Datasets:

librarian-bots

/

dataset_cards_with_metadata

like 11

pe-nlp/ov-kit-files-filtered-dedup-v2

pe-nlp

--- dataset_info: features: - name: file_path dtype: string - name: content dtype: string - name: size dtype: int64 - name: lang dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphanum_fraction dtype: float64 splits: - name: train num_bytes: 131808984.32157095 num_examples: 13422 download_size: 41164163 dataset_size: 131808984.32157095 configs: - config_name: default data_files: - split: train path: data/train-* ---

Navi2184/training

Navi2184

--- license: llama3 ---

arjunshajitech/sample_dataset_ml

arjunshajitech

--- dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train num_bytes: 121879.0 num_examples: 1 download_size: 125034 dataset_size: 121879.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.21_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string splits: - name: train num_bytes: 2100525 num_examples: 5000 download_size: 998537 dataset_size: 2100525 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.22_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2459572 num_examples: 5000 download_size: 964261 dataset_size: 2459572 configs: - config_name: default data_files: - split: train path: data/train-* ---

procit005/final_2

procit005

--- dataset_info: features: - name: train struct: - name: dimension struct: - name: length dtype: float64 - name: width dtype: float64 - name: occupied_space dtype: string - name: time dtype: string - name: type dtype: string splits: - name: train num_bytes: 1141 num_examples: 24 - name: test num_bytes: 145 num_examples: 3 - name: eval num_bytes: 145 num_examples: 3 download_size: 9481 dataset_size: 1431 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: eval path: data/eval-* ---

Huggmachas/Humor_dataset

Huggmachas

--- dataset_info: features: - name: Text dtype: string - name: Lang dtype: string - name: Label dtype: int64 splits: - name: train num_bytes: 500764 num_examples: 3418 download_size: 281608 dataset_size: 500764 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.23_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2447473 num_examples: 5000 download_size: 956394 dataset_size: 2447473 configs: - config_name: default data_files: - split: train path: data/train-* ---

IntellyaDS/wikipedia-23-11-sr

IntellyaDS

--- license: apache-2.0 ---

rakshya34/filtered_voice_english_v1.24_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2469311 num_examples: 5000 download_size: 966815 dataset_size: 2469311 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.25_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2492519 num_examples: 5000 download_size: 982526 dataset_size: 2492519 configs: - config_name: default data_files: - split: train path: data/train-* ---

MusketCat/CyberMysticism

MusketCat

--- license: apache-2.0 ---

rakshya34/filtered_voice_english_v1.27_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string splits: - name: train num_bytes: 2077426 num_examples: 5000 download_size: 980434 dataset_size: 2077426 configs: - config_name: default data_files: - split: train path: data/train-* ---

Huggmachas/Hate_Dataset

Huggmachas

--- dataset_info: features: - name: id dtype: int64 - name: id_str dtype: string - name: full_text dtype: string - name: label dtype: string splits: - name: train num_bytes: 562324 num_examples: 2940 download_size: 392059 dataset_size: 562324 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.28_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2481963 num_examples: 5000 download_size: 965757 dataset_size: 2481963 configs: - config_name: default data_files: - split: train path: data/train-* ---

CHHHH/M3AV_v1.0

CHHHH

--- license: cc-by-nc-sa-4.0 --- Project website: https://jack-zc8.github.io/M3AV-dataset-page/

rakshya34/filtered_voice_english_v1.29_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2449310 num_examples: 5000 download_size: 955370 dataset_size: 2449310 configs: - config_name: default data_files: - split: train path: data/train-* ---

Angel55/sample_spider_data

Angel55

--- dataset_info: features: - name: text dtype: string splits: - name: test num_bytes: 1976728 num_examples: 969 download_size: 190517 dataset_size: 1976728 configs: - config_name: default data_files: - split: test path: data/test-* ---

rakshya34/filtered_voice_english_v1.30_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2465265 num_examples: 5000 download_size: 960745 dataset_size: 2465265 configs: - config_name: default data_files: - split: train path: data/train-* ---

theodorr/mls_10k_eng_encodec

theodorr

--- dataset_info: features: - name: audio dtype: audio - name: original_path dtype: string - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: transcript dtype: string - name: audio_duration dtype: float64 - name: speaker_id dtype: string - name: book_id dtype: string - name: audio_token sequence: sequence: sequence: sequence: int64 splits: - name: train num_bytes: 244934398940.205 num_examples: 2420047 download_size: 171168727024 dataset_size: 244934398940.205 configs: - config_name: default data_files: - split: train path: data/train-* ---

deboleen6/telugu_tts

deboleen6

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 splits: - name: train num_bytes: 603114413.488 num_examples: 4448 download_size: 466010970 dataset_size: 603114413.488 configs: - config_name: default data_files: - split: train path: data/train-* ---

CAGV/marcus

CAGV

--- license: openrail ---

rakshya34/filtered_voice_english_v1.31_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2375915 num_examples: 5000 download_size: 890882 dataset_size: 2375915 configs: - config_name: default data_files: - split: train path: data/train-* ---

baby88888888/666666

baby88888888

--- license: apache-2.0 ---

Angel55/sample_data

Angel55

--- dataset_info: features: - name: text dtype: string splits: - name: test num_bytes: 6053 num_examples: 5 download_size: 8850 dataset_size: 6053 configs: - config_name: default data_files: - split: test path: data/test-* ---

rakshya34/filtered_voice_english_v1.32_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2405183 num_examples: 5000 download_size: 918773 dataset_size: 2405183 configs: - config_name: default data_files: - split: train path: data/train-* ---

bloomdata/wikisql

bloomdata

--- license: apache-2.0 ---

rakshya34/filtered_voice_english_v1.33_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2477280 num_examples: 5000 download_size: 967960 dataset_size: 2477280 configs: - config_name: default data_files: - split: train path: data/train-* ---

bloomdata/spider

bloomdata

--- license: apache-2.0 ---

rakshya34/filtered_voice_english_v1.35_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string splits: - name: train num_bytes: 2091394 num_examples: 5000 download_size: 986698 dataset_size: 2091394 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.36_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2462261 num_examples: 5000 download_size: 969379 dataset_size: 2462261 configs: - config_name: default data_files: - split: train path: data/train-* ---

CVPR2024/CVPR2024-papers-abstract-index

CVPR2024

--- license: apache-2.0 ---

RochatAI/ShareGPT-COIG

RochatAI

--- license: apache-2.0 dataset_info: features: - name: messages list: - name: from dtype: string - name: value dtype: string - name: system dtype: string splits: - name: train num_bytes: 281928865 num_examples: 275985 download_size: 127001629 dataset_size: 281928865 configs: - config_name: default data_files: - split: train path: data/train-* --- The original data is from https://huggingface.co/datasets/BAAI/COIG. Format the original data to ShareGPT format.

DT4LM/sst2_adversarial_training

DT4LM

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 361083.9155290884 num_examples: 5457 download_size: 227440 dataset_size: 361083.9155290884 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.37_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string splits: - name: train num_bytes: 2463033 num_examples: 5000 download_size: 976268 dataset_size: 2463033 configs: - config_name: default data_files: - split: train path: data/train-* ---

mesolitica/google-image-malaysia-location-dedup

mesolitica

--- task_categories: - image-feature-extraction --- # Google Image Malaysia Location Dedup Original dataset https://huggingface.co/datasets/malaysia-ai/crawl-google-image-malaysia-location Source code at https://github.com/mesolitica/malaysian-dataset/tree/master/vlm/dedup-malaysia-location ## Dedup 50% similar [dedup-0.5.jsonl](dedup-0.5.jsonl), total deduped 227937 images, ``` {'filename': 'train-00812-of-01000.parquet', 'keyword': 'Taman Megah Jaya Ayer Tawar', 'no': 16, 'selected_indices': [2556, 2559, 2575, 2577, 2586, 2587, 2595]} ``` ## Dedup 60% similar [dedup-0.6.jsonl](dedup-0.6.jsonl), total deduped 487301 images, ``` {'filename': 'train-00404-of-01000.parquet', 'keyword': 'Kampung Tok Wan Nik Padang Besar', 'no': 92, 'selected_indices': [2100, 2102, 2103, 2104]} ``` - `filename` is the parquet file from the original repository. - `selected_indices` is the index of dataframe of that filename. ## Embedding We convert to embedding using https://huggingface.co/google/siglip-base-patch16-512, we use MosaicML for faster indexing, ```python from streaming import MDSWriter from streaming.base.format.mds.encodings import Encoding, _encodings from streaming import LocalDataset import streaming import numpy as np from tqdm import tqdm class Float32(Encoding): def encode(self, obj) -> bytes: return obj.tobytes() def decode(self, data: bytes): return np.frombuffer(data, np.float32) _encodings['float32'] = Float32 dataset = LocalDataset('embedding') ```

Kuvvi/dataandmodel

Kuvvi

--- license: apache-2.0 ---

rakshya34/filtered_voice_english_v1.38_tag

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string splits: - name: train num_bytes: 1980168 num_examples: 5000 download_size: 903378 dataset_size: 1980168 configs: - config_name: default data_files: - split: train path: data/train-* ---

