - name: image dtype: image - name: question dtype: string - name: actions sequence: string - name: answer_index dtype: int64 - name: reason dtype: string - name: key_concept sequence: string - name: question_prompt dtype: string - name: answer_with_reason dtype: string - name: full_meta_data_json dtype: string splits: - name: test_open num_bytes: 91702060 num_examples: 100 - name: test_closed num_bytes: 177827577 num_examples: 200 download_size: 105390299 dataset_size: 269529637 - config_name: Open-World Game features: - name: domain dtype: string - name: image dtype: image - name: question dtype: string - name: actions sequence: string - name: answer_index dtype: int64 - name: reason dtype: string - name: key_concept sequence: string - name: question_prompt dtype: string - name: answer_with_reason dtype: string - name: full_meta_data_json dtype: string splits: - name: test_open num_bytes: 16139511 num_examples: 117 - name: test_closed num_bytes: 19069366 num_examples: 141 download_size: 34988721 dataset_size: 35208877 configs: - config_name: Autonomous Driving data_files: - split: test_open path: Autonomous Driving/test_open-* - split: test_closed path: Autonomous Driving/test_closed-* - config_name: Domestic Robot data_files: - split: test_open path: Domestic Robot/test_open-* - split: test_closed path: Domestic Robot/test_closed-* - config_name: Open-World Game data_files: - split: test_open path: Open-World Game/test_open-* - split: test_closed path: Open-World Game/test_closed-* license: apache-2.0 task_categories: - multiple-choice - visual-question-answering language: - en pretty_name: PCA-Bench --- <h1 align="center">PCA-Bench</h1> <p align="center"> <a href="https://github.com/pkunlp-icler/PCA-EVAL"> <img alt="Static Badge" src="https://img.shields.io/badge/Github-Online-white"> <a href="https://github.com/pkunlp-icler/PCA-EVAL/blob/main/PCA_Bench_Paper.pdf"> <img alt="Static Badge" src="https://img.shields.io/badge/Paper-PCABench-red"> <a href="https://huggingface.co/datasets/PCA-Bench/PCA-Bench-V1"> <img alt="Static Badge" src="https://img.shields.io/badge/HFDataset-PCABenchV1-yellow"> </a> <a href="https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV"> <img alt="Static Badge" src="https://img.shields.io/badge/Leaderboard-Online-blue"> </a> </p> *PCA-Bench is an innovative benchmark for evaluating and locating errors in Multimodal LLMs when conducting embodied decision making tasks, specifically focusing on perception, cognition, and action.* ## Release - [2024.02.15] [PCA-Bench-V1](https://github.com/pkunlp-icler/PCA-EVAL) is released. We release the open and closed track data in [huggingface](https://huggingface.co/datasets/PCA-Bench/PCA-Bench-V1). We also set an online [leaderboard ](https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV) accepting users' submission. - [2023.12.15] [PCA-EVAL](https://arxiv.org/abs/2310.02071) is accepted to Foundation Model for Decision Making Workshop @NeurIPS 2023. PCA-Evaluation tool is released in github. ## Leaderboard [Leaderboard with Full Metrics](https://docs.qq.com/sheet/DVUd4WUpGRHRqUnNV) ## Submit Results 📢 For close track evaluaiton and PCA-Evaluation, please follow [this file](https://github.com/pkunlp-icler/PCA-EVAL/blob/main/pca-eval/results/chatgpt_holmes_outputs/Autonomous%20Driving.json) to organize your model output. Submit **Six JSON files** from different domains and different tracks, along with your **model name** and **organization** to us via [email](mailto:leo.liang.chen@stu.pku.edu.cn). Ensure you use the dataset's provided prompt as the default input for fair comparison. We will send the PCA-Eval results of your model to you and update the leaderboard. We provide sample code to get the six json files. User only needs to add your model inference code: ```python # Sample code for PCA-Eval from datasets import load_dataset from tqdm import tqdm import json import os def YOUR_INFERENCE_CODE(prompt,image): """Simple single round multimodal conversation call. """ response = YOUR_MODEL.inference(prompt,image) return response output_path = "./Results-DIR-PATH/" os.mkdir(output_path) dataset_ad = load_dataset("PCA-Bench/PCA-Bench-V1","Autonomous Driving") dataset_dr = load_dataset("PCA-Bench/PCA-Bench-V1","Domestic Robot") dataset_og = load_dataset("PCA-Bench/PCA-Bench-V1","Open-World Game") test_dataset_dict = {"Autonomous-Driving":dataset_ad,"Domestic-Robot":dataset_dr,"Open-World-Game":dataset_og} test_split = ["test_closed","test_open"] test_domain = list(test_dataset_dict.keys()) for domain in test_domain: for split in test_split: print("testing on %s:%s"%(domain,split)) prediction_results = [] output_filename = output_path+"%s-%s.json"%(domain,split) prompts = test_dataset_dict[domain][split]['question_prompt'] images = test_dataset_dict[domain][split]['image'] for prompt_id in tqdm(range(len(prompts))): user_inputs = prompts[prompt_id] # do not change the prompts for fair comparison index = prompt_id image = images[prompt_id] outputs = YOUR_INFERENCE_CODE(user_inputs,image) prediction_results.append({ 'prompt': user_inputs, 'model_output': outputs, 'index': index, }) with open(output_filename, 'w') as f: json.dump(prediction_results, f, indent=4) # submit the 6 json files in the output_path to our email ``` You could also simply compute the multiple-choice accuracy locally as a comparison metric in your own experiments. However, in the online leaderboard, we only consider the average action score and Genuine PCA score when ranking models. For more information, refer to the offical [github repo](https://github.com/pkunlp-icler/PCA-EVAL)
laion/School_BUD-E
--- license: cc-by-4.0 ---
wiki_source
--- annotations_creators: - found language_creators: - found language: - en - sv license: - unknown multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: WikiSource dataset_info: features: - name: id dtype: string - name: translation dtype: translation: languages: - en - sv config_name: en-sv splits: - name: train num_bytes: 8153542 num_examples: 33283 download_size: 2375052 dataset_size: 8153542 --- # Dataset Card for WikiSource ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://opus.nlpl.eu/WikiSource.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
xinqiyang/iruca_llama2_japanese_demo
--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 24485.34975369458 num_examples: 15 download_size: 3242 dataset_size: 24485.34975369458 configs: - config_name: default data_files: - split: train path: data/train-* --- # iruca-1k: Lazy Llama 2 Formatting This is a subset (1000 samples) of the excellent [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing). Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab. ### Format from xlsx file to CSV ```bash pip install openpyxl pandas python generate.py pip install huggingface_hub huggingface-cli repo create iruca_llama2_japanese_demo --type dataset git clone https://huggingface.co/datasets/xinqiyang/iruca_llama2_japanese_demo ```
sedkichayata/beauty
--- license: apache-2.0 license_name: sedki license_link: LICENSE ---
newspop
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring paperswithcode_id: null pretty_name: News Popularity in Multiple Social Media Platforms tags: - social-media-shares-prediction dataset_info: features: - name: id dtype: int32 - name: title dtype: string - name: headline dtype: string - name: source dtype: string - name: topic dtype: string - name: publish_date dtype: string - name: facebook dtype: int32 - name: google_plus dtype: int32 - name: linked_in dtype: int32 splits: - name: train num_bytes: 27927641 num_examples: 93239 download_size: 30338277 dataset_size: 27927641 --- # Dataset Card for News Popularity in Multiple Social Media Platforms ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [UCI](https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms) - **Repository:** - **Paper:** [Arxiv](https://arxiv.org/abs/1801.07055) - **Leaderboard:** [Kaggle](https://www.kaggle.com/nikhiljohnk/news-popularity-in-multiple-social-media-platforms/code) - **Point of Contact:** ### Dataset Summary Social sharing data across Facebook, Google+ and LinkedIn for 100k news items on the topics of: economy, microsoft, obama and palestine. ### Supported Tasks and Leaderboards Popularity prediction/shares prediction ### Languages English ## Dataset Structure ### Data Instances ``` { "id": 35873, "title": "Microsoft's 'teen girl' AI turns into a Hitler-loving sex robot within 24 ...", "headline": "Developers at Microsoft created 'Tay', an AI modelled to speak 'like a teen girl', in order to improve the customer service on their voice", "source": "Telegraph.co.uk", "topic": "microsoft", "publish_date": "2016-03-24 09:53:54", "facebook": 22346, "google_plus": 973, "linked_in": 1009 } ``` ### Data Fields - id: the sentence id in the source dataset - title: the title of the link as shared on social media - headline: the headline, or sometimes the lede of the story - source: the source news site - topic: the topic: one of "economy", "microsoft", "obama" and "palestine" - publish_date: the date the original article was published - facebook: the number of Facebook shares, or -1 if this data wasn't collected - google_plus: the number of Google+ likes, or -1 if this data wasn't collected - linked_in: the number of LinkedIn shares, or -1 if if this data wasn't collected ### Data Splits None ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? The source headlines were by journalists, while the titles were written by the people sharing it on social media. ### Annotations #### Annotation process The 'annotations' are simply the number of shares, or likes in the case of Google+ as collected from various API endpoints. #### Who are the annotators? Social media users. ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information License: Creative Commons Attribution 4.0 International License (CC-BY) ### Citation Information ``` @article{Moniz2018MultiSourceSF, title={Multi-Source Social Feedback of Online News Feeds}, author={N. Moniz and L. Torgo}, journal={ArXiv}, year={2018}, volume={abs/1801.07055} } ``` ### Contributions Thanks to [@frankier](https://github.com/frankier) for adding this dataset.
severo/doc-formats-txt-1
--- size_categories: - n<1K --- # [doc] formats - txt - 1 This dataset contains one txt file at the root. It can only contain one column of strings.
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_31_1000
--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 790 num_examples: 32 download_size: 1847 dataset_size: 790 --- # Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_31_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ronec
--- annotations_creators: - expert-generated language_creators: - expert-generated - found language: - ro license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: ronec pretty_name: RONEC dataset_info: features: - name: id dtype: int32 - name: tokens sequence: string - name: ner_ids sequence: int32 - name: space_after sequence: bool - name: ner_tags sequence: class_label: names: '0': O '1': B-PERSON '2': I-PERSON '3': B-ORG '4': I-ORG '5': B-GPE '6': I-GPE '7': B-LOC '8': I-LOC '9': B-NAT_REL_POL '10': I-NAT_REL_POL '11': B-EVENT '12': I-EVENT '13': B-LANGUAGE '14': I-LANGUAGE '15': B-WORK_OF_ART '16': I-WORK_OF_ART '17': B-DATETIME '18': I-DATETIME '19': B-PERIOD '20': I-PERIOD '21': B-MONEY '22': I-MONEY '23': B-QUANTITY '24': I-QUANTITY '25': B-NUMERIC '26': I-NUMERIC '27': B-ORDINAL '28': I-ORDINAL '29': B-FACILITY '30': I-FACILITY config_name: ronec splits: - name: train num_bytes: 8701577 num_examples: 9000 - name: validation num_bytes: 1266490 num_examples: 1330 - name: test num_bytes: 1902224 num_examples: 2000 download_size: 14675943 dataset_size: 11870291 --- # Dataset Card for RONEC ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/dumitrescustefan/ronec - **Repository:** https://github.com/dumitrescustefan/ronec - **Paper:** https://arxiv.org/abs/1909.01247 - **Leaderboard:** https://lirobenchmark.github.io/ - **Point of Contact:** [Stefan](dumitrescu.stefan@gmail.com) and [Andrei-Marius](avram.andreimarius@gmail.com) ### Dataset Summary RONEC, at version 2.0, holds 12330 sentences with over 0.5M tokens, annotated with 15 classes, to a total of 80.283 distinctly annotated entities. The corpus has the following classes and distribution in the train/valid/test splits: | Classes | Total | Train | | Valid | | Test | | |------------- |:------: |:------: |:-------: |:------: |:-------: |:------: |:-------: | | | # | # | % | # | % | # | % | | PERSON | **26130** | 19167 | 73.35 | 2733 | 10.46 | 4230 | 16.19 | | GPE | **11103** | 8193 | 73.79 | 1182 | 10.65 | 1728 | 15.56 | | LOC | **2467** | 1824 | 73.94 | 270 | 10.94 | 373 | 15.12 | | ORG | **7880** | 5688 | 72.18 | 880 | 11.17 | 1312 | 16.65 | | LANGUAGE | **467** | 342 | 73.23 | 52 | 11.13 | 73 | 15.63 | | NAT_REL_POL | **4970** | 3673 | 73.90 | 516 | 10.38 | 781 | 15.71 | | DATETIME | **9614** | 6960 | 72.39 | 1029 | 10.7 | 1625 | 16.9 | | PERIOD | **1188** | 862 | 72.56 | 129 | 10.86 | 197 | 16.58 | | QUANTITY | **1588** | 1161 | 73.11 | 181 | 11.4 | 246 | 15.49 | | MONEY | **1424** | 1041 | 73.10 | 159 | 11.17 | 224 | 15.73 | | NUMERIC | **7735** | 5734 | 74.13 | 814 | 10.52 | 1187 | 15.35 | | ORDINAL | **1893** | 1377 | 72.74 | 212 | 11.2 | 304 | 16.06 | | FACILITY | **1126** | 840 | 74.6 | 113 | 10.04 | 173 | 15.36 | | WORK_OF_ART | **1596** | 1157 | 72.49 | 176 | 11.03 | 263 | 16.48 | | EVENT | **1102** | 826 | 74.95 | 107 | 9.71 | 169 | 15.34 | ### Supported Tasks and Leaderboards The corpus is meant to train Named Entity Recognition models for the Romanian language. Please see the leaderboard here : [https://lirobenchmark.github.io/](https://lirobenchmark.github.io/) ### Languages RONEC is in Romanian (`ro`) ## Dataset Structure ### Data Instances The dataset is a list of instances. For example, an instance looks like: ```json { "id": 10454, "tokens": ["Pentru", "a", "vizita", "locația", "care", "va", "fi", "pusă", "la", "dispoziția", "reprezentanților", "consiliilor", "județene", ",", "o", "delegație", "a", "U.N.C.J.R.", ",", "din", "care", "a", "făcut", "parte", "și", "dl", "Constantin", "Ostaficiuc", ",", "președintele", "C.J.T.", ",", "a", "fost", "prezentă", "la", "Bruxelles", ",", "între", "1-3", "martie", "."], "ner_tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "O", "O", "O", "O", "O", "O", "B-ORG", "O", "O", "O", "O", "O", "O", "O", "B-PERSON", "I-PERSON", "I-PERSON", "I-PERSON", "I-PERSON", "B-ORG", "O", "O", "O", "O", "O", "B-GPE", "O", "B-PERIOD", "I-PERIOD", "I-PERIOD", "O"], "ner_ids": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 3, 0, 0, 0, 0, 0, 5, 0, 19, 20, 20, 0], "space_after": [true, true, true, true, true, true, true, true, true, true, true, true, false, true, true, true, true, false, true, true, true, true, true, true, true, true, true, false, true, true, false, true, true, true, true, true, false, true, true, true, false, false] } ``` ### Data Fields The fields of each examples are: - ``tokens`` are the words of the sentence. - ``ner_tags`` are the string tags assigned to each token, following the BIO2 format. For example, the span ``"între", "1-3", "martie"`` has three tokens, but is a single class ``PERIOD``, marked as ``"B-PERIOD", "I-PERIOD", "I-PERIOD"``. - ``ner_ids`` are the integer encoding of each tag, to be compatible with the standard and to be quickly used for model training. Note that each ``B``-starting tag is odd, and each ``I``-starting tag is even. - ``space_after`` is used to help if there is a need to detokenize the dataset. A ``true`` value means that there is a space after the token on that respective position. ### Data Splits The dataset is split in train: 9000 sentences, dev: 1330 sentence and test: 2000 sentences. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data *The corpus data source represents sentences that are free of copyright, taken from older datasets like the freely available SEETimes and more recent datasources like the Romanian Wikipedia or the Common Crawl.* #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations The corpus was annotated with the following classes: 1. PERSON - proper nouns, including common nouns or pronouns if they refer to a person. (e.g. 'sister') 2. GPE - geo political entity, like a city or a country; has to have a governance form 3. LOC - location, like a sea, continent, region, road, address, etc. 4. ORG - organization 5. LANGUAGE - language (e.g. Romanian, French, etc.) 6. NAT_REL_POL - national, religious or political organizations 7. DATETIME - a time and date in any format, including references to time (e.g. 'yesterday') 8. PERIOD - a period that is precisely bounded by two date times 9. QUANTITY - a quantity that is not numerical; it has a unit of measure 10. MONEY - a monetary value, numeric or otherwise 11. NUMERIC - a simple numeric value, represented as digits or words 12. ORDINAL - an ordinal value like 'first', 'third', etc. 13. FACILITY - a named place that is easily recognizable 14. WORK_OF_ART - a work of art like a named TV show, painting, etc. 15. EVENT - a named recognizable or periodic major event #### Annotation process The corpus was annotated by 3 language experts, and was cross-checked for annotation consistency. The annotation took several months to complete, but the result is a high quality dataset. #### Who are the annotators? Stefan Dumitrescu (lead). ### Personal and Sensitive Information All the source data is already freely downloadable and usable online, so there are no privacy concerns. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information MIT License ### Citation Information ```bibtex @article{dumitrescu2019introducing, title={Introducing RONEC--the Romanian Named Entity Corpus}, author={Dumitrescu, Stefan Daniel and Avram, Andrei-Marius}, journal={arXiv preprint arXiv:1909.01247}, year={2019} } ``` ### Contributions Thanks to [@iliemihai](https://github.com/iliemihai) for adding v1.0 of the dataset.
thanhduycao/data_soict_train_synthesis_entity
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: id dtype: string - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: sentence_norm dtype: string splits: - name: train num_bytes: 6498333095 num_examples: 18312 - name: test num_bytes: 389981876 num_examples: 748 download_size: 1639149838 dataset_size: 6888314971 --- # Dataset Card for "data_soict_train_synthesis_entity" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chillguypoonawala/temp123
--- license: mit ---
dinhquangson/FUNSD_RE
--- license: mit task_categories: - token-classification ---
IlyaGusev/ru_turbo_alpaca
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: alternative_output dtype: string - name: label dtype: string - name: all_labels sequence: string - name: agreement dtype: float32 - name: overlap dtype: uint32 splits: - name: train num_bytes: 54774775 num_examples: 29822 download_size: 14565995 dataset_size: 54774775 license: cc-by-4.0 task_categories: - text-generation - text2text-generation language: - ru tags: - instruction-finetuning - instruction generation - alpaca size_categories: - 10K<n<100K --- # RuTurboAlpaca Dataset of ChatGPT-generated instructions in Russian. <img src="https://cdn.midjourney.com/770a35fa-00c0-4214-bb88-727dbc7cfaf3/0_0.png" > * Code: [rulm/self_instruct](https://github.com/IlyaGusev/rulm/tree/master/self_instruct) * Code is based on [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [self-instruct](https://github.com/yizhongw/self-instruct/). * 29822 examples Preliminary evaluation by an expert based on 400 samples: * 83% of samples contain correct instructions * 63% of samples have correct instructions and outputs Crowdsouring-based evaluation on 3500 samples: * 90% of samples contain correct instructions * 68% of samples have correct instructions and outputs Prompt template: ``` Составь набор из {{num_tasks}} разных заданий для дообучения языковой модели: 1. Делай задания максимально непохожими друг на друга: по типу, по запрашиваемым действиям, по формулировке, по наличию входа. 2. Задания должны быть выполнимы языковой моделью, которая не умеет работать с картинками, видео, и аудио, и не имеет доступа ко внешнему миру. 3. Используй хороший грамотный русский язык. 4. Делай задания в одно или два предложения. 5. Генерируй подходящие реалистичные входные данные, не используй общие шаблоны типа \"Имя человека\" или [имя] вместо реального имени. 6. Задание может быть без входных данных, в таком случае используй токен <noinput> вместо них. 7. На выходе сгенерируй подходящий длинный ответ. 8. Следуй тому же шаблону, который приведен в примерах, разделяй задания с помощью ###. Это важно! Примеры заданий: {% for task in example_tasks %} {{task.index}}. Задание: {{task.instruction}} {{task.index}}. Вход: {{task.input}} {{task.index}}. Выход: {{task.output}} {{ "###" if not loop.last else "" }} {% endfor %} ``` ## Legal disclaimer Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit for us developing models that compete with OpenAI. Not for you.
aladaf/homo-silicus-unboxing-mistral-instruct
--- license: apache-2.0 ---
sc890/DEEPFRUlT_DATASET
--- language: - en license: apache-2.0 size_categories: - 100M<n<1B task_categories: - feature-extraction - text-classification tags: - biomedical - imaging - computer vision - tuberculosis - multimodal dataset_info: features: - name: image_name dtype: string - name: image_id dtype: string - name: number dtype: string - name: image_path dtype: string - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 1229202 num_examples: 10689 - name: test num_bytes: 306617 num_examples: 2694 download_size: 42809832 dataset_size: 70088819.588 configs: - config_name: default data_files: - split: train path: data/train-data-* - split: test path: data/test-data-* --- # DeepFruit Dataset <!--The dataset is from Mendeley, comprises 21,122 images of 20 diverse fruit types across 8 different combinations and 2 csv files. --> ## Dataset Details This dataset contains total of 21,122 fully labeled images, featuring 20 different kinds of fruits. It is structured into an 80% training set (16,899 images) and a 20% testing set (4,223 images), facilitating a ready-to-use framework for model training and evaluation. Additionally, there are two CSV files that label the types of fruits depicted in each image. ### Dataset Description The "DeepFruit" dataset is a comprehensive collection designed for the advancement of research in fruit detection, recognition, and classification. It encompasses a wide array of applications, including but not limited to, fruit recognition systems and calorie estimation. A total of 21,122 fully labeled images, featuring 20 different kinds of fruits. It is structured into an 80% training set (16,899 images) and a 20% testing set (4,223 images), facilitating a ready-to-use framework for model training and evaluation. This dataset provides a valuable resource for researchers aiming to develop automated systems leveraging deep learning, computer vision, and machine learning techniques for fruit image analysis. - **Language(s):** en - **License:** Mendeley License: CC BY 4.0 ### Dataset Sources Data: https://data.mendeley.com/datasets/5prc54r4rt/1 Paper: https://www.sciencedirect.com/science/article/pii/S2352340923006248#sec0003 ## Uses Convert Fruit Dataset From Image to PIL. ### Direct Use This section describes suitable use cases for the dataset. ## Dataset Structure "Train" & "Test": Datasets "image_id": datasets.Value("string") "number" - folder number:datasets.Value("int32") "image": datasets.Image() "image_path": datasets.Value("string") "label": datasets.Value("string") ### Curation Rationale It lies in its foundational role for enabling advanced machine learning applications in dietary and health management. By converting fruit images to the PIL format, it prepares data for analysis that could lead to innovations in recognizing and understanding fruit characteristics. This groundwork is crucial for developing technologies that assist in dietary planning, nutritional education, and managing health conditions through better food choices, thereby having a broad positive effect on public health and awareness. #### Data Collection and Processing Image Format: All images are expected to be in JPEG format. Non-JPEG files are excluded during the data processing phase, ensuring consistency in file format. Label Extraction: Labels are extracted from separate CSV files (Labels_Train.csv and Labels_Test.csv), which map image names to their corresponding fruit labels. This method ensures that labels are organized and accessible. Data Splitting: The dataset is split into training and testing sets, as indicated by the separate ZIP files for train and test data. This standard practice facilitates the evaluation of model performance on unseen data. Python Imaging Library (PIL): Used for opening and manipulating images in the Python Imaging Library format. This choice is made for its wide adoption and ease of integration with other Python libraries for data science and machine learning tasks. Datasets Library from Hugging Face: Facilitates the creation, distribution, and loading of the dataset. This library provides a standardized way to work with datasets, including features for splitting, processing, and accessing dataset information. #### Supported Tasks The fruit images were captured under various conditions, including different plate sizes, shapes, and situations, as well as varying angles, brightness levels, and distances. 1. Foundation for Advanced ML Models/ Algorithms Training: By converting the fruit dataset into PIL format, we ensure that the data is in a uniform, accessible format that is compatible with various machine learning and deep learning libraries. This standardization is vital for the efficient training, validation, and testing of different classification models. 2. Enables Comprehensive Analysis: The dataset, featuring a wide variety of fruit images, is essential for developing a deep understanding of fruit characteristics. This includes not only basic identification but also detailed analyses such as sugar content, calorie count, and vitamin composition, which are crucial for dietary planning and health management. 3. Basis for Practical Applications: The dataset's conversion and subsequent use in machine learning model training are not academic exercises but are intended for real-world applications. The insights gained from this project could significantly impact dietary planning, particularly for individuals with specific health considerations like diabetes, by providing accurate, detailed information about fruit characteristics. ## Bias, Risks, and Limitations Representation Bias: Given the dataset comprises 20 diverse fruit types across 8 combinations, there might be an underrepresentation of certain fruits, particularly those that are less common or indigenous to specific regions. This could lead to a model trained on this dataset performing less accurately on fruit types or varieties not included or underrepresented. Misclassification Risk: In critical applications where accurate fruit identification is crucial (e.g., dietary management apps, agricultural sorting mechanisms), misclassification could lead to adverse outcomes. This risk is heightened if the dataset contains mislabeled examples or if the model struggles with fruits that have similar appearances. Scope of Application: The dataset's utility is primarily confined to the domain of fruit recognition and classification. It may not be suitable for more nuanced tasks within agricultural technology, such as detecting fruit diseases or assessing ripeness, unless supplemented with additional, specialized data.