DuongTrongChi/thanos

DuongTrongChi

--- dataset_info: features: - name: category dtype: string - name: category_id dtype: int64 - name: prompt dtype: string splits: - name: train num_bytes: 114839.29571428572 num_examples: 631 download_size: 48916 dataset_size: 114839.29571428572 configs: - config_name: default data_files: - split: train path: data/train-* ---

anonydass/finetuning_demo

anonydass

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 2045 num_examples: 4 download_size: 3386 dataset_size: 2045 configs: - config_name: default data_files: - split: train path: data/train-* ---

philipp-zettl/GGU-xx

philipp-zettl

--- dataset_info: features: - name: sample dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 39382 num_examples: 1049 download_size: 20576 dataset_size: 39382 configs: - config_name: default data_files: - split: train path: data/train-* ---

deboleen6/telugu_OpenSLR

deboleen6

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: string splits: - name: train num_bytes: 603118861.488 num_examples: 4448 download_size: 466007876 dataset_size: 603118861.488 configs: - config_name: default data_files: - split: train path: data/train-* ---

TerminatorJ/PDB_Ribonanzanet

TerminatorJ

--- license: mit configs: - config_name: default data_files: - split: train path: train.csv - split: test path: test.csv - split: validation path: val.csv ---

DokHee/FT

DokHee

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 4734 num_examples: 10 download_size: 6824 dataset_size: 4734 configs: - config_name: default data_files: - split: train path: data/train-* ---

SenseLLM/ReflectionSeq-DS

SenseLLM

--- license: apache-2.0 task_categories: - text-generation language: - en tags: - code pretty_name: reflection_seq_for_code_generation size_categories: - 10K<n<100K --- ## ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation <p align="center"> <a href="https://arxiv.org/abs/2405.17057">📄 Paper</a> • <a href="https://github.com/SenseLLM/ReflectionCoder">🏠 Repo</a> • <a href="https://huggingface.co/SenseLLM/ReflectionCoder-DS-33B">🤖 Models</a> • <a href="https://huggingface.co/datasets/SenseLLM/ReflectionSeq-GPT">📚 Datasets </a> </p> ## Introduction ReflectionCoder is a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance. Please refer to our paper and repo for more details!  <hr> ## Models | Model | Checkpoint | Size | HumanEval (+) | MBPP (+) | License| |:-------|:------------|:------|:---------------|:----------|:--------| | ReflectionCoder-CL-7B | 🤗 [HF Link](https://huggingface.co/SenseLM/ReflectionCoder-CL-7B) | 7B | 75.0 (68.9) | 72.2 (61.4) | [Llama2](https://ai.meta.com/llama/license/) | | ReflectionCoder-CL-34B | 🤗 [HF Link](https://huggingface.co/SenseLM/ReflectionCoder-CL-34B) | 34B | 70.7 (66.5) | 68.4 (56.6) | [Llama2](https://ai.meta.com/llama/license/) | | ReflectionCoder-DS-6.7B | 🤗 [HF Link](https://huggingface.co/SenseLM/ReflectionCoder-DS-6.7B) | 6.7B | 80.5 (74.4) | 81.5 (69.6) | [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) | | ReflectionCoder-DS-33B | 🤗 [HF Link](https://huggingface.co/SenseLM/ReflectionCoder-DS-33B) | 33B | 82.9 (76.8) | 84.1 (72.0) | [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) | ## Datasets | Dataset | Link | License | |:-------------------|:----------------|:----------------------------------------------| | ReflectionSeq-GPT | 🤗 [HF Link](https://huggingface.co/datasets/SenseLM/ReflectionSeq-GPT) | [License](LICENSE) | | ReflectionSeq-DS | 🤗 [HF Link](https://huggingface.co/datasets/SenseLM/ReflectionSeq-DS) | [License](LICENSE) | ## How to Use #### Chat Format Following chat templates of most models, we use two special tokens to wrap the message of user and assistant, *i.e.*, ``<|user|>``, ``<|assistant|>``, and ``<|endofmessage|>``. Furthermore, we use two special tokens to wrap the content of different blocks, *i.e.*, ``<|text|>`` and ``<|endofblock|>``. You can use the following template to prompt our ReflectionCoder. ```python <|user|><|text|> Your Instruction <|endofblock|><|endofmessage|><|assistant|> ``` #### Inference Code Please refer to our GitHub Repo [need update](https://ai.meta.com/llama/license/) for more technical details. ## Citation If you find this repo useful for your research, please kindly cite our paper: ``` @misc{ren2024reflectioncoder, title={ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation}, author={Houxing Ren and Mingjie Zhan and Zhongyuan Wu and Aojun Zhou and Junting Pan and Hongsheng Li}, year={2024}, eprint={2405.17057}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgments We thank the following amazing projects that truly inspired us: - [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) - [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder) - [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder) - [Evol-CodeAlpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) - [MagiCoder](https://github.com/ise-uiuc/magicoder/tree/main) - [EvalPlus](https://github.com/evalplus/evalplus) - [OpenCoderInterpreter](https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/tree/main)

mfrx/electrocity

mfrx

--- license: apache-2.0 ---

realbenpope/PERSUADE_manageable

realbenpope

--- license: mit task_categories: - text-classification - feature-extraction - zero-shot-classification language: - en pretty_name: PERSUADE manageable tags: - education - writing size_categories: - 100K<n<1M --- # PERSUADE_manageable This is a more space efficient version of the [PERSUADE dataset](https://the-learning-agency-lab.com/learning-exchange/persuade-dataset/) with the full_text split into a second file, reducing it from 750mb to 130mb Everything else remains the same.

Magneto/CustomPhi3VisionData_v3

Magneto

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: pixel_values sequence: sequence: sequence: sequence: float64 splits: - name: train num_bytes: 122737772355 num_examples: 2660 download_size: 20100863583 dataset_size: 122737772355 configs: - config_name: default data_files: - split: train path: data/train-* ---

gingercake01/240527STT_audio_noise_2930samples

gingercake01

--- license: mit dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 2251399480 num_examples: 2344 - name: test num_bytes: 281425736 num_examples: 293 - name: valid num_bytes: 281424000 num_examples: 293 download_size: 728396086 dataset_size: 2814249216 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

opelumen/tai_0001

opelumen

--- license: mit ---

fine-tuned/BAAI_bge-m3-27052024-w9t8-webapp

fine-tuned

--- license: apache-2.0 task_categories: - feature-extraction - sentence-similarity language: - fr tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - Intimate - Care - Hygiene - Products - Health pretty_name: e-commerce search for intimate care products size_categories: - n<1K --- # BAAI_bge-m3-27052024-w9t8-webapp Dataset ## Dataset Description The dataset "e-commerce search for intimate care products" is a generated dataset designed to support the development of domain specific embedding models for retrieval tasks. ## Associated Model This dataset was used to train the [**BAAI_bge-m3-27052024-w9t8-webapp**](https://huggingface.co/fine-tuned/BAAI_bge-m3-27052024-w9t8-webapp) model. ## How to Use To use this dataset for model training or evaluation, you can load it using the Hugging Face `datasets` library as follows: ```python from datasets import load_dataset dataset = load_dataset("fine-tuned/BAAI_bge-m3-27052024-w9t8-webapp") print(dataset['test'][0]) ```

llmem/conversation_alpha_01

llmem

--- dataset_info: features: - name: conversation dtype: string - name: session dtype: int64 splits: - name: train num_bytes: 25375 num_examples: 9 download_size: 18291 dataset_size: 25375 configs: - config_name: default data_files: - split: train path: data/train-* ---

Yukify/finetune-llama3-for-smp-regpart-3-shot

Yukify

--- license: apache-2.0 ---

MAPS-research/GelRec_images_1

MAPS-research

--- dataset_info: features: - name: image dtype: image - name: base_model dtype: string - name: model_id dtype: float64 - name: model_name dtype: string - name: modelVersion_id dtype: int64 - name: modelVersion_name dtype: string - name: lora_scale dtype: float64 - name: prompt_id dtype: int64 - name: prompt dtype: string - name: negative_prompt dtype: string - name: seed dtype: int64 splits: - name: train num_bytes: 10894595130.6 num_examples: 8040 download_size: 11006254884 dataset_size: 10894595130.6 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-image language: - en --- **Images Generated by Different LoRA and Prompts** - **Prompt IDs**: 2, 16, 42, 51 - **Number of Models per Prompt**: 10 - **Number of Images per Model**: 201 - **Seed Range**: [0, 500, 10000, ..., 100000] - **Base Model**: SDXL 1.0

QHQK/conversation_hall_completion_v2

QHQK

--- dataset_info: features: - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: choice_a dtype: string - name: choice_b dtype: string - name: choice_c dtype: string - name: choice_d dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: test num_bytes: 93269630.25 num_examples: 1270 download_size: 38010954 dataset_size: 93269630.25 configs: - config_name: default data_files: - split: test path: data/test-* ---

dimitarpg13/DIS5K

dimitarpg13

--- license: openrail ---

alexandreteles/fama_fraternitatis_multiturn

alexandreteles

--- license: c-uda dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 233932 num_examples: 112 download_size: 74302 dataset_size: 233932 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-generation language: - en tags: - spirituality - occultism - esoterism pretty_name: Fama Fraternitatis Rosae Crucis (multiturn) size_categories: - n<1K --- # Fama Fraternitatis Rosae Crucis Multiturn Conversation Dataset ## Overview This dataset consists of structured multiturn conversations modeled around the esoteric and philosophical themes of the "Fama Fraternitatis." The text, known for its deep allegorical content, serves as the foundation for generating dialogues that involve rigorous inquiry into the occult and philosophical. ## Objective The primary objective of this dataset is to facilitate the development and testing of AI models specialized in understanding and generating responses grounded in esoteric philosophy. By providing a rich set of dialogues based on the "Fama Fraternitatis," the dataset aims to promote deeper intellectual engagement with historic philosophical texts and their contemporary relevance in esoteric studies. ## Access The dataset is available under the [c-uda](https://github.com/microsoft/Computational-Use-of-Data-Agreement/blob/master/C-UDA-1.0.md) license, intended for educational and research purposes.. ## Contribution Contributions to the dataset are welcome, especially those that expand the depth and range of philosophical inquiries.

mogoi/re_01_12

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 211177 num_examples: 1950 download_size: 14273 dataset_size: 211177 configs: - config_name: default data_files: - split: train path: data/train-* ---

mogoi/re_01_34

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 218985 num_examples: 1950 download_size: 17681 dataset_size: 218985 configs: - config_name: default data_files: - split: train path: data/train-* ---

mogoi/re_03_12

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 222694 num_examples: 1950 download_size: 22232 dataset_size: 222694 configs: - config_name: default data_files: - split: train path: data/train-* ---

mogoi/re_03_345

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 332241 num_examples: 2850 download_size: 32900 dataset_size: 332241 configs: - config_name: default data_files: - split: train path: data/train-* ---

coatedincrimson/LeedoONEUS

coatedincrimson

--- license: openrail ---

Farjfar/SGS-ukpolitics-train

Farjfar

--- dataset_info: features: - name: body_cleaned dtype: string - name: id dtype: string - name: subreddit dtype: string - name: year dtype: int64 - name: annotation1 dtype: string - name: annotation2 dtype: string - name: gold_annotation dtype: string - name: bio_annotation sequence: string - name: ids sequence: int64 - name: tokenized_body_cleaned sequence: string - name: continuation dtype: string - name: first_two_sentences dtype: string - name: continuation_tokenized sequence: string - name: tokenized_length dtype: int64 - name: ids_continuation sequence: int64 splits: - name: train num_bytes: 2564526 num_examples: 401 download_size: 900565 dataset_size: 2564526 configs: - config_name: default data_files: - split: train path: data/train-* ---

mogoi/re_06_13

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 185521 num_examples: 1650 download_size: 16301 dataset_size: 185521 configs: - config_name: default data_files: - split: train path: data/train-* ---

mogoi/re_06_2

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 118638 num_examples: 1050 download_size: 13424 dataset_size: 118638 configs: - config_name: default data_files: - split: train path: data/train-* ---

mogoi/re_08_12

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 178965 num_examples: 1650 download_size: 16447 dataset_size: 178965 configs: - config_name: default data_files: - split: train path: data/train-* ---

mogoi/re_08_34

mogoi

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 222176 num_examples: 1950 download_size: 19822 dataset_size: 222176 configs: - config_name: default data_files: - split: train path: data/train-* ---

QHQK/description_hall_completion_v2

QHQK

--- dataset_info: features: - name: question_id dtype: string - name: question dtype: string - name: answer dtype: string - name: choice_a dtype: string - name: choice_b dtype: string - name: choice_c dtype: string - name: choice_d dtype: string - name: image_source dtype: string - name: image dtype: image - name: category dtype: string splits: - name: test num_bytes: 70464191.0 num_examples: 879 download_size: 44010036 dataset_size: 70464191.0 configs: - config_name: default data_files: - split: test path: data/test-* ---

mbiarreta/MariposasClasif

mbiarreta

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Anartia jatrophae '1': Caligo atreus '2': Danaus plexippus '3': Dryas iulia '4': Siproeta stelenes splits: - name: train num_bytes: 194810357.178 num_examples: 1842 - name: validation num_bytes: 22775122.0 num_examples: 208 - name: test num_bytes: 54352736.0 num_examples: 515 download_size: 273069690 dataset_size: 271938215.178 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

ttgroup/blueneg-release

ttgroup

--- license: other license_name: license.txt license_link: LICENSE tags: - negative film - printed photo - high dynamic range - ultra-high resolution - image restoration pretty_name: BlueNeg Dataset --- # BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration ## Description This set of images is collected for studying the research problem of restoring the corrupted negative films. Due to the physical nature of negative film, the red, green, and blue light-sensitive layers are located differently inside the negative film. Therefore, the rates of deterioration of these three layers are different. Tien-Tsin Wong found that the blue channel is relatively vulnerable compared to the other two channels. Because the blue light-sensitive layer is on the outermost layer on the emulsion side. That is, the deterioration rates are heterogeneous. This characteristic is inherited from its ancestor, i.e., storing the three color negatives (R, G, B) on three separate glass plates by Prokudin-Gorsky. Since the blue channel is more vulnerable and the other two channels are relatively well-preserved, this means we can restore the blue channel by exploiting the retained information from the red and green channels to restore the color photograph. This is especially sound with the latest AI technologies. Unfortunately, most existing photo restoration techniques are developed based on printed photographs, in which the nature of deterioration is different from that of the negatives. This is why this dataset is created. ## Data Information <table style="border-collapse:collapse;border-spacing:0" class="tg"><thead> <tr><th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Data Source</th><th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Partition</th><th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Number</th><th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Folder</th> <th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Bits per channel</th><th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Resolution (may varies)</th><th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">File extension</th><th style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:bold;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Public</th></tr> </thead> <tbody> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">Negative films</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-corrupted</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">297</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">negative-16bit</td> <td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">16</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">10,128 x 6,840</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">dng</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">Yes</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-intact</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">194</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">Printed photo</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-corrupted</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">247</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">printed-16bit</td> <td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">16</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">2,994 x 1,920</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">tif</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">Yes</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-intact</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">151</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">Negative preview (after negation)</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-corrupted</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">297</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">negative-preview-8bit</td> <td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">8</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">1,322 x 892</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">preview.png</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal" rowspan="2">Yes</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-intact</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">194</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Pseudo ground truth</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-corrupted</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">247</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">pseudogt-8bit</td> <td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">8</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">&lt; 1,322 x 892</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">pseudogt.png</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Yes</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Printed photo (testset)</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-corrupted</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">30</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">-</td> <td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">16</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">2,994 x 1,920</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">tif</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">No</td></tr> <tr><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">Pseudo ground truth (testset)</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">blue-corrupted</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">30</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">-</td> <td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">8</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">&lt; 1,322 x 892</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">pseudogt.png</td><td style="border-color:inherit;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;text-align:center;vertical-align:top;word-break:normal">No</td></tr> </tbody></table> ## Meta Information The meta information is stored in the `meta.json` and `transformations.pkl`. The meta information includes the following fields: - `meta.json` - filename - partition - is_testset - date - roll_id - number_in_roll - location - geo_location - film_type - negative_path - printed_path - preview_path - pseudogt_path - scene_property - is_indoor - is_daytime - `transformations.pkl`: a python dict using filename (without extension) as key, and the value is a dict with the following fields: - matrix: the perspective projection matrix to warp the printed photo to the negative preview - bbox: the bounding box of the warped printed photo in the negative preview; the bounding box is in the format of [x0, y0, x1, y1], preview[y0:y1, x0:x1, :] has the same size as pseudo ground truth ## Naming Convention `YYYYMMDD[R]-NN-NAME.EXT` - `YYYYMMDD`: The date when the photo was taken (might not always be accurate, but roughly correct). - `[R]`: the roll number, if available - `NN`: the photo number in the roll - `NAME`: the photo's name, usually the location or the subject of the photo. - `EXT`: the file extension, e.g., `.dng`, `.tif`, `.preview.png`, `.pseudogt.png`, etc. ## License Our license shares the same spirit with the [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). However, please note that our license is not CC-BY 4.0. Please check the LICENSE file for more details. ``` All photos are owned and copyrighted by Tien-Tsin Wong. You are automatically granted with permission to use the images for academic and commerical usages, provided that the image credit "Copyrighted by Tien-Tsin Wong" is included in any forms of publication, reproduction, redistribution, or derivatives of the images. ``` ## Citation If you find the dataset is useful to you, please also cite our publication below in your work/publication. - Hanyuan Liu, Chengze Li, Minshan Xie, Zhenni Wang, Jiawen Liang, Chi-Sing Leung, and Tien-Tsin Wong, "BlueNeg: BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration", arXiv preprint, 2024. ```bibtex @misc{blueneg, title={BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration}, author={Hanyuan Liu and Chengze Li and Minshan Xie and Zhenni Wang and Jiawen Liang and Chi-Sing Leung and and Tien-Tsin Wong}, year={2024}, eprint={to be updated}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

rexching/dataset_0001

rexching

--- dataset_info: features: [] splits: - name: train num_bytes: 0 num_examples: 0 download_size: 324 dataset_size: 0 configs: - config_name: default data_files: - split: train path: data/train-* ---

haixuantao/just_gripper_eval_3

haixuantao

--- license: mit dataset_info: features: - name: index dtype: int64 - name: observation.images.cam_right_wrist dtype: video_frame - name: observation.images.cam_high dtype: video_frame - name: observation.state sequence: float32 length: 14 - name: observation.images.cam_left_wrist dtype: video_frame - name: action sequence: float32 length: 14 - name: episode_index dtype: int64 - name: observation.images.cam_low dtype: video_frame - name: frame_index dtype: int64 - name: next.done dtype: bool - name: timestamp dtype: float32 splits: - name: train num_bytes: 475350 num_examples: 1200 download_size: 162171 dataset_size: 475350 configs: - config_name: default data_files: - split: train path: data/train-* ---

ByungJoo/custom_dataset

ByungJoo

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1908 num_examples: 16 download_size: 1771 dataset_size: 1908 configs: - config_name: default data_files: - split: train path: data/train-* ---