da2-52000720/vec-seed
--- dataset_info: features: - name: syllable dtype: string - name: wrong dtype: string - name: correct dtype: string splits: - name: seed0 num_bytes: 907640.0263149611 num_examples: 31289 - name: seed1 num_bytes: 12758155 num_examples: 436392 - name: seed0_filtered num_bytes: 263339.9336508038 num_examples: 9092 - name: seed1_filtered num_bytes: 3312328.0105730626 num_examples: 113298 - name: seed1_1 num_bytes: 13386857 num_examples: 459920 - name: seed_filtered num_bytes: 3282350 num_examples: 117816 download_size: 43197420 dataset_size: 33910669.970538825 configs: - config_name: default data_files: - split: seed0 path: data/seed0-* - split: seed1 path: data/seed1-* - split: seed0_filtered path: data/seed0_filtered-* - split: seed1_filtered path: data/seed1_filtered-* - split: seed_filtered path: data/seed_filtered-* - split: seed1_1 path: data/seed1_1-* ---
open-llm-leaderboard/details_TheBloke__gpt4-x-vicuna-13B-HF
--- pretty_name: Evaluation run of TheBloke/gpt4-x-vicuna-13B-HF dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/gpt4-x-vicuna-13B-HF](https://huggingface.co/TheBloke/gpt4-x-vicuna-13B-HF)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__gpt4-x-vicuna-13B-HF\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-07-19T19:01:51.030763](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-x-vicuna-13B-HF/blob/main/results_2023-07-19T19%3A01%3A51.030763.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\": 0.5137597162733054,\n\ \ \"acc_stderr\": 0.03484317305077308,\n \"acc_norm\": 0.5174954549900392,\n\ \ \"acc_norm_stderr\": 0.03482742951911445,\n \"mc1\": 0.3635250917992656,\n\ \ \"mc1_stderr\": 0.016838862883965827,\n \"mc2\": 0.5357942440986606,\n\ \ \"mc2_stderr\": 0.015916184024373756\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5110921501706485,\n \"acc_stderr\": 0.01460779491401305,\n\ \ \"acc_norm\": 0.5341296928327645,\n \"acc_norm_stderr\": 0.014577311315231104\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6038637721569409,\n\ \ \"acc_stderr\": 0.004880937933163287,\n \"acc_norm\": 0.8012348137821151,\n\ \ \"acc_norm_stderr\": 0.003982553164086259\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\ \ \"acc_stderr\": 0.04304979692464243,\n \"acc_norm\": 0.45925925925925926,\n\ \ \"acc_norm_stderr\": 0.04304979692464243\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.506578947368421,\n \"acc_stderr\": 0.040685900502249704,\n\ \ \"acc_norm\": 0.506578947368421,\n \"acc_norm_stderr\": 0.040685900502249704\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.4867924528301887,\n \"acc_stderr\": 0.030762134874500482,\n\ \ \"acc_norm\": 0.4867924528301887,\n \"acc_norm_stderr\": 0.030762134874500482\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5486111111111112,\n\ \ \"acc_stderr\": 0.04161402398403279,\n \"acc_norm\": 0.5486111111111112,\n\ \ \"acc_norm_stderr\": 0.04161402398403279\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"\ acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\ : 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4046242774566474,\n\ \ \"acc_stderr\": 0.03742461193887249,\n \"acc_norm\": 0.4046242774566474,\n\ \ \"acc_norm_stderr\": 0.03742461193887249\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n\ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.3617021276595745,\n \"acc_stderr\": 0.03141082197596241,\n\ \ \"acc_norm\": 0.3617021276595745,\n \"acc_norm_stderr\": 0.03141082197596241\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\ \ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\ \ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.28835978835978837,\n \"acc_stderr\": 0.023330654054535896,\n \"\ acc_norm\": 0.28835978835978837,\n \"acc_norm_stderr\": 0.023330654054535896\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\": 0.31,\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\": 0.5741935483870968,\n\ \ \"acc_stderr\": 0.028129112709165894,\n \"acc_norm\": 0.5741935483870968,\n\ \ \"acc_norm_stderr\": 0.028129112709165894\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3891625615763547,\n \"acc_stderr\": 0.03430462416103873,\n\ \ \"acc_norm\": 0.3891625615763547,\n \"acc_norm_stderr\": 0.03430462416103873\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.037425970438065864,\n\ \ \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.037425970438065864\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6363636363636364,\n \"acc_stderr\": 0.034273086529999344,\n \"\ acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.034273086529999344\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7046632124352331,\n \"acc_stderr\": 0.03292296639155141,\n\ \ \"acc_norm\": 0.7046632124352331,\n \"acc_norm_stderr\": 0.03292296639155141\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.43846153846153846,\n \"acc_stderr\": 0.02515826601686857,\n\ \ \"acc_norm\": 0.43846153846153846,\n \"acc_norm_stderr\": 0.02515826601686857\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.23703703703703705,\n \"acc_stderr\": 0.025928876132766135,\n \ \ \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.025928876132766135\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.0322529423239964,\n \ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.0322529423239964\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6770642201834862,\n \"acc_stderr\": 0.02004811592341531,\n \"\ acc_norm\": 0.6770642201834862,\n \"acc_norm_stderr\": 0.02004811592341531\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.36574074074074076,\n \"acc_stderr\": 0.03284738857647206,\n \"\ acc_norm\": 0.36574074074074076,\n \"acc_norm_stderr\": 0.03284738857647206\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.6715686274509803,\n \"acc_stderr\": 0.03296245110172228,\n \"\ acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.03296245110172228\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7046413502109705,\n \"acc_stderr\": 0.02969633871342288,\n \ \ \"acc_norm\": 0.7046413502109705,\n \"acc_norm_stderr\": 0.02969633871342288\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5739910313901345,\n\ \ \"acc_stderr\": 0.03318833286217281,\n \"acc_norm\": 0.5739910313901345,\n\ \ \"acc_norm_stderr\": 0.03318833286217281\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.04118438565806298,\n\ \ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.04118438565806298\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6776859504132231,\n \"acc_stderr\": 0.042664163633521685,\n \"\ acc_norm\": 0.6776859504132231,\n \"acc_norm_stderr\": 0.042664163633521685\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6574074074074074,\n\ \ \"acc_stderr\": 0.045879047413018105,\n \"acc_norm\": 0.6574074074074074,\n\ \ \"acc_norm_stderr\": 0.045879047413018105\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6625766871165644,\n \"acc_stderr\": 0.03714908409935574,\n\ \ \"acc_norm\": 0.6625766871165644,\n \"acc_norm_stderr\": 0.03714908409935574\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6796116504854369,\n \"acc_stderr\": 0.04620284082280041,\n\ \ \"acc_norm\": 0.6796116504854369,\n \"acc_norm_stderr\": 0.04620284082280041\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7735042735042735,\n\ \ \"acc_stderr\": 0.027421007295392912,\n \"acc_norm\": 0.7735042735042735,\n\ \ \"acc_norm_stderr\": 0.027421007295392912\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6871008939974457,\n\ \ \"acc_stderr\": 0.01658093594030406,\n \"acc_norm\": 0.6871008939974457,\n\ \ \"acc_norm_stderr\": 0.01658093594030406\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5375722543352601,\n \"acc_stderr\": 0.026842985519615375,\n\ \ \"acc_norm\": 0.5375722543352601,\n \"acc_norm_stderr\": 0.026842985519615375\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31731843575418994,\n\ \ \"acc_stderr\": 0.01556639263005703,\n \"acc_norm\": 0.31731843575418994,\n\ \ \"acc_norm_stderr\": 0.01556639263005703\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5424836601307189,\n \"acc_stderr\": 0.028526383452142638,\n\ \ \"acc_norm\": 0.5424836601307189,\n \"acc_norm_stderr\": 0.028526383452142638\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5530546623794212,\n\ \ \"acc_stderr\": 0.02823776942208535,\n \"acc_norm\": 0.5530546623794212,\n\ \ \"acc_norm_stderr\": 0.02823776942208535\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5740740740740741,\n \"acc_stderr\": 0.027513747284379424,\n\ \ \"acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.027513747284379424\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.38652482269503546,\n \"acc_stderr\": 0.02904919034254346,\n \ \ \"acc_norm\": 0.38652482269503546,\n \"acc_norm_stderr\": 0.02904919034254346\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41264667535853977,\n\ \ \"acc_stderr\": 0.012573836633799015,\n \"acc_norm\": 0.41264667535853977,\n\ \ \"acc_norm_stderr\": 0.012573836633799015\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.44485294117647056,\n \"acc_stderr\": 0.03018753206032939,\n\ \ \"acc_norm\": 0.44485294117647056,\n \"acc_norm_stderr\": 0.03018753206032939\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5196078431372549,\n \"acc_stderr\": 0.020212274976302957,\n \ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.020212274976302957\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5454545454545454,\n\ \ \"acc_stderr\": 0.04769300568972743,\n \"acc_norm\": 0.5454545454545454,\n\ \ \"acc_norm_stderr\": 0.04769300568972743\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5795918367346938,\n \"acc_stderr\": 0.03160106993449601,\n\ \ \"acc_norm\": 0.5795918367346938,\n \"acc_norm_stderr\": 0.03160106993449601\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7412935323383084,\n\ \ \"acc_stderr\": 0.030965903123573033,\n \"acc_norm\": 0.7412935323383084,\n\ \ \"acc_norm_stderr\": 0.030965903123573033\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\ \ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3635250917992656,\n\ \ \"mc1_stderr\": 0.016838862883965827,\n \"mc2\": 0.5357942440986606,\n\ \ \"mc2_stderr\": 0.015916184024373756\n }\n}\n```" repo_url: https://huggingface.co/TheBloke/gpt4-x-vicuna-13B-HF leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|arc:challenge|25_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hellaswag|10_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:01:51.030763.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T19:01:51.030763.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T19_01_51.030763 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T19:01:51.030763.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T19:01:51.030763.parquet' - config_name: results data_files: - split: 2023_07_19T19_01_51.030763 path: - results_2023-07-19T19:01:51.030763.parquet - split: latest path: - results_2023-07-19T19:01:51.030763.parquet --- # Dataset Card for Evaluation run of TheBloke/gpt4-x-vicuna-13B-HF ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/gpt4-x-vicuna-13B-HF - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TheBloke/gpt4-x-vicuna-13B-HF](https://huggingface.co/TheBloke/gpt4-x-vicuna-13B-HF) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TheBloke__gpt4-x-vicuna-13B-HF", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-07-19T19:01:51.030763](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-x-vicuna-13B-HF/blob/main/results_2023-07-19T19%3A01%3A51.030763.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": 0.5137597162733054, "acc_stderr": 0.03484317305077308, "acc_norm": 0.5174954549900392, "acc_norm_stderr": 0.03482742951911445, "mc1": 0.3635250917992656, "mc1_stderr": 0.016838862883965827, "mc2": 0.5357942440986606, "mc2_stderr": 0.015916184024373756 }, "harness|arc:challenge|25": { "acc": 0.5110921501706485, "acc_stderr": 0.01460779491401305, "acc_norm": 0.5341296928327645, "acc_norm_stderr": 0.014577311315231104 }, "harness|hellaswag|10": { "acc": 0.6038637721569409, "acc_stderr": 0.004880937933163287, "acc_norm": 0.8012348137821151, "acc_norm_stderr": 0.003982553164086259 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.45925925925925926, "acc_stderr": 0.04304979692464243, "acc_norm": 0.45925925925925926, "acc_norm_stderr": 0.04304979692464243 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.506578947368421, "acc_stderr": 0.040685900502249704, "acc_norm": 0.506578947368421, "acc_norm_stderr": 0.040685900502249704 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4867924528301887, "acc_stderr": 0.030762134874500482, "acc_norm": 0.4867924528301887, "acc_norm_stderr": 0.030762134874500482 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5486111111111112, "acc_stderr": 0.04161402398403279, "acc_norm": 0.5486111111111112, "acc_norm_stderr": 0.04161402398403279 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4046242774566474, "acc_stderr": 0.03742461193887249, "acc_norm": 0.4046242774566474, "acc_norm_stderr": 0.03742461193887249 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.20588235294117646, "acc_stderr": 0.04023382273617747, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.04023382273617747 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3617021276595745, "acc_stderr": 0.03141082197596241, "acc_norm": 0.3617021276595745, "acc_norm_stderr": 0.03141082197596241 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159394, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159394 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.28835978835978837, "acc_stderr": 0.023330654054535896, "acc_norm": 0.28835978835978837, "acc_norm_stderr": 0.023330654054535896 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "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": 0.5741935483870968, "acc_stderr": 0.028129112709165894, "acc_norm": 0.5741935483870968, "acc_norm_stderr": 0.028129112709165894 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3891625615763547, "acc_stderr": 0.03430462416103873, "acc_norm": 0.3891625615763547, "acc_norm_stderr": 0.03430462416103873 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6424242424242425, "acc_stderr": 0.037425970438065864, "acc_norm": 0.6424242424242425, "acc_norm_stderr": 0.037425970438065864 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6363636363636364, "acc_stderr": 0.034273086529999344, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.034273086529999344 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7046632124352331, "acc_stderr": 0.03292296639155141, "acc_norm": 0.7046632124352331, "acc_norm_stderr": 0.03292296639155141 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.43846153846153846, "acc_stderr": 0.02515826601686857, "acc_norm": 0.43846153846153846, "acc_norm_stderr": 0.02515826601686857 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.23703703703703705, "acc_stderr": 0.025928876132766135, "acc_norm": 0.23703703703703705, "acc_norm_stderr": 0.025928876132766135 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.0322529423239964, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.0322529423239964 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6770642201834862, "acc_stderr": 0.02004811592341531, "acc_norm": 0.6770642201834862, "acc_norm_stderr": 0.02004811592341531 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.36574074074074076, "acc_stderr": 0.03284738857647206, "acc_norm": 0.36574074074074076, "acc_norm_stderr": 0.03284738857647206 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6715686274509803, "acc_stderr": 0.03296245110172228, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.03296245110172228 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7046413502109705, "acc_stderr": 0.02969633871342288, "acc_norm": 0.7046413502109705, "acc_norm_stderr": 0.02969633871342288 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5739910313901345, "acc_stderr": 0.03318833286217281, "acc_norm": 0.5739910313901345, "acc_norm_stderr": 0.03318833286217281 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6717557251908397, "acc_stderr": 0.04118438565806298, "acc_norm": 0.6717557251908397, "acc_norm_stderr": 0.04118438565806298 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6776859504132231, "acc_stderr": 0.042664163633521685, "acc_norm": 0.6776859504132231, "acc_norm_stderr": 0.042664163633521685 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6574074074074074, "acc_stderr": 0.045879047413018105, "acc_norm": 0.6574074074074074, "acc_norm_stderr": 0.045879047413018105 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6625766871165644, "acc_stderr": 0.03714908409935574, "acc_norm": 0.6625766871165644, "acc_norm_stderr": 0.03714908409935574 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.39285714285714285, "acc_stderr": 0.04635550135609976, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.04635550135609976 }, "harness|hendrycksTest-management|5": { "acc": 0.6796116504854369, "acc_stderr": 0.04620284082280041, "acc_norm": 0.6796116504854369, "acc_norm_stderr": 0.04620284082280041 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7735042735042735, "acc_stderr": 0.027421007295392912, "acc_norm": 0.7735042735042735, "acc_norm_stderr": 0.027421007295392912 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6871008939974457, "acc_stderr": 0.01658093594030406, "acc_norm": 0.6871008939974457, "acc_norm_stderr": 0.01658093594030406 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5375722543352601, "acc_stderr": 0.026842985519615375, "acc_norm": 0.5375722543352601, "acc_norm_stderr": 0.026842985519615375 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.31731843575418994, "acc_stderr": 0.01556639263005703, "acc_norm": 0.31731843575418994, "acc_norm_stderr": 0.01556639263005703 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5424836601307189, "acc_stderr": 0.028526383452142638, "acc_norm": 0.5424836601307189, "acc_norm_stderr": 0.028526383452142638 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5530546623794212, "acc_stderr": 0.02823776942208535, "acc_norm": 0.5530546623794212, "acc_norm_stderr": 0.02823776942208535 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5740740740740741, "acc_stderr": 0.027513747284379424, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.027513747284379424 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.38652482269503546, "acc_stderr": 0.02904919034254346, "acc_norm": 0.38652482269503546, "acc_norm_stderr": 0.02904919034254346 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.41264667535853977, "acc_stderr": 0.012573836633799015, "acc_norm": 0.41264667535853977, "acc_norm_stderr": 0.012573836633799015 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.44485294117647056, "acc_stderr": 0.03018753206032939, "acc_norm": 0.44485294117647056, "acc_norm_stderr": 0.03018753206032939 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5196078431372549, "acc_stderr": 0.020212274976302957, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.020212274976302957 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5454545454545454, "acc_stderr": 0.04769300568972743, "acc_norm": 0.5454545454545454, "acc_norm_stderr": 0.04769300568972743 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5795918367346938, "acc_stderr": 0.03160106993449601, "acc_norm": 0.5795918367346938, "acc_norm_stderr": 0.03160106993449601 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7412935323383084, "acc_stderr": 0.030965903123573033, "acc_norm": 0.7412935323383084, "acc_norm_stderr": 0.030965903123573033 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-virology|5": { "acc": 0.45180722891566266, "acc_stderr": 0.03874371556587953, "acc_norm": 0.45180722891566266, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7426900584795322, "acc_stderr": 0.03352799844161865, "acc_norm": 0.7426900584795322, "acc_norm_stderr": 0.03352799844161865 }, "harness|truthfulqa:mc|0": { "mc1": 0.3635250917992656, "mc1_stderr": 0.016838862883965827, "mc2": 0.5357942440986606, "mc2_stderr": 0.015916184024373756 } } ``` ### 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]