NCube/europa-random-split

NCube

--- language: - fr - de - en - it - nl - el - da - pt - es - sv - fi - lt - et - cs - hu - lv - sl - pl - mt - sk - ro - bg - hr - ga license: mit size_categories: - 100K<n<1M pretty_name: Europa Random Split dataset_info: features: - name: celex_id dtype: string - name: lang dtype: string - name: input_text dtype: string - name: keyphrases sequence: string splits: - name: train num_bytes: 6405779590 num_examples: 159306 - name: valid num_bytes: 2182262528 num_examples: 53943 - name: test num_bytes: 2853853947 num_examples: 71708 download_size: 5183354316 dataset_size: 11441896065 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* tags: - keyphrase-generation - text-to-text - legal --- # Dataset Card for EUROPA This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description EUROPA is a dataset designed for training and evaluating multilingual keyphrase generation models in the legal domain. It consists of legal judgments from the Court of Justice of the European Union (EU) and includes instances in all 24 official EU languages. **Key Features**: **Multilingual:** Covers 24 official EU languages. **Domain-Specific:** Focuses on legal documents. **Source:** Derived from Court of Justice of the European Union judgments. - **Curated by:** N3 team - **Languages:** French, German, English, Italian, Dutch, Greek, Danish, Portuguese, Spanish, Swedish, Finnish, Lithuanian, Estonian, Czech, Hungarian, Latvian, Slovenian, Polish, Maltese, Slovak, Romanian, Bulgarian, Croatian, Irish - **License:** MIT License ### Dataset Sources - **Paper:** https://arxiv.org/abs/2403.00252 ## Dataset Structure - **celex_id:** CELEX identifier inherited from CJEU. Different translated versions of the same judgment share the same celex_id. If you wish to have a unique identifier for each instance, you can concatenate `lang` and `celex_id` values; - **lang:** ISO 639-1 language code; - **input:** judgment transcription or translation; - **keyphrases:** reference keyphrases drafted by the CJEU. This page presents an randomly split version of the dataset, thus allowing all sets to have the same distribution in terms of vocabulary and languages. More details can be found in Appendix H of our paper. - **training set**: 159 306 instances; - **validation set**: 53 943 instances; - **test set**: 71 708 instances. ## Citation ``` @article{salaun2024europa, title={EUROPA: A Legal Multilingual Keyphrase Generation Dataset}, author={Sala{\"u}n, Olivier and Piedboeuf, Fr{\'e}d{\'e}ric and Le Berre, Guillaume and Hermelo, David Alfonso and Langlais, Philippe}, journal={arXiv preprint arXiv:2403.00252}, year={2024} } ```

nhyha/QA-Dataset

nhyha

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 20409 num_examples: 30 download_size: 14672 dataset_size: 20409 configs: - config_name: default data_files: - split: train path: data/train-* ---

gurdeep101/mini-platypus

gurdeep101

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245921 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details Filtered version of mini platypus dataset created as a part of code along tutorial from databricks here https://www.datacamp.com/code-along/fine-tuning-your-own-llama-2-model ### 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]

alexandreteles/chymical_wedding_of_christian_rosenkreutz_multiturn

alexandreteles

--- license: c-uda dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 963095 num_examples: 528 download_size: 367520 dataset_size: 963095 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-generation language: - en tags: - spirituality - occultism - esoterism size_categories: - n<1K pretty_name: The Chymical Wedding of Christian Rosenkreutz (multiturn) --- # The Chymical Wedding of Christian Rosenkreutz Multiturn Conversation Dataset ## Overview This dataset consists of structured multiturn conversations modeled around the esoteric and philosophical themes of "The Chymical Wedding of Christian Rosenkreutz." The text, known for its deep allegorical content, serves as the foundation for generating dialogues that involve rigorous inquiry into the occult and philosophical. ## Objective The primary objective of this dataset is to facilitate the development and testing of AI models specialized in understanding and generating responses grounded in esoteric philosophy. By providing a rich set of dialogues based on "The Chymical Wedding of Christian Rosenkreutz," the dataset aims to promote deeper intellectual engagement with historic philosophical texts and their contemporary relevance in esoteric studies. ## Access The dataset is available under the [c-uda](https://github.com/microsoft/Computational-Use-of-Data-Agreement/blob/master/C-UDA-1.0.md) license, intended for educational and research purposes.. ## Contribution Contributions to the dataset are welcome, especially those that expand the depth and range of philosophical inquiries.

fresvyx/alpaca2-imam

fresvyx

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 2580358 num_examples: 1000 download_size: 1367091 dataset_size: 2580358 configs: - config_name: default data_files: - split: train path: data/train-* ---

OALL/details_Omartificial-Intelligence-Space__al-baka-llama3-8b-experimental

OALL

--- pretty_name: Evaluation run of Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental](https://huggingface.co/Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental).\n\ \nThe dataset is composed of 136 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.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\"OALL/details_Omartificial-Intelligence-Space__al-baka-llama3-8b-experimental\"\ ,\n\t\"lighteval_xstory_cloze_ar_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2024-05-27T14:06:39.187751](https://huggingface.co/datasets/OALL/details_Omartificial-Intelligence-Space__al-baka-llama3-8b-experimental/blob/main/results_2024-05-27T14-06-39.187751.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_norm\": 0.4253986985051422,\n\ \ \"acc_norm_stderr\": 0.0384347707626982,\n \"acc\": 0.5956320317670417,\n\ \ \"acc_stderr\": 0.012629580396570928\n },\n \"community|acva:Algeria|0\"\ : {\n \"acc_norm\": 0.5743589743589743,\n \"acc_norm_stderr\": 0.03549871080367707\n\ \ },\n \"community|acva:Ancient_Egypt|0\": {\n \"acc_norm\": 0.10158730158730159,\n\ \ \"acc_norm_stderr\": 0.01704876125499024\n },\n \"community|acva:Arab_Empire|0\"\ : {\n \"acc_norm\": 0.4377358490566038,\n \"acc_norm_stderr\": 0.030533338430467512\n\ \ },\n \"community|acva:Arabic_Architecture|0\": {\n \"acc_norm\":\ \ 0.47692307692307695,\n \"acc_norm_stderr\": 0.0358596530894741\n },\n\ \ \"community|acva:Arabic_Art|0\": {\n \"acc_norm\": 0.39487179487179486,\n\ \ \"acc_norm_stderr\": 0.03509545602262037\n },\n \"community|acva:Arabic_Astronomy|0\"\ : {\n \"acc_norm\": 0.49230769230769234,\n \"acc_norm_stderr\": 0.03589365940635213\n\ \ },\n \"community|acva:Arabic_Calligraphy|0\": {\n \"acc_norm\": 0.5294117647058824,\n\ \ \"acc_norm_stderr\": 0.03131846503821582\n },\n \"community|acva:Arabic_Ceremony|0\"\ : {\n \"acc_norm\": 0.5351351351351351,\n \"acc_norm_stderr\": 0.03676936950948699\n\ \ },\n \"community|acva:Arabic_Clothing|0\": {\n \"acc_norm\": 0.4717948717948718,\n\ \ \"acc_norm_stderr\": 0.035840746749208334\n },\n \"community|acva:Arabic_Culture|0\"\ : {\n \"acc_norm\": 0.2564102564102564,\n \"acc_norm_stderr\": 0.031349709942744934\n\ \ },\n \"community|acva:Arabic_Food|0\": {\n \"acc_norm\": 0.46153846153846156,\n\ \ \"acc_norm_stderr\": 0.03579154352544571\n },\n \"community|acva:Arabic_Funeral|0\"\ : {\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.050529115263991134\n\ \ },\n \"community|acva:Arabic_Geography|0\": {\n \"acc_norm\": 0.6344827586206897,\n\ \ \"acc_norm_stderr\": 0.04013124195424386\n },\n \"community|acva:Arabic_History|0\"\ : {\n \"acc_norm\": 0.3641025641025641,\n \"acc_norm_stderr\": 0.0345465386778639\n\ \ },\n \"community|acva:Arabic_Language_Origin|0\": {\n \"acc_norm\"\ : 0.6,\n \"acc_norm_stderr\": 0.050529115263991134\n },\n \"community|acva:Arabic_Literature|0\"\ : {\n \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n\ \ },\n \"community|acva:Arabic_Math|0\": {\n \"acc_norm\": 0.40512820512820513,\n\ \ \"acc_norm_stderr\": 0.03524577495610961\n },\n \"community|acva:Arabic_Medicine|0\"\ : {\n \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"community|acva:Arabic_Music|0\": {\n \"acc_norm\": 0.23741007194244604,\n\ \ \"acc_norm_stderr\": 0.036220593237998276\n },\n \"community|acva:Arabic_Ornament|0\"\ : {\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03384487217112065\n\ \ },\n \"community|acva:Arabic_Philosophy|0\": {\n \"acc_norm\": 0.5310344827586206,\n\ \ \"acc_norm_stderr\": 0.04158632762097828\n },\n \"community|acva:Arabic_Physics_and_Chemistry|0\"\ : {\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.03581804596782232\n\ \ },\n \"community|acva:Arabic_Wedding|0\": {\n \"acc_norm\": 0.6205128205128205,\n\ \ \"acc_norm_stderr\": 0.03483959266365359\n },\n \"community|acva:Bahrain|0\"\ : {\n \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.07309112127323451\n\ \ },\n \"community|acva:Comoros|0\": {\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.07491109582924915\n },\n \"community|acva:Egypt_modern|0\"\ : {\n \"acc_norm\": 0.5684210526315789,\n \"acc_norm_stderr\": 0.05108592673308946\n\ \ },\n \"community|acva:InfluenceFromAncientEgypt|0\": {\n \"acc_norm\"\ : 0.5384615384615384,\n \"acc_norm_stderr\": 0.0357915435254457\n },\n\ \ \"community|acva:InfluenceFromByzantium|0\": {\n \"acc_norm\": 0.7172413793103448,\n\ \ \"acc_norm_stderr\": 0.037528339580033376\n },\n \"community|acva:InfluenceFromChina|0\"\ : {\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.03174930436412669\n\ \ },\n \"community|acva:InfluenceFromGreece|0\": {\n \"acc_norm\":\ \ 0.6461538461538462,\n \"acc_norm_stderr\": 0.03433004254147036\n },\n\ \ \"community|acva:InfluenceFromIslam|0\": {\n \"acc_norm\": 0.31724137931034485,\n\ \ \"acc_norm_stderr\": 0.038783523721386215\n },\n \"community|acva:InfluenceFromPersia|0\"\ : {\n \"acc_norm\": 0.7028571428571428,\n \"acc_norm_stderr\": 0.03464507889884372\n\ \ },\n \"community|acva:InfluenceFromRome|0\": {\n \"acc_norm\": 0.5743589743589743,\n\ \ \"acc_norm_stderr\": 0.03549871080367708\n },\n \"community|acva:Iraq|0\"\ : {\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.0521414859075246\n\ \ },\n \"community|acva:Islam_Education|0\": {\n \"acc_norm\": 0.4564102564102564,\n\ \ \"acc_norm_stderr\": 0.03576123096991215\n },\n \"community|acva:Islam_branches_and_schools|0\"\ : {\n \"acc_norm\": 0.4857142857142857,\n \"acc_norm_stderr\": 0.03788942763158507\n\ \ },\n \"community|acva:Islamic_law_system|0\": {\n \"acc_norm\": 0.4307692307692308,\n\ \ \"acc_norm_stderr\": 0.0355521325205876\n },\n \"community|acva:Jordan|0\"\ : {\n \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.07309112127323451\n\ \ },\n \"community|acva:Kuwait|0\": {\n \"acc_norm\": 0.3111111111111111,\n\ \ \"acc_norm_stderr\": 0.06979205927323111\n },\n \"community|acva:Lebanon|0\"\ : {\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.06267511942419626\n\ \ },\n \"community|acva:Libya|0\": {\n \"acc_norm\": 0.5111111111111111,\n\ \ \"acc_norm_stderr\": 0.07535922203472523\n },\n \"community|acva:Mauritania|0\"\ : {\n \"acc_norm\": 0.4888888888888889,\n \"acc_norm_stderr\": 0.07535922203472523\n\ \ },\n \"community|acva:Mesopotamia_civilization|0\": {\n \"acc_norm\"\ : 0.5806451612903226,\n \"acc_norm_stderr\": 0.03976361630387024\n },\n\ \ \"community|acva:Morocco|0\": {\n \"acc_norm\": 0.37777777777777777,\n\ \ \"acc_norm_stderr\": 0.0730911212732345\n },\n \"community|acva:Oman|0\"\ : {\n \"acc_norm\": 0.4222222222222222,\n \"acc_norm_stderr\": 0.07446027270295806\n\ \ },\n \"community|acva:Palestine|0\": {\n \"acc_norm\": 0.3764705882352941,\n\ \ \"acc_norm_stderr\": 0.052863310306265295\n },\n \"community|acva:Qatar|0\"\ : {\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.07385489458759965\n\ \ },\n \"community|acva:Saudi_Arabia|0\": {\n \"acc_norm\": 0.5435897435897435,\n\ \ \"acc_norm_stderr\": 0.03576123096991214\n },\n \"community|acva:Somalia|0\"\ : {\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.07385489458759965\n\ \ },\n \"community|acva:Sudan|0\": {\n \"acc_norm\": 0.4,\n \ \ \"acc_norm_stderr\": 0.07385489458759965\n },\n \"community|acva:Syria|0\"\ : {\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.07385489458759964\n\ \ },\n \"community|acva:Tunisia|0\": {\n \"acc_norm\": 0.26666666666666666,\n\ \ \"acc_norm_stderr\": 0.06666666666666665\n },\n \"community|acva:United_Arab_Emirates|0\"\ : {\n \"acc_norm\": 0.4823529411764706,\n \"acc_norm_stderr\": 0.054520483406618934\n\ \ },\n \"community|acva:Yemen|0\": {\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.16666666666666666\n },\n \"community|acva:communication|0\"\ : {\n \"acc_norm\": 0.43131868131868134,\n \"acc_norm_stderr\": 0.02599443023962308\n\ \ },\n \"community|acva:computer_and_phone|0\": {\n \"acc_norm\": 0.45084745762711864,\n\ \ \"acc_norm_stderr\": 0.02901934773187137\n },\n \"community|acva:daily_life|0\"\ : {\n \"acc_norm\": 0.18694362017804153,\n \"acc_norm_stderr\": 0.021268948348414647\n\ \ },\n \"community|acva:entertainment|0\": {\n \"acc_norm\": 0.23389830508474577,\n\ \ \"acc_norm_stderr\": 0.024687839412166384\n },\n \"community|alghafa:mcq_exams_test_ar|0\"\ : {\n \"acc_norm\": 0.3303411131059246,\n \"acc_norm_stderr\": 0.01994668532793601\n\ \ },\n \"community|alghafa:meta_ar_dialects|0\": {\n \"acc_norm\":\ \ 0.3088044485634847,\n \"acc_norm_stderr\": 0.006290523225095885\n },\n\ \ \"community|alghafa:meta_ar_msa|0\": {\n \"acc_norm\": 0.3463687150837989,\n\ \ \"acc_norm_stderr\": 0.015913546784020117\n },\n \"community|alghafa:multiple_choice_facts_truefalse_balanced_task|0\"\ : {\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.05807730170189531\n\ \ },\n \"community|alghafa:multiple_choice_grounded_statement_soqal_task|0\"\ : {\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.04092881363092387\n\ \ },\n \"community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0\"\ : {\n \"acc_norm\": 0.4066666666666667,\n \"acc_norm_stderr\": 0.04024162665739063\n\ \ },\n \"community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0\"\ : {\n \"acc_norm\": 0.7697310819262039,\n \"acc_norm_stderr\": 0.004708744220539922\n\ \ },\n \"community|alghafa:multiple_choice_rating_sentiment_task|0\": {\n\ \ \"acc_norm\": 0.5296080066722268,\n \"acc_norm_stderr\": 0.006446869248966106\n\ \ },\n \"community|alghafa:multiple_choice_sentiment_task|0\": {\n \ \ \"acc_norm\": 0.3598837209302326,\n \"acc_norm_stderr\": 0.011576375321508726\n\ \ },\n \"community|arabic_exams|0\": {\n \"acc_norm\": 0.3780260707635009,\n\ \ \"acc_norm_stderr\": 0.020944238360403714\n },\n \"community|arabic_mmlu:abstract_algebra|0\"\ : {\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n\ \ },\n \"community|arabic_mmlu:anatomy|0\": {\n \"acc_norm\": 0.3851851851851852,\n\ \ \"acc_norm_stderr\": 0.042039210401562783\n },\n \"community|arabic_mmlu:astronomy|0\"\ : {\n \"acc_norm\": 0.4407894736842105,\n \"acc_norm_stderr\": 0.040403110624904356\n\ \ },\n \"community|arabic_mmlu:business_ethics|0\": {\n \"acc_norm\"\ : 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"community|arabic_mmlu:clinical_knowledge|0\"\ : {\n \"acc_norm\": 0.3886792452830189,\n \"acc_norm_stderr\": 0.03000048544867599\n\ \ },\n \"community|arabic_mmlu:college_biology|0\": {\n \"acc_norm\"\ : 0.3819444444444444,\n \"acc_norm_stderr\": 0.040629907841466674\n },\n\ \ \"community|arabic_mmlu:college_chemistry|0\": {\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"community|arabic_mmlu:college_computer_science|0\"\ : {\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n\ \ },\n \"community|arabic_mmlu:college_mathematics|0\": {\n \"acc_norm\"\ : 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"community|arabic_mmlu:college_medicine|0\"\ : {\n \"acc_norm\": 0.32947976878612717,\n \"acc_norm_stderr\": 0.03583901754736412\n\ \ },\n \"community|arabic_mmlu:college_physics|0\": {\n \"acc_norm\"\ : 0.27450980392156865,\n \"acc_norm_stderr\": 0.044405219061793254\n },\n\ \ \"community|arabic_mmlu:computer_security|0\": {\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"community|arabic_mmlu:conceptual_physics|0\"\ : {\n \"acc_norm\": 0.34893617021276596,\n \"acc_norm_stderr\": 0.031158522131357756\n\ \ },\n \"community|arabic_mmlu:econometrics|0\": {\n \"acc_norm\":\ \ 0.3508771929824561,\n \"acc_norm_stderr\": 0.044895393502706986\n },\n\ \ \"community|arabic_mmlu:electrical_engineering|0\": {\n \"acc_norm\"\ : 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n },\n\ \ \"community|arabic_mmlu:elementary_mathematics|0\": {\n \"acc_norm\"\ : 0.3333333333333333,\n \"acc_norm_stderr\": 0.024278568024307706\n },\n\ \ \"community|arabic_mmlu:formal_logic|0\": {\n \"acc_norm\": 0.31746031746031744,\n\ \ \"acc_norm_stderr\": 0.041634530313028585\n },\n \"community|arabic_mmlu:global_facts|0\"\ : {\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n\ \ },\n \"community|arabic_mmlu:high_school_biology|0\": {\n \"acc_norm\"\ : 0.3903225806451613,\n \"acc_norm_stderr\": 0.027751256636969576\n },\n\ \ \"community|arabic_mmlu:high_school_chemistry|0\": {\n \"acc_norm\"\ : 0.3645320197044335,\n \"acc_norm_stderr\": 0.0338640574606209\n },\n\ \ \"community|arabic_mmlu:high_school_computer_science|0\": {\n \"acc_norm\"\ : 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"community|arabic_mmlu:high_school_european_history|0\"\ : {\n \"acc_norm\": 0.23636363636363636,\n \"acc_norm_stderr\": 0.03317505930009179\n\ \ },\n \"community|arabic_mmlu:high_school_geography|0\": {\n \"acc_norm\"\ : 0.3838383838383838,\n \"acc_norm_stderr\": 0.03464881675016338\n },\n\ \ \"community|arabic_mmlu:high_school_government_and_politics|0\": {\n \ \ \"acc_norm\": 0.38860103626943004,\n \"acc_norm_stderr\": 0.03517739796373134\n\ \ },\n \"community|arabic_mmlu:high_school_macroeconomics|0\": {\n \ \ \"acc_norm\": 0.37435897435897436,\n \"acc_norm_stderr\": 0.024537591572830503\n\ \ },\n \"community|arabic_mmlu:high_school_mathematics|0\": {\n \"\ acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.027309140588230175\n\ \ },\n \"community|arabic_mmlu:high_school_microeconomics|0\": {\n \ \ \"acc_norm\": 0.3319327731092437,\n \"acc_norm_stderr\": 0.030588697013783663\n\ \ },\n \"community|arabic_mmlu:high_school_physics|0\": {\n \"acc_norm\"\ : 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526731\n },\n\ \ \"community|arabic_mmlu:high_school_psychology|0\": {\n \"acc_norm\"\ : 0.381651376146789,\n \"acc_norm_stderr\": 0.020828148517022606\n },\n\ \ \"community|arabic_mmlu:high_school_statistics|0\": {\n \"acc_norm\"\ : 0.24537037037037038,\n \"acc_norm_stderr\": 0.029346665094372937\n },\n\ \ \"community|arabic_mmlu:high_school_us_history|0\": {\n \"acc_norm\"\ : 0.27450980392156865,\n \"acc_norm_stderr\": 0.031321798030832904\n },\n\ \ \"community|arabic_mmlu:high_school_world_history|0\": {\n \"acc_norm\"\ : 0.34177215189873417,\n \"acc_norm_stderr\": 