galman33/gal_yair_166000_256x256_fixed
--- dataset_info: features: - name: lat dtype: float64 - name: lon dtype: float64 - name: country_code dtype: class_label: names: '0': ad '1': ae '2': al '3': aq '4': ar '5': au '6': bd '7': be '8': bg '9': bm '10': bo '11': br '12': bt '13': bw '14': ca '15': ch '16': cl '17': co '18': cz '19': de '20': dk '21': ec '22': ee '23': es '24': fi '25': fr '26': gb '27': gh '28': gl '29': gr '30': gt '31': hk '32': hr '33': hu '34': id '35': ie '36': il '37': is '38': it '39': ix '40': jp '41': kg '42': kh '43': kr '44': la '45': lk '46': ls '47': lt '48': lu '49': lv '50': me '51': mg '52': mk '53': mn '54': mo '55': mt '56': mx '57': my '58': nl '59': 'no' '60': nz '61': pe '62': ph '63': pl '64': pt '65': ro '66': rs '67': ru '68': se '69': sg '70': si '71': sk '72': sn '73': sz '74': th '75': tn '76': tr '77': tw '78': ua '79': ug '80': us '81': uy '82': za - name: image dtype: image splits: - name: train num_bytes: 16156275005.0 num_examples: 166000 download_size: 16115168331 dataset_size: 16156275005.0 --- # Dataset Card for "gal_yair_166000_256x256_fixed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/olivia_asobiasobase
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Olivia This is the dataset of Olivia, containing 300 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)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 641 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 641 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 641 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 641 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
rwitz2/no_robots_formatted
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: category dtype: string - name: text dtype: string splits: - name: train num_bytes: 28805395 num_examples: 9500 - name: test num_bytes: 1545168 num_examples: 500 download_size: 18891461 dataset_size: 30350563 --- # Dataset Card for "no_robots_formatted" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Lilsunx/sih
--- license: openrail ---
open-llm-leaderboard/details_azarafrooz__mistral-v2-7b-selfplay-low-tmp
--- pretty_name: Evaluation run of azarafrooz/mistral-v2-7b-selfplay-low-tmp dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [azarafrooz/mistral-v2-7b-selfplay-low-tmp](https://huggingface.co/azarafrooz/mistral-v2-7b-selfplay-low-tmp)\ \ 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_azarafrooz__mistral-v2-7b-selfplay-low-tmp\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T11:31:23.339994](https://huggingface.co/datasets/open-llm-leaderboard/details_azarafrooz__mistral-v2-7b-selfplay-low-tmp/blob/main/results_2024-03-21T11-31-23.339994.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\": 0.6075157900064023,\n\ \ \"acc_stderr\": 0.0331399850758573,\n \"acc_norm\": 0.6121293596581681,\n\ \ \"acc_norm_stderr\": 0.03381162626787054,\n \"mc1\": 0.5287637698898409,\n\ \ \"mc1_stderr\": 0.017474513848525518,\n \"mc2\": 0.6813244751586996,\n\ \ \"mc2_stderr\": 0.015204757863568796\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5861774744027304,\n \"acc_stderr\": 0.014392730009221005,\n\ \ \"acc_norm\": 0.6305460750853242,\n \"acc_norm_stderr\": 0.014104578366491888\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6670981876120294,\n\ \ \"acc_stderr\": 0.004702886273189419,\n \"acc_norm\": 0.849133638717387,\n\ \ \"acc_norm_stderr\": 0.0035718708487317116\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\ \ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \ \ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6716981132075471,\n \"acc_stderr\": 0.02890159361241178,\n\ \ \"acc_norm\": 0.6716981132075471,\n \"acc_norm_stderr\": 0.02890159361241178\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6875,\n\ \ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\ \ \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n\ \ \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \ \ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5838150289017341,\n\ \ \"acc_stderr\": 0.03758517775404948,\n \"acc_norm\": 0.5838150289017341,\n\ \ \"acc_norm_stderr\": 0.03758517775404948\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n\ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.04615186962583703,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.04615186962583703\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6137931034482759,\n \"acc_stderr\": 0.04057324734419035,\n\ \ \"acc_norm\": 0.6137931034482759,\n \"acc_norm_stderr\": 0.04057324734419035\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155254,\n \"\ acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155254\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\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.632258064516129,\n\ \ \"acc_stderr\": 0.02743086657997347,\n \"acc_norm\": 0.632258064516129,\n\ \ \"acc_norm_stderr\": 0.02743086657997347\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7626262626262627,\n \"acc_stderr\": 0.030313710538198896,\n \"\ acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.030313710538198896\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306443,\n\ \ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.558974358974359,\n \"acc_stderr\": 0.025174048384000745,\n \ \ \"acc_norm\": 0.558974358974359,\n \"acc_norm_stderr\": 0.025174048384000745\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114993,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114993\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.030778057422931673,\n\ \ \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.030778057422931673\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7944954128440367,\n \"acc_stderr\": 0.01732435232501601,\n \"\ acc_norm\": 0.7944954128440367,\n \"acc_norm_stderr\": 0.01732435232501601\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321616,\n \"\ acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321616\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145624,\n \"\ acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145624\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7552742616033755,\n \"acc_stderr\": 0.027985699387036423,\n \ \ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.027985699387036423\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n\ \ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n\ \ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.038808483010823944,\n\ \ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.038808483010823944\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077785,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077785\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\": 0.7816091954022989,\n\ \ \"acc_stderr\": 0.01477435831993449,\n \"acc_norm\": 0.7816091954022989,\n\ \ \"acc_norm_stderr\": 0.01477435831993449\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\ \ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3139664804469274,\n\ \ \"acc_stderr\": 0.01552192393352364,\n \"acc_norm\": 0.3139664804469274,\n\ \ \"acc_norm_stderr\": 0.01552192393352364\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.026568921015457138,\n\ \ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.026568921015457138\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\ \ \"acc_stderr\": 0.025922371788818777,\n \"acc_norm\": 0.7041800643086816,\n\ \ \"acc_norm_stderr\": 0.025922371788818777\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.02540719779889017,\n\ \ \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.02540719779889017\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \ \ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4322033898305085,\n\ \ \"acc_stderr\": 0.012652297777114968,\n \"acc_norm\": 0.4322033898305085,\n\ \ \"acc_norm_stderr\": 0.012652297777114968\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n\ \ \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6356209150326797,\n \"acc_stderr\": 0.019469518221573705,\n \ \ \"acc_norm\": 0.6356209150326797,\n \"acc_norm_stderr\": 0.019469518221573705\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\": 0.710204081632653,\n \"acc_stderr\": 0.029043088683304328,\n\ \ \"acc_norm\": 0.710204081632653,\n \"acc_norm_stderr\": 0.029043088683304328\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7263681592039801,\n\ \ \"acc_stderr\": 0.031524391865554016,\n \"acc_norm\": 0.7263681592039801,\n\ \ \"acc_norm_stderr\": 0.031524391865554016\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \ \ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\ \ \"acc_stderr\": 0.03892212195333047,\n \"acc_norm\": 0.4939759036144578,\n\ \ \"acc_norm_stderr\": 0.03892212195333047\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5287637698898409,\n\ \ \"mc1_stderr\": 0.017474513848525518,\n \"mc2\": 0.6813244751586996,\n\ \ \"mc2_stderr\": 0.015204757863568796\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.01176414905469834\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3957543593631539,\n \ \ \"acc_stderr\": 0.013469823701048812\n }\n}\n```" repo_url: https://huggingface.co/azarafrooz/mistral-v2-7b-selfplay-low-tmp leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|arc:challenge|25_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T11-31-23.339994.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|gsm8k|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hellaswag|10_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-31-23.339994.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-31-23.339994.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T11-31-23.339994.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T11_31_23.339994 path: - '**/details_harness|winogrande|5_2024-03-21T11-31-23.339994.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T11-31-23.339994.parquet' - config_name: results data_files: - split: 2024_03_21T11_31_23.339994 path: - results_2024-03-21T11-31-23.339994.parquet - split: latest path: - results_2024-03-21T11-31-23.339994.parquet --- # Dataset Card for Evaluation run of azarafrooz/mistral-v2-7b-selfplay-low-tmp <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [azarafrooz/mistral-v2-7b-selfplay-low-tmp](https://huggingface.co/azarafrooz/mistral-v2-7b-selfplay-low-tmp) 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_azarafrooz__mistral-v2-7b-selfplay-low-tmp", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T11:31:23.339994](https://huggingface.co/datasets/open-llm-leaderboard/details_azarafrooz__mistral-v2-7b-selfplay-low-tmp/blob/main/results_2024-03-21T11-31-23.339994.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": 0.6075157900064023, "acc_stderr": 0.0331399850758573, "acc_norm": 0.6121293596581681, "acc_norm_stderr": 0.03381162626787054, "mc1": 0.5287637698898409, "mc1_stderr": 0.017474513848525518, "mc2": 0.6813244751586996, "mc2_stderr": 0.015204757863568796 }, "harness|arc:challenge|25": { "acc": 0.5861774744027304, "acc_stderr": 0.014392730009221005, "acc_norm": 0.6305460750853242, "acc_norm_stderr": 0.014104578366491888 }, "harness|hellaswag|10": { "acc": 0.6670981876120294, "acc_stderr": 0.004702886273189419, "acc_norm": 0.849133638717387, "acc_norm_stderr": 0.0035718708487317116 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6716981132075471, "acc_stderr": 0.02890159361241178, "acc_norm": 0.6716981132075471, "acc_norm_stderr": 0.02890159361241178 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6875, "acc_stderr": 0.038760854559127644, "acc_norm": 0.6875, "acc_norm_stderr": 0.038760854559127644 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5838150289017341, "acc_stderr": 0.03758517775404948, "acc_norm": 0.5838150289017341, "acc_norm_stderr": 0.03758517775404948 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.04615186962583703, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.04615186962583703 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6137931034482759, "acc_stderr": 0.04057324734419035, "acc_norm": 0.6137931034482759, "acc_norm_stderr": 0.04057324734419035 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3783068783068783, "acc_stderr": 0.024976954053155254, "acc_norm": 0.3783068783068783, "acc_norm_stderr": 0.024976954053155254 }, "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": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.632258064516129, "acc_stderr": 0.02743086657997347, "acc_norm": 0.632258064516129, "acc_norm_stderr": 0.02743086657997347 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7626262626262627, "acc_stderr": 0.030313710538198896, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.030313710538198896 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8549222797927462, "acc_stderr": 0.025416343096306443, "acc_norm": 0.8549222797927462, "acc_norm_stderr": 0.025416343096306443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.558974358974359, "acc_stderr": 0.025174048384000745, "acc_norm": 0.558974358974359, "acc_norm_stderr": 0.025174048384000745 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3037037037037037, "acc_stderr": 0.028037929969114993, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.028037929969114993 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6596638655462185, "acc_stderr": 0.030778057422931673, "acc_norm": 0.6596638655462185, "acc_norm_stderr": 0.030778057422931673 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7944954128440367, "acc_stderr": 0.01732435232501601, "acc_norm": 0.7944954128440367, "acc_norm_stderr": 0.01732435232501601 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.44907407407407407, "acc_stderr": 0.03392238405321616, "acc_norm": 0.44907407407407407, "acc_norm_stderr": 0.03392238405321616 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7647058823529411, "acc_stderr": 0.029771775228145624, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.029771775228145624 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7552742616033755, "acc_stderr": 0.027985699387036423, "acc_norm": 0.7552742616033755, "acc_norm_stderr": 0.027985699387036423 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6188340807174888, "acc_stderr": 0.03259625118416827, "acc_norm": 0.6188340807174888, "acc_norm_stderr": 0.03259625118416827 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.732824427480916, "acc_stderr": 0.038808483010823944, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.038808483010823944 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.042365112580946336, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.042365112580946336 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077785, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077785 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7816091954022989, "acc_stderr": 0.01477435831993449, "acc_norm": 0.7816091954022989, "acc_norm_stderr": 0.01477435831993449 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6936416184971098, "acc_stderr": 0.024818350129436593, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.024818350129436593 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3139664804469274, "acc_stderr": 0.01552192393352364, "acc_norm": 0.3139664804469274, "acc_norm_stderr": 0.01552192393352364 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6862745098039216, "acc_stderr": 0.026568921015457138, "acc_norm": 0.6862745098039216, "acc_norm_stderr": 0.026568921015457138 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7041800643086816, "acc_stderr": 0.025922371788818777, "acc_norm": 0.7041800643086816, "acc_norm_stderr": 0.025922371788818777 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7037037037037037, "acc_stderr": 0.02540719779889017, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.02540719779889017 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.450354609929078, "acc_stderr": 0.029680105565029036, "acc_norm": 0.450354609929078, "acc_norm_stderr": 0.029680105565029036 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4322033898305085, "acc_stderr": 0.012652297777114968, "acc_norm": 0.4322033898305085, "acc_norm_stderr": 0.012652297777114968 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6213235294117647, "acc_stderr": 0.02946513363977613, "acc_norm": 0.6213235294117647, "acc_norm_stderr": 0.02946513363977613 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6356209150326797, "acc_stderr": 0.019469518221573705, "acc_norm": 0.6356209150326797, "acc_norm_stderr": 0.019469518221573705 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.710204081632653, "acc_stderr": 0.029043088683304328, "acc_norm": 0.710204081632653, "acc_norm_stderr": 0.029043088683304328 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7263681592039801, "acc_stderr": 0.031524391865554016, "acc_norm": 0.7263681592039801, "acc_norm_stderr": 0.031524391865554016 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.81, "acc_stderr": 0.03942772444036625, "acc_norm": 0.81, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-virology|5": { "acc": 0.4939759036144578, "acc_stderr": 0.03892212195333047, "acc_norm": 0.4939759036144578, "acc_norm_stderr": 0.03892212195333047 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.5287637698898409, "mc1_stderr": 0.017474513848525518, "mc2": 0.6813244751586996, "mc2_stderr": 0.015204757863568796 }, "harness|winogrande|5": { "acc": 0.7734806629834254, "acc_stderr": 0.01176414905469834 }, "harness|gsm8k|5": { "acc": 0.3957543593631539, "acc_stderr": 0.013469823701048812 } } ``` ## 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]
Seanxh/twitter_dataset_1713196951
--- 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: 85739 num_examples: 199 download_size: 34951 dataset_size: 85739 configs: - config_name: default data_files: - split: train path: data/train-* ---
bruraz/teste
--- license: openrail ---
EgilKarlsen/Thunderbird_BERT_FT
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: '0' dtype: float32 - name: '1' dtype: float32 - name: '2' dtype: float32 - name: '3' dtype: float32 - name: '4' dtype: float32 - name: '5' dtype: float32 - name: '6' dtype: float32 - name: '7' dtype: float32 - name: '8' dtype: float32 - name: '9' dtype: float32 - name: '10' dtype: float32 - name: '11' dtype: float32 - name: '12' dtype: float32 - name: '13' dtype: float32 - name: '14' dtype: float32 - name: '15' dtype: float32 - name: '16' dtype: float32 - name: '17' dtype: float32 - name: '18' dtype: float32 - name: '19' dtype: float32 - name: '20' dtype: float32 - name: '21' dtype: float32 - name: '22' dtype: float32 - name: '23' dtype: float32 - name: '24' dtype: float32 - name: '25' dtype: float32 - name: '26' dtype: float32 - name: '27' dtype: float32 - name: '28' dtype: float32 - name: '29' dtype: float32 - name: '30' dtype: float32 - name: '31' dtype: float32 - name: '32' dtype: float32 - name: '33' dtype: float32 - name: '34' dtype: float32 - name: '35' dtype: float32 - name: '36' dtype: float32 - name: '37' dtype: float32 - name: '38' dtype: float32 - name: '39' dtype: float32 - name: '40' dtype: float32 - name: '41' dtype: float32 - name: '42' dtype: float32 - name: '43' dtype: float32 - name: '44' dtype: float32 - name: '45' dtype: float32 - name: '46' dtype: float32 - name: '47' dtype: float32 - name: '48' dtype: float32 - name: '49' dtype: float32 - name: '50' dtype: float32 - name: '51' dtype: float32 - name: '52' dtype: float32 - name: '53' dtype: float32 - name: '54' dtype: float32 - name: '55' dtype: float32 - name: '56' dtype: float32 - name: '57' dtype: float32 - name: '58' dtype: float32 - name: '59' dtype: float32 - name: '60' dtype: float32 - name: '61' dtype: float32 - name: '62' dtype: float32 - name: '63' dtype: float32 - name: '64' dtype: float32 - name: '65' dtype: float32 - name: '66' dtype: float32 - name: '67' dtype: float32 - 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name: '139' dtype: float32 - name: '140' dtype: float32 - name: '141' dtype: float32 - name: '142' dtype: float32 - name: '143' dtype: float32 - name: '144' dtype: float32 - name: '145' dtype: float32 - name: '146' dtype: float32 - name: '147' dtype: float32 - name: '148' dtype: float32 - name: '149' dtype: float32 - name: '150' dtype: float32 - name: '151' dtype: float32 - name: '152' dtype: float32 - name: '153' dtype: float32 - name: '154' dtype: float32 - name: '155' dtype: float32 - name: '156' dtype: float32 - name: '157' dtype: float32 - name: '158' dtype: float32 - name: '159' dtype: float32 - name: '160' dtype: float32 - name: '161' dtype: float32 - name: '162' dtype: float32 - name: '163' dtype: float32 - name: '164' dtype: float32 - name: '165' dtype: float32 - name: '166' dtype: float32 - name: '167' dtype: float32 - name: '168' dtype: float32 - name: '169' dtype: float32 - name: '170' dtype: float32 - name: '171' dtype: float32 - name: '172' dtype: float32 - name: '173' dtype: float32 - 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name: '734' dtype: float32 - name: '735' dtype: float32 - name: '736' dtype: float32 - name: '737' dtype: float32 - name: '738' dtype: float32 - name: '739' dtype: float32 - name: '740' dtype: float32 - name: '741' dtype: float32 - name: '742' dtype: float32 - name: '743' dtype: float32 - name: '744' dtype: float32 - name: '745' dtype: float32 - name: '746' dtype: float32 - name: '747' dtype: float32 - name: '748' dtype: float32 - name: '749' dtype: float32 - name: '750' dtype: float32 - name: '751' dtype: float32 - name: '752' dtype: float32 - name: '753' dtype: float32 - name: '754' dtype: float32 - name: '755' dtype: float32 - name: '756' dtype: float32 - name: '757' dtype: float32 - name: '758' dtype: float32 - name: '759' dtype: float32 - name: '760' dtype: float32 - name: '761' dtype: float32 - name: '762' dtype: float32 - name: '763' dtype: float32 - name: '764' dtype: float32 - name: '765' dtype: float32 - name: '766' dtype: float32 - name: '767' dtype: float32 - name: label dtype: string splits: - name: train num_bytes: 115576722 num_examples: 37500 - name: test num_bytes: 38525585 num_examples: 12500 download_size: 211880915 dataset_size: 154102307 --- # Dataset Card for "Thunderbird_BERT_FT" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-MicPie__QA_bias-v2_TEST-MicPie__QA_bias-v2_TEST-e54ae6-1669159075
--- type: predictions tags: - autotrain - evaluation datasets: - MicPie/QA_bias-v2_TEST eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-1.3b_eval metrics: [] dataset_name: MicPie/QA_bias-v2_TEST dataset_config: MicPie--QA_bias-v2_TEST dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-1.3b_eval * Dataset: MicPie/QA_bias-v2_TEST * Config: MicPie--QA_bias-v2_TEST * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.
Nexdata/Chinese_Young_Children_Speech_Data_by_Mobile_Phone_and_Microphone
--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Chinese_Young_Children_Speech_Data_by_Mobile_Phone_and_Microphone ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** hhttps://www.nexdata.ai/datasets/76?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The data were recorded by 797 Chinese children aged 3 to 5, of whom 39% were children aged 5. The recording content conforms to the characteristics of children, mainly storybooks, children's songs, spoken language. Around 120 sentences for each speaker. It is simultaneously recorded by hi-fi microphone and cellphone. The vaild data are 41.8 hours. Texts are manually transcribed with high accuracy. For more details, please refer to the link: https://www.nexdata.ai/datasets/76?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Mandarin Chinese ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
datasets-examples/doc-formats-tsv-3
--- configs: - config_name: default data_files: "data.tsv" names: ["kind", "sound"] size_categories: - n<1K --- # [doc] formats - tsv - 3 This dataset contains one tsv file at the root: - [data.tsv](./data.tsv) ```tsv dog woof cat meow pokemon pika human hello ``` We define the config name in the YAML config, the file's exact location, and the columns' name. As we provide the `names` option, but not the `header` one, the first row in the file is considered a row of values, not a row of column names. The delimiter is set to `"\t"` (tabulation) due to the file's extension. The reference for the options is the [documentation of pandas.read_csv()](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html). ```yaml --- configs: - config_name: default data_files: "data.tsv" names: ["kind", "sound"] size_categories: - n<1K --- ```
ColinCcz/combined_non_MH_dataset
--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 939124270.6964713 num_examples: 1298192 download_size: 598207611 dataset_size: 939124270.6964713 configs: - config_name: default data_files: - split: train path: data/train-* ---
atmallen/quirky_bookrating_bob_hard
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: float64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 96619.75463773188 num_examples: 718 - name: validation num_bytes: 63544.77 num_examples: 472 - name: test num_bytes: 60446.35725 num_examples: 447 download_size: 75163 dataset_size: 220610.88188773187 --- # Dataset Card for "quirky_bookrating_bob_hard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
atiranela/SubaruNatsuki
--- license: openrail ---
gaurav-mac/dolly-databricks-mbrt
--- license: cc-by-sa-3.0 ---
joey234/mmlu-high_school_macroeconomics-neg-answer
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_answer dtype: string splits: - name: test num_bytes: 137273 num_examples: 390 download_size: 65743 dataset_size: 137273 --- # Dataset Card for "mmlu-high_school_macroeconomics-neg-answer" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/aloy_genshin
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of aloy/アーロイ/埃洛伊 (Genshin Impact) This is the dataset of aloy/アーロイ/埃洛伊 (Genshin Impact), containing 261 images and their tags. The core tags of this character are `long_hair, breasts, braid, freckles, green_eyes, brown_hair, lips, large_breasts, orange_hair, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 261 | 379.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aloy_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 261 | 331.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aloy_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 590 | 572.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aloy_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/aloy_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 | 8 | ![](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, navel, nipples, solo, pussy, body_freckles, completely_nude, looking_at_viewer, sitting, uncensored, blurry_background, female_pubic_hair, jewelry, abs, artist_name, blush, hair_ornament, outdoors, smile, sweat, thighs | | 1 | 12 | ![](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, solo, simple_background, brown_eyes, red_hair, white_background, portrait | | 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, looking_at_viewer, nipples, solo, artist_name, completely_nude, navel, necklace, on_back, parted_lips, red_hair, armpits, mosaic_censoring, pillow, pussy, arms_behind_head, arms_up, bed_sheet, on_bed | | 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, erection, futanari, large_penis, looking_at_viewer, uncensored, nipples, nose, solo, spread_legs, veiny_penis, navel, parted_lips, abs, breasts_apart, large_testicles, sitting, blue_eyes, completely_nude, huge_penis, jewelry, muscular_female, precum | | 4 | 17 | ![](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, solo, necklace, arrow_(projectile), beads, holding_bow_(weapon), boots, quiver, fur_trim, pants, simple_background, full_body, multiple_braids, tribal, white_background | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, from_behind, looking_back, solo, body_freckles, looking_at_viewer, completely_nude, blurry_background, blush, thighs, artist_name, blue_eyes, cowboy_shot, huge_ass, mole_on_ass, outdoors, red_hair, sideboob, sweat | | 6 | 9 | ![](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, outdoors, solo, blue_sky, day, looking_at_viewer, bare_shoulders, cloud, red_hair, beach, ocean, thighs, twin_braids, bikini, cowboy_shot, navel, palm_tree, standing, cameltoe, cleavage | | 7 | 9 | ![](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, solo, uncensored, nipples, anus, completely_nude, ass, female_masturbation, pussy_juice, spread_legs, blush, body_freckles, fingering, jewelry, looking_at_viewer, simple_background | | 8 | 10 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, nipples, solo, pussy, spread_legs, vaginal_object_insertion, nude, uncensored, open_mouth, sex_machine, barefoot, bondage, red_hair, restrained, toes, clitoris, feet, necklace, sex_toy, soles | | 9 | 17 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, hetero, penis, uncensored, sex, 1boy, pussy, solo_focus, nipples, vaginal, completely_nude, navel, open_mouth, outdoors, spread_legs, blush, looking_at_viewer, ass, body_freckles, testicles, straddling | | 10 | 10 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | arms_behind_back, bondage, gagged, nipples, 1girl, rope, solo, shibari, nipple_piercing, ball_gag, barefoot, collar, nude, feet, pussy, restrained | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | navel | nipples | solo | pussy | body_freckles | completely_nude | looking_at_viewer | sitting | uncensored | blurry_background | female_pubic_hair | jewelry | abs | artist_name | blush | hair_ornament | outdoors | smile | sweat | thighs | simple_background | brown_eyes | red_hair | white_background | portrait | necklace | on_back | parted_lips | armpits | mosaic_censoring | pillow | arms_behind_head | arms_up | bed_sheet | on_bed | erection | futanari | large_penis | nose | spread_legs | veiny_penis | breasts_apart | large_testicles | blue_eyes | huge_penis | muscular_female | precum | arrow_(projectile) | beads | holding_bow_(weapon) | boots | quiver | fur_trim | pants | full_body | multiple_braids | tribal | from_behind | looking_back | cowboy_shot | huge_ass | mole_on_ass | sideboob | blue_sky | day | bare_shoulders | cloud | beach | ocean | twin_braids | bikini | palm_tree | standing | cameltoe | cleavage | anus | ass | female_masturbation | pussy_juice | fingering | vaginal_object_insertion | nude | open_mouth | sex_machine | barefoot | bondage | restrained | toes | clitoris | feet | sex_toy | soles | hetero | penis | sex | 1boy | solo_focus | vaginal | testicles | straddling | arms_behind_back | gagged | rope | shibari | nipple_piercing | ball_gag | collar | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:----------|:-------|:--------|:----------------|:------------------|:--------------------|:----------|:-------------|:--------------------|:--------------------|:----------|:------|:--------------|:--------|:----------------|:-----------|:--------|:--------|:---------|:--------------------|:-------------|:-----------|:-------------------|:-----------|:-----------|:----------|:--------------|:----------|:-------------------|:---------|:-------------------|:----------|:------------|:---------|:-----------|:-----------|:--------------|:-------|:--------------|:--------------|:----------------|:------------------|:------------|:-------------|:------------------|:---------|:---------------------|:--------|:-----------------------|:--------|:---------|:-----------|:--------|:------------|:------------------|:---------|:--------------|:---------------|:--------------|:-----------|:--------------|:-----------|:-----------|:------|:-----------------|:--------|:--------|:--------|:--------------|:---------|:------------|:-----------|:-----------|:-----------|:-------|:------|:----------------------|:--------------|:------------|:---------------------------|:-------|:-------------|:--------------|:-----------|:----------|:-------------|:-------|:-----------|:-------|:----------|:--------|:---------|:--------|:------|:-------|:-------------|:----------|:------------|:-------------|:-------------------|:---------|:-------|:----------|:------------------|:-----------|:---------| | 0 | 8 | ![](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 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | 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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 17 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | X | | X | X | X | | | X | | | | X | X | | X | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 10 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | X | X | X | | | | | X | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 9 | 17 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | X | | X | X | X | X | | X | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | 10 | 10 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | | | X | | | | | | | | | | | X | X | X | X | X | X | X |
ChaiML/20240108_chai_prize_reward_model_data_season_v
--- dataset_info: features: - name: input_text dtype: string - name: labels dtype: int64 - name: season dtype: string splits: - name: train num_bytes: 66684838 num_examples: 33867 download_size: 36785187 dataset_size: 66684838 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "20240108_chai_prize_reward_model_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
theblackcat102/oasst-red-team
--- language: - en - de - fr - ru - zh - ja - it - pt - th - nl - ro - pl - hu - hr --- Work in progress Red team datasets for training and testing reward model for open assistant
autoevaluate/autoeval-staging-eval-project-banking77-34727576-11425522
--- type: predictions tags: - autotrain - evaluation datasets: - banking77 eval_info: task: multi_class_classification model: nickprock/distilbert-base-uncased-banking77-classification metrics: [] dataset_name: banking77 dataset_config: default dataset_split: test col_mapping: text: text target: label --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Multi-class Text Classification * Model: nickprock/distilbert-base-uncased-banking77-classification * Dataset: banking77 * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@nickprock](https://huggingface.co/nickprock) for evaluating this model.