0.030874537537553617\n },\n\ \ \"community|arabic_mmlu:human_aging|0\": {\n \"acc_norm\": 0.3991031390134529,\n\ \ \"acc_norm_stderr\": 0.03286745312567961\n },\n \"community|arabic_mmlu:human_sexuality|0\"\ : {\n \"acc_norm\": 0.4198473282442748,\n \"acc_norm_stderr\": 0.04328577215262972\n\ \ },\n \"community|arabic_mmlu:international_law|0\": {\n \"acc_norm\"\ : 0.5785123966942148,\n \"acc_norm_stderr\": 0.04507732278775087\n },\n\ \ \"community|arabic_mmlu:jurisprudence|0\": {\n \"acc_norm\": 0.46296296296296297,\n\ \ \"acc_norm_stderr\": 0.04820403072760627\n },\n \"community|arabic_mmlu:logical_fallacies|0\"\ : {\n \"acc_norm\": 0.4723926380368098,\n \"acc_norm_stderr\": 0.039223782906109894\n\ \ },\n \"community|arabic_mmlu:machine_learning|0\": {\n \"acc_norm\"\ : 0.3482142857142857,\n \"acc_norm_stderr\": 0.04521829902833586\n },\n\ \ \"community|arabic_mmlu:management|0\": {\n \"acc_norm\": 0.4368932038834951,\n\ \ \"acc_norm_stderr\": 0.04911147107365777\n },\n \"community|arabic_mmlu:marketing|0\"\ : {\n \"acc_norm\": 0.5683760683760684,\n \"acc_norm_stderr\": 0.0324483553531149\n\ \ },\n \"community|arabic_mmlu:medical_genetics|0\": {\n \"acc_norm\"\ : 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"community|arabic_mmlu:miscellaneous|0\"\ : {\n \"acc_norm\": 0.4623243933588761,\n \"acc_norm_stderr\": 0.017829131764287184\n\ \ },\n \"community|arabic_mmlu:moral_disputes|0\": {\n \"acc_norm\"\ : 0.407514450867052,\n \"acc_norm_stderr\": 0.026454578146931505\n },\n\ \ \"community|arabic_mmlu:moral_scenarios|0\": {\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"community|arabic_mmlu:nutrition|0\"\ : {\n \"acc_norm\": 0.46405228758169936,\n \"acc_norm_stderr\": 0.028555827516528787\n\ \ },\n \"community|arabic_mmlu:philosophy|0\": {\n \"acc_norm\": 0.4212218649517685,\n\ \ \"acc_norm_stderr\": 0.028043399858210635\n },\n \"community|arabic_mmlu:prehistory|0\"\ : {\n \"acc_norm\": 0.4104938271604938,\n \"acc_norm_stderr\": 0.027371350925124764\n\ \ },\n \"community|arabic_mmlu:professional_accounting|0\": {\n \"\ acc_norm\": 0.2907801418439716,\n \"acc_norm_stderr\": 0.027090664368353178\n\ \ },\n \"community|arabic_mmlu:professional_law|0\": {\n \"acc_norm\"\ : 0.3109517601043025,\n \"acc_norm_stderr\": 0.011822252917799205\n },\n\ \ \"community|arabic_mmlu:professional_medicine|0\": {\n \"acc_norm\"\ : 0.21691176470588236,\n \"acc_norm_stderr\": 0.02503584522771127\n },\n\ \ \"community|arabic_mmlu:professional_psychology|0\": {\n \"acc_norm\"\ : 0.36764705882352944,\n \"acc_norm_stderr\": 0.019506291693954847\n },\n\ \ \"community|arabic_mmlu:public_relations|0\": {\n \"acc_norm\": 0.39090909090909093,\n\ \ \"acc_norm_stderr\": 0.04673752333670237\n },\n \"community|arabic_mmlu:security_studies|0\"\ : {\n \"acc_norm\": 0.4326530612244898,\n \"acc_norm_stderr\": 0.031717528240626645\n\ \ },\n \"community|arabic_mmlu:sociology|0\": {\n \"acc_norm\": 0.5621890547263682,\n\ \ \"acc_norm_stderr\": 0.035080801121998406\n },\n \"community|arabic_mmlu:us_foreign_policy|0\"\ : {\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n\ \ },\n \"community|arabic_mmlu:virology|0\": {\n \"acc_norm\": 0.41566265060240964,\n\ \ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"community|arabic_mmlu:world_religions|0\"\ : {\n \"acc_norm\": 0.4327485380116959,\n \"acc_norm_stderr\": 0.03799978644370608\n\ \ },\n \"community|arc_challenge_okapi_ar|0\": {\n \"acc_norm\": 0.36982758620689654,\n\ \ \"acc_norm_stderr\": 0.01418037210859747\n },\n \"community|arc_easy_ar|0\"\ : {\n \"acc_norm\": 0.41455160744500846,\n \"acc_norm_stderr\": 0.010134486532149578\n\ \ },\n \"community|boolq_ar|0\": {\n \"acc_norm\": 0.6220858895705521,\n\ \ \"acc_norm_stderr\": 0.00849336032152894\n },\n \"community|copa_ext_ar|0\"\ : {\n \"acc_norm\": 0.5666666666666667,\n \"acc_norm_stderr\": 0.05252667118728808\n\ \ },\n \"community|hellaswag_okapi_ar|0\": {\n \"acc_norm\": 0.26616508559590013,\n\ \ \"acc_norm_stderr\": 0.004615195054138359\n },\n \"community|openbook_qa_ext_ar|0\"\ : {\n \"acc_norm\": 0.4383838383838384,\n \"acc_norm_stderr\": 0.02232459513248414\n\ \ },\n \"community|piqa_ar|0\": {\n \"acc_norm\": 0.5575559192580469,\n\ \ \"acc_norm_stderr\": 0.011604078962074162\n },\n \"community|race_ar|0\"\ : {\n \"acc_norm\": 0.3590992087644553,\n \"acc_norm_stderr\": 0.006833878896866116\n\ \ },\n \"community|sciq_ar|0\": {\n \"acc_norm\": 0.4562814070351759,\n\ \ \"acc_norm_stderr\": 0.015798297434857358\n },\n \"community|toxigen_ar|0\"\ : {\n \"acc_norm\": 0.4320855614973262,\n \"acc_norm_stderr\": 0.01620887578524445\n\ \ },\n \"lighteval|xstory_cloze:ar|0\": {\n \"acc\": 0.5956320317670417,\n\ \ \"acc_stderr\": 0.012629580396570928\n },\n \"community|acva:_average|0\"\ : {\n \"acc_norm\": 0.45897910079456206,\n \"acc_norm_stderr\": 0.04763314008202572\n\ \ },\n \"community|alghafa:_average|0\": {\n \"acc_norm\": 0.454600416994282,\n\ \ \"acc_norm_stderr\": 0.02268116512425296\n },\n \"community|arabic_mmlu:_average|0\"\ : {\n \"acc_norm\": 0.3834369097890124,\n \"acc_norm_stderr\": 0.03575749811075187\n\ \ }\n}\n```" repo_url: https://huggingface.co/Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental configs: - config_name: community_acva_Algeria_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Algeria|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Algeria|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Ancient_Egypt_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Ancient_Egypt|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Ancient_Egypt|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arab_Empire_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arab_Empire|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arab_Empire|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Architecture_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Architecture|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Architecture|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Art_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Art|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Art|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Astronomy_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Astronomy|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Astronomy|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Calligraphy_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Calligraphy|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Calligraphy|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Ceremony_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Ceremony|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Ceremony|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Clothing_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Clothing|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Clothing|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Culture_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Culture|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Culture|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Food_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Food|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Food|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Funeral_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Funeral|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Funeral|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Geography_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Geography|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Geography|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_History_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_History|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_History|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Language_Origin_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Language_Origin|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Language_Origin|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Literature_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Literature|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Literature|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Math_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Math|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Math|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Medicine_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Medicine|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Medicine|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Music_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Music|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Music|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Ornament_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Ornament|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Ornament|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Philosophy_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Philosophy|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Philosophy|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Physics_and_Chemistry_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Physics_and_Chemistry|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Physics_and_Chemistry|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Arabic_Wedding_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Arabic_Wedding|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Arabic_Wedding|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Bahrain_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Bahrain|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Bahrain|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Comoros_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Comoros|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Comoros|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Egypt_modern_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Egypt_modern|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Egypt_modern|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_InfluenceFromAncientEgypt_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:InfluenceFromAncientEgypt|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromAncientEgypt|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_InfluenceFromByzantium_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:InfluenceFromByzantium|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromByzantium|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_InfluenceFromChina_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:InfluenceFromChina|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromChina|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_InfluenceFromGreece_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:InfluenceFromGreece|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromGreece|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_InfluenceFromIslam_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:InfluenceFromIslam|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromIslam|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_InfluenceFromPersia_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:InfluenceFromPersia|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromPersia|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_InfluenceFromRome_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:InfluenceFromRome|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromRome|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Iraq_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Iraq|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Iraq|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Islam_Education_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Islam_Education|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Islam_Education|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Islam_branches_and_schools_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Islam_branches_and_schools|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Islam_branches_and_schools|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Islamic_law_system_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Islamic_law_system|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Islamic_law_system|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Jordan_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Jordan|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Jordan|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Kuwait_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Kuwait|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Kuwait|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Lebanon_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Lebanon|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Lebanon|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Libya_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Libya|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Libya|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Mauritania_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Mauritania|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Mauritania|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Mesopotamia_civilization_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Mesopotamia_civilization|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Mesopotamia_civilization|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Morocco_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Morocco|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Morocco|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Oman_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Oman|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Oman|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Palestine_