aisuko/vqa
--- license: apache-2.0 --- # Overview The original code is from https://huggingface.co/datasets/Graphcore/vqa/tree/main Adaptered by: Aisuko # How to use it ```python from datasets import load_dataset dataset = load_dataset("aisuko/vqa", split="validation[:200]") dataset ``` ``` Dataset({ features: ['question', 'question_type', 'question_id', 'image_id', 'answer_type', 'label'], num_rows: 200 }) ``` ## Remove the label column ```python dataset = dataset.remove_columns(['question_type', 'question_id', 'answer_type']) ``` ## Check the image ```python from PIL import Image image = Image.open(dataset[0]['image_id']) image ```
clarin-knext/scifact-pl
--- language: - pl pretty_name: BEIR-PL benchmark Scifact-PL --- Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**. Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf Contact: konrad.wojtasik@pwr.edu.pl
sudarsa/tts_hindi
--- license: apache-2.0 dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 21241940.0 num_examples: 10 download_size: 15708375 dataset_size: 21241940.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
llm-aes/gpt-3.5_SummEval_gpt2-vs-others_analyze_rate
--- dataset_info: features: - name: task_id dtype: string - name: worker_id dtype: string - name: human_label dtype: int64 - name: llm_label dtype: int64 - name: generator_1 dtype: string - name: generator_2 dtype: string - name: premise dtype: string splits: - name: train num_bytes: 3292945 num_examples: 1500 download_size: 288733 dataset_size: 3292945 configs: - config_name: default data_files: - split: train path: data/train-* ---
tyzhu/lmind_hotpot_train300_eval100_v1_doc_qa
--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string splits: - name: train_qa num_bytes: 51441 num_examples: 300 - name: train_recite_qa num_bytes: 312070 num_examples: 300 - name: eval_qa num_bytes: 16148 num_examples: 100 - name: eval_recite_qa num_bytes: 104950 num_examples: 100 - name: all_docs num_bytes: 361191 num_examples: 797 - name: all_docs_eval num_bytes: 361140 num_examples: 797 - name: train num_bytes: 412632 num_examples: 1097 - name: validation num_bytes: 16148 num_examples: 100 download_size: 813503 dataset_size: 1635720 --- # Dataset Card for "lmind_hotpot_train300_eval100_v1_doc_qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
poorfish/fishdataset
--- size_categories: - 1K<n<10K task_categories: - text-classification ---
megantron/aesthetic_labeled
--- dataset_info: features: - name: image dtype: image - name: 'Unnamed: 0' dtype: int64 - name: label dtype: int64 splits: - name: test num_bytes: 3101095.0 num_examples: 8 download_size: 1553003 dataset_size: 3101095.0 --- # Dataset Card for "aesthetic_labeled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sakshamrzt/medical_qa
--- license: cc0-1.0 task_categories: - table-question-answering language: - en size_categories: - 1K<n<10K dataset_info: - config_name: default features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: float64 splits: - name: test num_examples: 2048 - config_name: corpus features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string splits: - name: corpus num_examples: 2048 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: queries num_examples: 2048 configs: - config_name: default data_files: - split: test path: default.jsonl - config_name: corpus data_files: - split: corpus path: corpus.jsonl - config_name: queries data_files: - split: queries path: queries.jsonl --- # Dataset Card for Dataset Name ## Dataset Details The MedQuad dataset normalised for use with mteb. The dataset contains questions and answers related to medical conditions, treatments, and protocols ### Dataset Description - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
liuyanchen1015/MULTI_VALUE_qqp_relativizer_where
--- 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: 363726 num_examples: 1820 - name: test num_bytes: 3454497 num_examples: 17679 - name: train num_bytes: 3186941 num_examples: 15990 download_size: 4159938 dataset_size: 7005164 --- # Dataset Card for "MULTI_VALUE_qqp_relativizer_where" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-college_mathematics-verbal-neg-prepend
--- dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: neg_prompt dtype: string splits: - name: test num_bytes: 40583 num_examples: 100 download_size: 25747 dataset_size: 40583 --- # Dataset Card for "mmlu-college_mathematics-verbal-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceM4/sharegpt4v-nowebimages
Invalid username or password.
tasksource/lexcomp-nc-attributes
--- license: apache-2.0 language: - en --- https://github.com/vered1986/lexcomp/tree/master ``` @article{shwartz-dagan-2019-still, title = "Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition", author = "Shwartz, Vered and Dagan, Ido", journal = "Transactions of the Association for Computational Linguistics", volume = "7", year = "2019", address = "Cambridge, MA", publisher = "MIT Press", url = "https://aclanthology.org/Q19-1027", doi = "10.1162/tacl_a_00277", pages = "403--419", abstract = "Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that, as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, consisting of six tasks related to lexical composition effects, can serve future research aiming to improve representations.", } ```
CyberHarem/ratura_lapisrelights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Ratura (Lapis Re:LiGHTs) This is the dataset of Ratura (Lapis Re:LiGHTs), containing 90 images and their tags. The core tags of this character are `blonde_hair, long_hair, hair_ornament, x_hair_ornament, hair_between_eyes, blue_eyes, purple_eyes, bangs, breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 90 | 57.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ratura_lapisrelights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 90 | 47.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ratura_lapisrelights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 183 | 87.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ratura_lapisrelights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 90 | 57.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ratura_lapisrelights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 183 | 103.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ratura_lapisrelights/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/ratura_lapisrelights', 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, black_gloves, fingerless_gloves, closed_mouth, capelet, upper_body, outdoors, blush, medium_breasts, tree | | 1 | 9 | ![](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) | blush, 2girls, school_uniform, solo_focus, collarbone, closed_mouth, short_sleeves, hairclip, outdoors, pink_hair, smile | | 2 | 17 | ![](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, solo, closed_mouth, blush, school_uniform, smile, anime_coloring, blurry_background, collarbone, hairclip, indoors, portrait, shirt, low_twintails, looking_at_viewer, upper_body | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, closed_mouth, hat, sailor_collar, smile, solo, white_headwear, sleeveless_dress, standing, white_dress, looking_at_viewer, sailor_dress, collarbone, full_body, short_dress, striped, twintails | | 4 | 6 | ![](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, indoors, short_sleeves, solo, closed_mouth, collarbone, frills, sitting, skirt, smile, ascot, puffy_sleeves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | black_gloves | fingerless_gloves | closed_mouth | capelet | upper_body | outdoors | blush | medium_breasts | tree | 2girls | school_uniform | solo_focus | collarbone | short_sleeves | hairclip | pink_hair | smile | anime_coloring | blurry_background | indoors | portrait | shirt | low_twintails | looking_at_viewer | hat | sailor_collar | white_headwear | sleeveless_dress | standing | white_dress | sailor_dress | full_body | short_dress | striped | twintails | frills | sitting | skirt | ascot | puffy_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------------------|:---------------|:----------|:-------------|:-----------|:--------|:-----------------|:-------|:---------|:-----------------|:-------------|:-------------|:----------------|:-----------|:------------|:--------|:-----------------|:--------------------|:----------|:-----------|:--------|:----------------|:--------------------|:------|:----------------|:-----------------|:-------------------|:-----------|:--------------|:---------------|:------------|:--------------|:----------|:------------|:---------|:----------|:--------|:--------|:----------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 17 | ![](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 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | | X | | | | | | | | | | X | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | 4 | 6 | ![](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 |
stefan-it/co-funer
--- license: mit task_categories: - token-classification language: - de --- # CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction This inofficial dataset repository provides a CoNLL-like version of the CO-Fun **NER** dataset, that was proposed in the CO-Fun paper (https://arxiv.org/abs/2403.15322): > The process of cyber mapping gives insights in relationships among financial entities and service providers. Centered around the outsourcing practices of companies within fund prospectuses in Germany, we introduce a dataset specifically designed for named entity recognition and relation extraction tasks. The labeling process on 948 sentences was carried out by three experts which yields to 5,969 annotations for four entity types (Outsourcing, Company, Location and Software) and 4,102 relation annotations (Outsourcing-Company, Company-Location). State-of-the-art deep learning models were trained to recognize entities and extract relations showing first promising results. ## Preprocessing The notebook [Export-To-CoNLL.ipynb](Export-To-CoNLL.ipynb) performs the necessary steps to create a CoNLL-like version of the CO-Fun dataset, that could easily be used for fine-tuning NER models. Additionally, the [FlairDatasetTest.ipynb](FlairDatasetTest.ipynb) notebooks loads the dataset with the Flair dataset loader and checks, if the number of parsed sentences is correct and identical to the number of sentences reported in the official CO-Fun paper. ## Named Entites The CO-Fun dataset provides annotations for the following Named Entities: * `Auslagerung` (engl. outsourcing) * `Unternehmen` (engl. company) * `Ort` (engl. location) * `Software` # Example: Load Dataset with Flair library The notebooks [FlairDatasetExample.ipynb](FlairDatasetExample.ipynb) shows how to load the dataset with the awesome [Flair library](https://github.com/flairNLP/flair). # Changelog * 25.03.2024: Initial version of the preprocessed CO-Fun NER dataset is released. # Licence The original CO-Fun dataset is released under MIT license. Thus, this preprocessed version is also licenced under MIT.
nateraw/quick-captioning-dataset-test
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 345244.0 num_examples: 4 download_size: 0 dataset_size: 345244.0 --- # Dataset Card for "quick-captioning-dataset-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BramVanroy/ultra_feedback_dutch_cleaned
--- language: - nl dataset_info: - config_name: default features: - name: prompt dtype: string - name: GEITje-7B-ultra dtype: string - name: gpt-4-turbo dtype: string - name: rating_conciseness_GEITje-7B-ultra dtype: int64 - name: rating_conciseness_gpt-4-turbo dtype: int64 - name: rating_dutchness_GEITje-7B-ultra dtype: int64 - name: rating_dutchness_gpt-4-turbo dtype: int64 - name: rating_helpfulness_GEITje-7B-ultra dtype: int64 - name: rating_helpfulness_gpt-4-turbo dtype: int64 - name: rating_avg_GEITje-7B-ultra dtype: float64 - name: rating_avg_gpt-4-turbo dtype: float64 splits: - name: train num_bytes: 238549993.0 num_examples: 50820 download_size: 136381277 dataset_size: 238549993.0 - config_name: dpo_all features: - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train_prefs num_bytes: 276826879.25 num_examples: 48279 - name: test_prefs num_bytes: 14569835.75 num_examples: 2541 download_size: 165576369 dataset_size: 291396715.0 - config_name: dpo_hq features: - name: prompt dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train_prefs num_bytes: 55192382.49245088 num_examples: 9186 - name: test_prefs num_bytes: 2908024.507549121 num_examples: 484 download_size: 33267119 dataset_size: 58100407.0 - config_name: sft_gpt4_all features: - name: prompt dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 145093644.4 num_examples: 48279 - name: test_sft num_bytes: 7636507.6 num_examples: 2541 download_size: 87206558 dataset_size: 152730152.0 - config_name: sft_gpt4_hq features: - name: prompt dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 61513259.16137732 num_examples: 19726 - name: test_sft num_bytes: 3240001.8386226823 num_examples: 1039 download_size: 37187813 dataset_size: 64753261.0 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: dpo_all data_files: - split: train_prefs path: dpo_all/train_prefs-* - split: test_prefs path: dpo_all/test_prefs-* - config_name: dpo_hq data_files: - split: train_prefs path: dpo_hq/train_prefs-* - split: test_prefs path: dpo_hq/test_prefs-* - config_name: sft_gpt4_all data_files: - split: train_sft path: sft_gpt4_all/train_sft-* - split: test_sft path: sft_gpt4_all/test_sft-* - config_name: sft_gpt4_hq data_files: - split: train_sft path: sft_gpt4_hq/train_sft-* - split: test_sft path: sft_gpt4_hq/test_sft-* --- # Ultra Feedback Dutch Cleaned This is a cleaned version of [BramVanroy/ultra_feedback_dutch](https://huggingface.co/datasets/BramVanroy/ultra_feedback_dutch), based on the [cleaning](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned) done by Argilla on the original Ultra Feedback dataset. After cleaning I also generated replies for other models (like TowerInstruct, Mistral), but the results were too poor (in Dutch) to include so we only kept the GEITje Ultra and gpt-4-turbo generations. For both of these models we then had gpt-4-1106-preview rate different aspects of the query responses, the Dutch-ness, Helpfulness, and Conciseness (see ""Prompts" below). The motivation for this dataset was heavily community-inspired. Most thanks go out to [David Berenstein](https://huggingface.co/davidberenstein1957) and [Edwin Rijgersberg](https://huggingface.co/Rijgersberg)! ## Usage The default dataset contains all the original information (after cleaning). For actually usage, you need to use one of the subsets. All subsets have a test split of 5%. ```python from datasets import load_dataset ds = load_dataset("BramVanroy/ultra_feedback_dutch_cleaned_rated", "sft_gpt4_hq") ``` - `sft_gpt4_all` (50.8k): for instruction tuning, only the GPT-4 generations are kept. No further filtering. - `sft_gpt4_hq` (20.8k): for instruction tuning, only high-quality GPT-4 generations are kept. That means: an average score of at least 4.5 and no individual score can be less than 4.0. - `dpo_all` (50.8k): for preference tuning, no further filtering. The model with the highest average score is chosen as `chosen`, the other as `rejected`. In case ofa tie, GPT4 wins. - `dpo_hq` (9.67k): for preference tuning. Only contains data where the average score of both models is at least 4.0, and where no score can be less than 3.5. Furthermore, the absolute difference between the two models' average scores cannot be less than 0.25 or higher than 2.0. The model with the highest average score is chosen as `chosen`, the other as `rejected`. In case ofa tie, GPT4 wins. ## Preprocessing First, the low-quality/contaminated samples [as removed in the English cleaned version](argilla/ultrafeedback-binarized-preferences-cleaned) were also removed here. Second, the data was deduplicated on all three text columns individually (model 1, model 2, prompt). Lastly, more specific filters were applied: - samples that were not identified as Dutch by fastText were removed - samples with non-Latin characters are removed (very strict filtering, removes any translation tasks with non-Latin languages) - samples with occurrences of "AI-assistent" or "AI-taalmodel" (and other derivations) are removed because these are often responses in the sense of "As an AI model, I cannot ...", which is not too useful - samples with mentions of ChatGPT, GPT 3/4, OpenAI or ShareGPT are removed - samples with mentions of the typical "knowledge cutoff" are removed - samples with apologies such as "spijt me" are removed, as we are more interested in factual information and content-filled responses ## Prompts These were originally made by [David Berenstein](https://huggingface.co/davidberenstein1957) at [Argilla](https://huggingface.co/argilla). I modified those slightly and used my own querying library. ### System prompt > Je bent een automatische annotator die de kwaliteit van de tekst van een AI-model beoordeelt aan de hand van gegeven criteria. De tekst van het AI-model is een reactie op een gegeven instructie en moet die instructie dus goed beantwoorden of volgen. ### User prompt For every model we query GPT4 multiple times, once for each criterion. We investigated three criteria: Dutch-ness (how good is the model's Dutch output), Helpfulness (how relevant is the model's reply), and Conciseness (how to-the-point is the model). Below you find the template and criteria. `criterion_options` is a formatted list of the given options for a given criterion according to `opt_template` for each option. ```python template = """Het volgende is een instructie geschreven door een mens (`Instructie:`), en een reactie op de instructie geschreven door een AI-model (`Reactie:`). Beoordeel de kwaliteit van de reactie van het AI-model, rekening houdend met de gegeven opties (`Opties:`). Instructie: {prompt} --- Reactie: {response} --- Criteria: {criterion_question} Opties: {criterion_options} --- Je antwoord moet in het volgende formaat zijn: <rating>[{{min_score}}-{{max_score}}]</rating> bijvoorbeeld: <rating>3</rating> --- Beoordeel nu alsjeblieft de `Reactie:` met een rating op basis van de `Opties:`. Geef geen extra uitleg.""" opt_template = """\ - {score}: {beschrijving}\ """ criteria = { "dutchness": { "criterion_question": "Is de reactie in vlot en gramaticaal correct Nederlands geschreven? Negeer code-fragmenten in je analyse en richt je enkel op de doorlopende tekst. Leenwoorden uit andere talen mogen gebruikt worden als dat gewoonlijk is in het domein (bv. bij software). Een hogere score duidt op beter Nederlands taalgebruik.", "criterion_options": { 1: "De reactie is onleesbaar, bevat veel grammaticale fouten, of is in slecht Nederlands geschreven.", 2: "De reactie is moeilijk te begrijpen of bevat veel grammaticale fouten.", 3: "De reactie is begrijpelijk maar bevat enkele grammaticale fouten.", 4: "De reactie is goed geschreven en bevat weinig grammaticale fouten.", 5: "De reactie is uitstekend geschreven, vlot leesbaar en bevat geen grammaticale fouten.", }, }, "helpfulness": { "criterion_question": "Is de reactie relevant en behulpzaam? Beantwoordt het model de instructie goed? Een hogere score duidt op een relevantere en behulpzamere reactie.", "criterion_options": { 1: "De reactie is helemaal niet relevant of heeft aanzienlijke afwijkingen.", 2: "De reactie is slechts enigszins relevant maar is niet concreet.", 3: "De reactie is min of meer relevant en geeft een relevant antwoord.", 4: "De reactie is grotendeels relevant en lijkt zeer nuttig.", 5: "De reactie biedt briljante ideeën die de taak nauwkeurig aanpakken.", }, }, "conciseness": { "criterion_question": "Is de reactie beknopt en ter zake, zonder onnodige herhaling of uitweiding? Een hogere score duidt op een beknoptere, duidelijkere reactie.", "criterion_options": { 1: "De reactie bevat overmatige herhaling of onnodige uitweiding.", 2: "De reactie is nogal omslachtig.", 3: "De reactie is redelijk beknopt met minimaal onnodige inhoud.", 4: "De reactie is beknopt en ter zake, met minimaal onnodige inhoud.", 5: "De reactie is uitzonderlijk positief beknopt, verstrekt informatie efficiënt.", }, }, } ``` ## Rating segmentation script Note that data filtering and deduplication was done separately, based on [`interactive-filter-dutch`](https://github.com/BramVanroy/dutch-instruction-datasets). The following script is simply to create the configs. ```python from typing import Literal from datasets import load_dataset ds = load_dataset("BramVanroy/ultra_feedback_dutch_cleaned", split="train") model_cols = ["GEITje-7B-ultra", "gpt-4-turbo"] model_ratings_no_avg_cols = {m: [c for c in ds.column_names if m in c and "rating" in c and "avg" not in c] for m in model_cols} model_ratings_avg_cols = {m: f"rating_avg_{m}" for m in model_cols} print("original dataset", ds.shape) def filter_score_single(sample, model_name: str, rating_type: Literal["any", "all", "avg"], threshold: float = 3.5): if rating_type == "all": return all(sample[r] >= threshold for r in model_ratings_no_avg_cols[model_name]) elif rating_type == "avg": return sample[model_ratings_avg_cols[model_name]] >= threshold else: raise ValueError(f"Invalid rating_type: {rating_type}") def as_messages(sample, model_name: str): messages = [ {"role": "user", "content": sample["prompt"]}, {"role": "assistant", "content": sample[model_name]}, ] return {"messages": messages} def as_chosen_reject(sample): model_chosen = "GEITje-7B-ultra" if sample["rating_avg_GEITje-7B-ultra"] > sample["rating_avg_gpt-4-turbo"] else "gpt-4-turbo" model_rejected = "GEITje-7B-ultra" if model_chosen == "gpt-4-turbo" else "gpt-4-turbo" chosen = [ {"role": "user", "content": sample["prompt"]}, {"role": "assistant", "content": sample[model_chosen]}, ] rejected = [ {"role": "user", "content": sample["prompt"]}, {"role": "assistant", "content": sample[model_rejected]}, ] return {"chosen": chosen, "rejected": rejected} def diff_filter(sample, min_diff: float, max_diff: float): rating1 = sample[model_ratings_avg_cols["gpt-4-turbo"]] rating2 = sample[model_ratings_avg_cols["GEITje-7B-ultra"]] diff = abs(rating1 - rating2) return min_diff <= diff <= max_diff # FOR SFT: ALL # ds_all_sft = ds.map(lambda x: as_messages(x, "gpt-4-turbo"), num_proc=64) # ds_all_sft = ds_all_sft.train_test_split(test_size=0.05, seed=42) # ds_all_sft["train_sft"] = ds_all_sft["train"] # ds_all_sft["test_sft"] = ds_all_sft["test"] # del ds_all_sft["train"] # del ds_all_sft["test"] # ds_all_sft = ds_all_sft.select_columns(["prompt", "messages"]) # ds_all_sft.push_to_hub("BramVanroy/ultra_feedback_dutch_cleaned", config_name="sft_gpt4_all") # FOR SFT: High quality GPT-4 generations ds_gpt4_hq = ds.filter(lambda x: filter_score_single(x, "gpt-4-turbo", "avg", 4.5), num_proc=64) ds_gpt4_hq = ds_gpt4_hq.filter(lambda x: filter_score_single(x, "gpt-4-turbo", "all", 4.0), num_proc=64) ds_gpt4_hq = ds_gpt4_hq.map(lambda x: as_messages(x, "gpt-4-turbo"), num_proc=64) ds_gpt4_hq = ds_gpt4_hq.select_columns(["prompt", "messages"]) ds_gpt4_hq = ds_gpt4_hq.train_test_split(test_size=0.05, seed=42) ds_gpt4_hq["train_sft"] = ds_gpt4_hq["train"] ds_gpt4_hq["test_sft"] = ds_gpt4_hq["test"] del ds_gpt4_hq["train"] del ds_gpt4_hq["test"] ds_gpt4_hq.push_to_hub("BramVanroy/ultra_feedback_dutch_cleaned", config_name="sft_gpt4_hq") print("gpt4_hq", ds_gpt4_hq.shape) # FOR DPO: ALL - highest avg model is picked ds_all_dpo = ds.map(as_chosen_reject, num_proc=64) ds_all_dpo = ds_all_dpo.select_columns(["prompt", "chosen", "rejected"]) ds_all_dpo = ds_all_dpo.train_test_split(test_size=0.05, seed=42) ds_all_dpo["train_prefs"] = ds_all_dpo["train"] ds_all_dpo["test_prefs"] = ds_all_dpo["test"] del ds_all_dpo["train"] del ds_all_dpo["test"] ds_all_dpo.push_to_hub("BramVanroy/ultra_feedback_dutch_cleaned", config_name="dpo_all") # FOR DPO: High quality - highest avg model is picked # + Min. avg score of 4.0, min. all scores of 3.5. Min diff. of 0.25, max diff. of 2. ds_dpo_hq = ds.filter(lambda x: filter_score_single(x, "gpt-4-turbo", "avg", 4.0), num_proc=64) ds_dpo_hq = ds_dpo_hq.filter(lambda x: filter_score_single(x, "gpt-4-turbo", "all", 3.5), num_proc=64) ds_dpo_hq = ds_dpo_hq.filter(lambda x: filter_score_single(x, "GEITje-7B-ultra", "avg", 4.0), num_proc=64) ds_dpo_hq = ds_dpo_hq.filter(lambda x: filter_score_single(x, "GEITje-7B-ultra", "all", 3.5), num_proc=64) ds_dpo_hq = ds_dpo_hq.filter(lambda x: diff_filter(x, 0.25, 2), num_proc=64) ds_dpo_hq = ds_dpo_hq.map(as_chosen_reject, num_proc=64) ds_dpo_hq = ds_dpo_hq.select_columns(["prompt", "chosen", "rejected"]) ds_dpo_hq = ds_dpo_hq.train_test_split(test_size=0.05, seed=42) ds_dpo_hq["train_prefs"] = ds_dpo_hq["train"] ds_dpo_hq["test_prefs"] = ds_dpo_hq["test"] del ds_dpo_hq["train"] del ds_dpo_hq["test"] ds_dpo_hq.push_to_hub("BramVanroy/ultra_feedback_dutch_cleaned", config_name="dpo_hq") # Geitje avg score higher than gpt 4 avg score # ds_geitje_higher = ds.filter(lambda x: x[model_ratings_avg_cols["GEITje-7B-ultra"]] > x[model_ratings_avg_cols["gpt-4-turbo"]], num_proc=64) # print(ds_geitje_higher.shape) ```
saibo/bookcorpus_compact_1024_shard1_of_10_meta