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Palestine|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Palestine|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Qatar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Qatar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Qatar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Saudi_Arabia_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Saudi_Arabia|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Saudi_Arabia|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Somalia_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Somalia|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Somalia|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Sudan_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Sudan|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Sudan|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Syria_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Syria|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Syria|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Tunisia_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Tunisia|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Tunisia|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_United_Arab_Emirates_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:United_Arab_Emirates|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:United_Arab_Emirates|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_Yemen_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:Yemen|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:Yemen|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_communication_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:communication|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:communication|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_computer_and_phone_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:computer_and_phone|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:computer_and_phone|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_daily_life_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:daily_life|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:daily_life|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_acva_entertainment_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|acva:entertainment|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|acva:entertainment|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_mcq_exams_test_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:mcq_exams_test_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:mcq_exams_test_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_meta_ar_dialects_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:meta_ar_dialects|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:meta_ar_dialects|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_meta_ar_msa_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:meta_ar_msa|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:meta_ar_msa|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_multiple_choice_facts_truefalse_balanced_task_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:multiple_choice_facts_truefalse_balanced_task|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_facts_truefalse_balanced_task|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_multiple_choice_grounded_statement_soqal_task_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:multiple_choice_grounded_statement_soqal_task|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_grounded_statement_soqal_task|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_multiple_choice_grounded_statement_xglue_mlqa_task_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_multiple_choice_rating_sentiment_no_neutral_task_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_multiple_choice_rating_sentiment_task_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_task|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_task|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_alghafa_multiple_choice_sentiment_task_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|alghafa:multiple_choice_sentiment_task|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_sentiment_task|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_exams_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_exams|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_exams|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_abstract_algebra_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:abstract_algebra|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:abstract_algebra|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_anatomy_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:anatomy|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:anatomy|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_astronomy_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:astronomy|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:astronomy|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_business_ethics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:business_ethics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:business_ethics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_clinical_knowledge_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:clinical_knowledge|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:clinical_knowledge|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_college_biology_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:college_biology|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_biology|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_college_chemistry_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:college_chemistry|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_chemistry|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_college_computer_science_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:college_computer_science|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_computer_science|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_college_mathematics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:college_mathematics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_mathematics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_college_medicine_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:college_medicine|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_medicine|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_college_physics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:college_physics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_physics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_computer_security_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:computer_security|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:computer_security|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_conceptual_physics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:conceptual_physics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:conceptual_physics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_econometrics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:econometrics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:econometrics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_electrical_engineering_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:electrical_engineering|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:electrical_engineering|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_elementary_mathematics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:elementary_mathematics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:elementary_mathematics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_formal_logic_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:formal_logic|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:formal_logic|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_global_facts_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:global_facts|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:global_facts|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_biology_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_biology|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_biology|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_chemistry_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_chemistry|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_chemistry|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_computer_science_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_computer_science|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_computer_science|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_european_history_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_european_history|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_european_history|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_geography_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_geography|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_geography|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_government_and_politics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_government_and_politics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_government_and_politics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_macroeconomics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_macroeconomics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_macroeconomics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_mathematics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_mathematics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_mathematics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_microeconomics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_microeconomics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_microeconomics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_physics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_physics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_physics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_psychology_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_psychology|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_psychology|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_statistics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_statistics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_statistics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_us_history_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_us_history|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_us_history|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_high_school_world_history_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:high_school_world_history|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_world_history|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_human_aging_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:human_aging|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:human_aging|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_human_sexuality_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:human_sexuality|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:human_sexuality|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_international_law_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:international_law|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:international_law|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_jurisprudence_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:jurisprudence|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:jurisprudence|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_logical_fallacies_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:logical_fallacies|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:logical_fallacies|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_machine_learning_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:machine_learning|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:machine_learning|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_management_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:management|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:management|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_marketing_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:marketing|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:marketing|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_medical_genetics_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:medical_genetics|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:medical_genetics|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_miscellaneous_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:miscellaneous|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:miscellaneous|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_moral_disputes_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:moral_disputes|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:moral_disputes|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_moral_scenarios_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:moral_scenarios|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:moral_scenarios|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_nutrition_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:nutrition|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:nutrition|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_philosophy_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:philosophy|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:philosophy|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_prehistory_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