--- dataset_info: features: - name: text dtype: string - name: concept_with_offset dtype: string - name: cid_arrangement sequence: int32 - name: schema_lengths sequence: int64 - name: topic_entity_mask sequence: int64 - name: text_lengths sequence: int64 splits: - name: train num_bytes: 7450626244 num_examples: 61605 download_size: 1631069561 dataset_size: 7450626244 --- # Dataset Card for "bookcorpus_compact_1024_shard1_of_10_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pk1762006/Realty2
--- license: mit ---
deu05232/multiwoz_v23_2
--- dataset_info: features: - name: intent sequence: string - name: text dtype: string splits: - name: train num_bytes: 5836889 num_examples: 54176 - name: validation num_bytes: 777785 num_examples: 7084 - name: test num_bytes: 772136 num_examples: 7056 download_size: 2518039 dataset_size: 7386810 --- # Dataset Card for "multiwoz_v23_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mstz/muskV2
--- language: - en tags: - musk - tabular_classification - binary_classification - multiclass_classification pretty_name: Musk size_categories: - 100<n<1K task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts - tabular-classification configs: - musk --- # Musk The [Musk dataset](https://archive.ics.uci.edu/ml/datasets/Musk) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets). Census dataset including personal characteristic of a person, and their income threshold. # Configurations and tasks | **Configuration** | **Task** | **Description** | |-------------------|---------------------------|------------------------| | musk | Binary classification | Is the molecule a musk?| # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/muskV2")["train"] ```
yan1984/pegasus-samsum
--- license: mit ---
rai-sandeep/whitepaper-data
--- dataset_info: features: - name: task dtype: string - name: solution dtype: string splits: - name: train num_bytes: 340930 num_examples: 22 download_size: 179210 dataset_size: 340930 --- # Dataset Card for "whitepaper-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yeniceriSGK/Falcon1BTestingDataSet
--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 19657 num_examples: 10 download_size: 20918 dataset_size: 19657 configs: - config_name: default data_files: - split: train path: data/train-* ---
BevenRozario/job_desc_5k
--- dataset_info: features: - name: Instruction dtype: string - name: Response dtype: string splits: - name: train_dataset num_bytes: 8140016.7 num_examples: 4500 - name: eval_dataset num_bytes: 904446.3 num_examples: 500 download_size: 2283111 dataset_size: 9044463.0 configs: - config_name: default data_files: - split: train_dataset path: data/train_dataset-* - split: eval_dataset path: data/eval_dataset-* ---
DylanonWic/common_voice_10_1_th_augmented_pitch
--- dataset_info: features: - name: sentence dtype: string - name: input_ids sequence: int32 - name: input_values sequence: float32 splits: - name: train num_bytes: 7093139791 num_examples: 28696 - name: test num_bytes: 3163850075.5886087 num_examples: 10123 - name: validation num_bytes: 2976158781.6036987 num_examples: 10009 download_size: 12714099625 dataset_size: 13233148648.192307 --- # Dataset Card for "common_voice_10_1_th_augmented_pitch" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vvtq/control_val_10
--- dataset_info: features: - name: image dtype: image - name: noised dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 15015921.0 num_examples: 11 download_size: 15018492 dataset_size: 15015921.0 --- # Dataset Card for "control_val_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MAdAiLab/lex_glue_scotus
--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' '5': '6' '6': '7' '7': '8' '8': '9' '9': '10' '10': '11' '11': '12' '12': '13' splits: - name: train num_bytes: 178959316 num_examples: 5000 - name: test num_bytes: 76213279 num_examples: 1400 - name: validation num_bytes: 75600243 num_examples: 1400 download_size: 173411381 dataset_size: 330772838 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
eswardivi/1_MSA_PHASE
--- dataset_info: features: - name: audio dtype: audio - name: Name dtype: string - name: Label dtype: string - name: text dtype: string splits: - name: train num_bytes: 384226713.0 num_examples: 116 download_size: 382442220 dataset_size: 384226713.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
phongmt184172/python_data_27k
--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 39244063.17425801 num_examples: 19056 - name: test num_bytes: 8410618.912870996 num_examples: 4084 - name: val num_bytes: 8410618.912870996 num_examples: 4084 download_size: 23588770 dataset_size: 56065301.0 --- # Dataset Card for "python_data_27k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
cburger/md_cleaned
--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': ' Allergy / Immunology' '1': ' Autopsy' '2': ' Bariatrics' '3': ' Cardiovascular / Pulmonary' '4': ' Chiropractic' '5': ' Consult - History and Phy.' '6': ' Cosmetic / Plastic Surgery' '7': ' Dentistry' '8': ' Dermatology' '9': ' Diets and Nutritions' '10': ' Discharge Summary' '11': ' ENT - Otolaryngology' '12': ' Emergency Room Reports' '13': ' Endocrinology' '14': ' Gastroenterology' '15': ' General Medicine' '16': ' Hematology - Oncology' '17': ' Hospice - Palliative Care' '18': ' IME-QME-Work Comp etc.' '19': ' Lab Medicine - Pathology' '20': ' Letters' '21': ' Nephrology' '22': ' Neurology' '23': ' Neurosurgery' '24': ' Obstetrics / Gynecology' '25': ' Office Notes' '26': ' Ophthalmology' '27': ' Orthopedic' '28': ' Pain Management' '29': ' Pediatrics - Neonatal' '30': ' Physical Medicine - Rehab' '31': ' Podiatry' '32': ' Psychiatry / Psychology' '33': ' Radiology' '34': ' Rheumatology' '35': ' SOAP / Chart / Progress Notes' '36': ' Sleep Medicine' '37': ' Speech - Language' '38': ' Surgery' '39': ' Urology' splits: - name: train num_bytes: 15217210 num_examples: 4948 download_size: 7196712 dataset_size: 15217210 --- # Dataset Card for "md_cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
carnival13/rbrt_eval_sur_full_lrg
--- dataset_info: features: - name: domain_label dtype: int64 - name: pass_label dtype: int64 - name: input dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 58030544 num_examples: 22480 download_size: 16743699 dataset_size: 58030544 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "rbrt_eval_sur_full_lrg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AI4EPS/quakeflow_nc
--- license: mit --- # Quakeflow_NC ## Introduction This dataset is part of the data (1970-2020) from [NCEDC (Northern California Earthquake Data Center)](https://ncedc.org/index.html) and is organized as several HDF5 files. The dataset structure is shown below, and you can find more information about the format at [AI4EPS](https://ai4eps.github.io/homepage/ml4earth/seismic_event_format1/)) Cite the NCEDC and PhaseNet: Zhu, W., & Beroza, G. C. (2018). PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method. arXiv preprint arXiv:1803.03211. NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Acknowledge the NCEDC: Waveform data, metadata, or data products for this study were accessed through the Northern California Earthquake Data Center (NCEDC), doi:10.7932/NCEDC. ``` Group: / len:16227 |- Group: /nc71111584 len:2 | |-* begin_time = 2020-01-02T07:01:19.620 | |-* depth_km = 3.69 | |-* end_time = 2020-01-02T07:03:19.620 | |-* event_id = nc71111584 | |-* event_time = 2020-01-02T07:01:48.240 | |-* event_time_index = 2862 | |-* latitude = 37.6545 | |-* longitude = -118.8798 | |-* magnitude = -0.15 | |-* magnitude_type = D | |-* num_stations = 2 | |- Dataset: /nc71111584/NC.MCB..HH (shape:(3, 12000)) | | |- (dtype=float32) | | | |-* azimuth = 233.0 | | | |-* component = ['E' 'N' 'Z'] | | | |-* distance_km = 1.9 | | | |-* dt_s = 0.01 | | | |-* elevation_m = 2391.0 | | | |-* emergence_angle = 159.0 | | | |-* event_id = ['nc71111584' 'nc71111584'] | | | |-* latitude = 37.6444 | | | |-* location = | | | |-* longitude = -118.8968 | | | |-* network = NC | | | |-* phase_index = [3000 3101] | | | |-* phase_polarity = ['U' 'N'] | | | |-* phase_remark = ['IP' 'ES'] | | | |-* phase_score = [1 2] | | | |-* phase_time = ['2020-01-02T07:01:49.620' '2020-01-02T07:01:50.630'] | | | |-* phase_type = ['P' 'S'] | | | |-* snr = [2.82143 3.055604 1.8412642] | | | |-* station = MCB | | | |-* unit = 1e-6m/s | |- Dataset: /nc71111584/NC.MCB..HN (shape:(3, 12000)) | | |- (dtype=float32) | | | |-* azimuth = 233.0 | | | |-* component = ['E' 'N' 'Z'] ...... ``` ## How to use ### Requirements - datasets - h5py - fsspec - torch (for PyTorch) ### Usage Import the necessary packages: ```python import h5py import numpy as np import torch from torch.utils.data import Dataset, IterableDataset, DataLoader from datasets import load_dataset ``` We have 6 configurations for the dataset: - "station" - "event" - "station_train" - "event_train" - "station_test" - "event_test" "station" yields station-based samples one by one, while "event" yields event-based samples one by one. The configurations with no suffix are the full dataset, while the configurations with suffix "_train" and "_test" only have corresponding split of the full dataset. Train split contains data from 1970 to 2019, while test split contains data in 2020. The sample of `station` is a dictionary with the following keys: - `data`: the waveform with shape `(3, nt)`, the default time length is 8192 - `phase_pick`: the probability of the phase pick with shape `(3, nt)`, the first dimension is noise, P and S - `event_location`: the event location with shape `(4,)`, including latitude, longitude, depth and time - `station_location`: the station location with shape `(3,)`, including latitude, longitude and depth The sample of `event` is a dictionary with the following keys: - `data`: the waveform with shape `(n_station, 3, nt)`, the default time length is 8192 - `phase_pick`: the probability of the phase pick with shape `(n_station, 3, nt)`, the first dimension is noise, P and S - `event_center`: the probability of the event time with shape `(n_station, feature_nt)`, default feature time length is 512 - `event_location`: the space-time coordinates of the event with shape `(n_staion, 4, feature_nt)` - `event_location_mask`: the probability mask of the event time with shape `(n_station, feature_nt)` - `station_location`: the space coordinates of the station with shape `(n_station, 3)`, including latitude, longitude and depth The default configuration is `station_test`. You can specify the configuration by argument `name`. For example: ```python # load dataset # ATTENTION: Streaming(Iterable Dataset) is difficult to support because of the feature of HDF5 # So we recommend to directly load the dataset and convert it into iterable later # The dataset is very large, so you need to wait for some time at the first time # to load "station_test" with test split quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", split="test") # or quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test") # to load "event" with train split quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="event", split="train") ``` #### Usage for `station` Then you can change the dataset into PyTorch format iterable dataset, and view the first sample: ```python quakeflow_nc = load_dataset("AI4EPS/quakeflow_nc", name="station_test", split="test") # for PyTorch DataLoader, we need to divide the dataset into several shards num_workers=4 quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers) # because add examples formatting to get tensors when using the "torch" format # has not been implemented yet, we need to manually add the formatting when using iterable dataset # if you want to use dataset directly, just use # quakeflow_nc.with_format("torch") quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()}) try: isinstance(quakeflow_nc, torch.utils.data.IterableDataset) except: raise Exception("quakeflow_nc is not an IterableDataset") # print the first sample of the iterable dataset for example in quakeflow_nc: print("\nIterable test\n") print(example.keys()) for key in example.keys(): print(key, example[key].shape, example[key].dtype) break dataloader = DataLoader(quakeflow_nc, batch_size=4, num_workers=num_workers) for batch in dataloader: print("\nDataloader test\n") print(batch.keys()) for key in batch.keys(): print(key, batch[key].shape, batch[key].dtype) break ``` #### Usage for `event` Then you can change the dataset into PyTorch format dataset, and view the first sample (Don't forget to reorder the keys): ```python quakeflow_nc = datasets.load_dataset("AI4EPS/quakeflow_nc", split="test", name="event_test") # for PyTorch DataLoader, we need to divide the dataset into several shards num_workers=4 quakeflow_nc = quakeflow_nc.to_iterable_dataset(num_shards=num_workers) quakeflow_nc = quakeflow_nc.map(lambda x: {key: torch.from_numpy(np.array(value, dtype=np.float32)) for key, value in x.items()}) try: isinstance(quakeflow_nc, torch.utils.data.IterableDataset) except: raise Exception("quakeflow_nc is not an IterableDataset") # print the first sample of the iterable dataset for example in quakeflow_nc: print("\nIterable test\n") print(example.keys()) for key in example.keys(): print(key, example[key].shape, example[key].dtype) break dataloader = DataLoader(quakeflow_nc, batch_size=1, num_workers=num_workers) for batch in dataloader: print("\nDataloader test\n") print(batch.keys()) for key in batch.keys(): print(key, batch[key].shape, batch[key].dtype) break ```
slushily/autotrain-data-hannah-jpg-test
--- task_categories: - image-classification --- # AutoTrain Dataset for project: hannah-jpg-test ## Dataset Description This dataset has been automatically processed by AutoTrain for project hannah-jpg-test. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<256x256 RGB PIL image>", "target": 0 }, { "image": "<256x256 RGB PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['hannah'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 7 | | valid | 7 |
gryffindor-ISWS/1500_dbp_abs_withoutIMG
--- license: gpl-3.0 language: - en tags: - art size_categories: - 1K<n<10K ---
adityarra07/czech_train_data
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: id dtype: string splits: - name: train num_bytes: 669027003.0330192 num_examples: 12613 - name: test num_bytes: 26521327.322326932 num_examples: 500 download_size: 658874865 dataset_size: 695548330.3553461 --- # Dataset Card for "czech_train_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)