:prehistory|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:prehistory|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_professional_accounting_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:professional_accounting|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_accounting|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_professional_law_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:professional_law|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_law|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_professional_medicine_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:professional_medicine|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_medicine|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_professional_psychology_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:professional_psychology|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_psychology|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_public_relations_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:public_relations|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:public_relations|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_security_studies_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:security_studies|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:security_studies|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_sociology_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:sociology|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:sociology|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_us_foreign_policy_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:us_foreign_policy|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:us_foreign_policy|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_virology_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:virology|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:virology|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arabic_mmlu_world_religions_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arabic_mmlu:world_religions|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arabic_mmlu:world_religions|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arc_challenge_okapi_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arc_challenge_okapi_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arc_challenge_okapi_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_arc_easy_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|arc_easy_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|arc_easy_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_boolq_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|boolq_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|boolq_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_copa_ext_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|copa_ext_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|copa_ext_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_hellaswag_okapi_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|hellaswag_okapi_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|hellaswag_okapi_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_openbook_qa_ext_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|openbook_qa_ext_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|openbook_qa_ext_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_piqa_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|piqa_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|piqa_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_race_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|race_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|race_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_sciq_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|sciq_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|sciq_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: community_toxigen_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_community|toxigen_ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_community|toxigen_ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: lighteval_xstory_cloze_ar_0 data_files: - split: 2024_05_27T14_06_39.187751 path: - '**/details_lighteval|xstory_cloze:ar|0_2024-05-27T14-06-39.187751.parquet' - split: latest path: - '**/details_lighteval|xstory_cloze:ar|0_2024-05-27T14-06-39.187751.parquet' - config_name: results data_files: - split: 2024_05_27T14_06_39.187751 path: - results_2024-05-27T14-06-39.187751.parquet - split: latest path: - results_2024-05-27T14-06-39.187751.parquet --- # Dataset Card for Evaluation run of Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental](https://huggingface.co/Omartificial-Intelligence-Space/al-baka-llama3-8b-experimental). The dataset is composed of 136 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. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("OALL/details_Omartificial-Intelligence-Space__al-baka-llama3-8b-experimental", "lighteval_xstory_cloze_ar_0", split="train") ``` ## Latest results These are the [latest results from run 2024-05-27T14:06:39.187751](https://huggingface.co/datasets/OALL/details_Omartificial-Intelligence-Space__al-baka-llama3-8b-experimental/blob/main/results_2024-05-27T14-06-39.187751.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_norm": 0.4253986985051422, "acc_norm_stderr": 0.0384347707626982, "acc": 0.5956320317670417, "acc_stderr": 0.012629580396570928 }, "community|acva:Algeria|0": { "acc_norm": 0.5743589743589743, "acc_norm_stderr": 0.03549871080367707 }, "community|acva:Ancient_Egypt|0": { "acc_norm": 0.10158730158730159, "acc_norm_stderr": 0.01704876125499024 }, "community|acva:Arab_Empire|0": { "acc_norm": 0.4377358490566038, "acc_norm_stderr": 0.030533338430467512 }, "community|acva:Arabic_Architecture|0": { "acc_norm": 0.47692307692307695, "acc_norm_stderr": 0.0358596530894741 }, "community|acva:Arabic_Art|0": { "acc_norm": 0.39487179487179486, "acc_norm_stderr": 0.03509545602262037 }, "community|acva:Arabic_Astronomy|0": { "acc_norm": 0.49230769230769234, "acc_norm_stderr": 0.03589365940635213 }, "community|acva:Arabic_Calligraphy|0": { "acc_norm": 0.5294117647058824, "acc_norm_stderr": 0.03131846503821582 }, "community|acva:Arabic_Ceremony|0": { "acc_norm": 0.5351351351351351, "acc_norm_stderr": 0.03676936950948699 }, "community|acva:Arabic_Clothing|0": { "acc_norm": 0.4717948717948718, "acc_norm_stderr": 0.035840746749208334 }, "community|acva:Arabic_Culture|0": { "acc_norm": 0.2564102564102564, "acc_norm_stderr": 0.031349709942744934 }, "community|acva:Arabic_Food|0": { "acc_norm": 0.46153846153846156, "acc_norm_stderr": 0.03579154352544571 }, "community|acva:Arabic_Funeral|0": { "acc_norm": 0.4, "acc_norm_stderr": 0.050529115263991134 }, "community|acva:Arabic_Geography|0": { "acc_norm": 0.6344827586206897, "acc_norm_stderr": 0.04013124195424386 }, "community|acva:Arabic_History|0": { "acc_norm": 0.3641025641025641, "acc_norm_stderr": 0.0345465386778639 }, "community|acva:Arabic_Language_Origin|0": { "acc_norm": 0.6, "acc_norm_stderr": 0.050529115263991134 }, "community|acva:Arabic_Literature|0": { "acc_norm": 0.496551724137931, "acc_norm_stderr": 0.041665675771015785 }, "community|acva:Arabic_Math|0": { "acc_norm": 0.40512820512820513, "acc_norm_stderr": 0.03524577495610961 }, "community|acva:Arabic_Medicine|0": { "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "community|acva:Arabic_Music|0": { "acc_norm": 0.23741007194244604, "acc_norm_stderr": 0.036220593237998276 }, "community|acva:Arabic_Ornament|0": { "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.03384487217112065 }, "community|acva:Arabic_Philosophy|0": { "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "community|acva:Arabic_Physics_and_Chemistry|0": { "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.03581804596782232 }, "community|acva:Arabic_Wedding|0": { "acc_norm": 0.6205128205128205, "acc_norm_stderr": 0.03483959266365359 }, "community|acva:Bahrain|0": { "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.07309112127323451 }, "community|acva:Comoros|0": { "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.07491109582924915 }, "community|acva:Egypt_modern|0": { "acc_norm": 0.5684210526315789, "acc_norm_stderr": 0.05108592673308946 }, "community|acva:InfluenceFromAncientEgypt|0": { "acc_norm": 0.5384615384615384, "acc_norm_stderr": 0.0357915435254457 }, "community|acva:InfluenceFromByzantium|0": { "acc_norm": 0.7172413793103448, "acc_norm_stderr": 0.037528339580033376 }, "community|acva:InfluenceFromChina|0": { "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.03174930436412669 }, "community|acva:InfluenceFromGreece|0": { "acc_norm": 0.6461538461538462, "acc_norm_stderr": 0.03433004254147036 }, "community|acva:InfluenceFromIslam|0": { "acc_norm": 0.31724137931034485, "acc_norm_stderr": 0.038783523721386215 }, "community|acva:InfluenceFromPersia|0": { "acc_norm": 0.7028571428571428, "acc_norm_stderr": 0.03464507889884372 }, "community|acva:InfluenceFromRome|0": { "acc_norm": 0.5743589743589743, "acc_norm_stderr": 0.03549871080367708 }, "community|acva:Iraq|0": { "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.0521414859075246 }, "community|acva:Islam_Education|0": { "acc_norm": 0.4564102564102564, "acc_norm_stderr": 0.03576123096991215 }, "community|acva:Islam_branches_and_schools|0": { "acc_norm": 0.4857142857142857, "acc_norm_stderr": 0.03788942763158507 }, "community|acva:Islamic_law_system|0": { "acc_norm": 0.4307692307692308, "acc_norm_stderr": 0.0355521325205876 }, "community|acva:Jordan|0": { "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.07309112127323451 }, "community|acva:Kuwait|0": { "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.06979205927323111 }, "community|acva:Lebanon|0": { "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.06267511942419626 }, "community|acva:Libya|0": { "acc_norm": 0.5111111111111111, "acc_norm_stderr": 0.07535922203472523 }, "community|acva:Mauritania|0": { "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.07535922203472523 }, "community|acva:Mesopotamia_civilization|0": { "acc_norm": 0.5806451612903226, "acc_norm_stderr": 0.03976361630387024 }, "community|acva:Morocco|0": { "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.0730911212732345 }, "community|acva:Oman|0": { "acc_norm": 0.4222222222222222, "acc_norm_stderr": 0.07446027270295806 }, "community|acva:Palestine|0": { "acc_norm": 0.3764705882352941, "acc_norm_stderr": 0.052863310306265295 }, "community|acva:Qatar|0": { "acc_norm": 0.6, "acc_norm_stderr": 0.07385489458759965 }, "community|acva:Saudi_Arabia|0": { "acc_norm": 0.5435897435897435, "acc_norm_stderr": 0.03576123096991214 }, "community|acva:Somalia|0": { "acc_norm": 0.6, "acc_norm_stderr": 0.07385489458759965 }, "community|acva:Sudan|0": { "acc_norm": 0.4, "acc_norm_stderr": 0.07385489458759965 }, "community|acva:Syria|0": { "acc_norm": 0.4, "acc_norm_stderr": 0.07385489458759964 }, "community|acva:Tunisia|0": { "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.06666666666666665 }, "community|acva:United_Arab_Emirates|0": { "acc_norm": 0.4823529411764706, "acc_norm_stderr": 0.054520483406618934 }, "community|acva:Yemen|0": { "acc_norm": 0.5, "acc_norm_stderr": 0.16666666666666666 }, "community|acva:communication|0": { "acc_norm": 0.43131868131868134, "acc_norm_stderr": 0.02599443023962308 }, "community|acva:computer_and_phone|0": { "acc_norm": 0.45084745762711864, "acc_norm_stderr": 0.02901934773187137 }, "community|acva:daily_life|0": { "acc_norm": 0.18694362017804153, "acc_norm_stderr": 0.021268948348414647 }, "community|acva:entertainment|0": { "acc_norm": 0.23389830508474577, "acc_norm_stderr": 0.024687839412166384 }, "community|alghafa:mcq_exams_test_ar|0": { "acc_norm": 0.3303411131059246, "acc_norm_stderr": 0.01994668532793601 }, "community|alghafa:meta_ar_dialects|0": { "acc_norm": 0.3088044485634847, "acc_norm_stderr": 0.006290523225095885 }, "community|alghafa:meta_ar_msa|0": { "acc_norm": 0.3463687150837989, "acc_norm_stderr": 0.015913546784020117 }, "community|alghafa:multiple_choice_facts_truefalse_balanced_task|0": { "acc_norm": 0.52, "acc_norm_stderr": 0.05807730170189531 }, "community|alghafa:multiple_choice_grounded_statement_soqal_task|0": { "acc_norm": 0.52, "acc_norm_stderr": 0.04092881363092387 }, "community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0": { "acc_norm": 0.4066666666666667, "acc_norm_stderr": 0.04024162665739063 }, "community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0": { "acc_norm": 0.7697310819262039, "acc_norm_stderr": 0.004708744220539922 }, "community|alghafa:multiple_choice_rating_sentiment_task|0": { "acc_norm": 0.5296080066722268, "acc_norm_stderr": 0.006446869248966106 }, "community|alghafa:multiple_choice_sentiment_task|0": { "acc_norm": 0.3598837209302326, "acc_norm_stderr": 0.011576375321508726 }, "community|arabic_exams|0": { "acc_norm": 0.3780260707635009, "acc_norm_stderr": 0.020944238360403714 }, "community|arabic_mmlu:abstract_algebra|0": { "acc_norm": 0.31, 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"acc_norm": 0.32947976878612717, "acc_norm_stderr": 0.03583901754736412 }, "community|arabic_mmlu:college_physics|0": { "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.044405219061793254 }, "community|arabic_mmlu:computer_security|0": { "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "community|arabic_mmlu:conceptual_physics|0": { "acc_norm": 0.34893617021276596, "acc_norm_stderr": 0.031158522131357756 }, "community|arabic_mmlu:econometrics|0": { "acc_norm": 0.3508771929824561, "acc_norm_stderr": 0.044895393502706986 }, "community|arabic_mmlu:electrical_engineering|0": { "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "community|arabic_mmlu:elementary_mathematics|0": { "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.024278568024307706 }, "community|arabic_mmlu:formal_logic|0": { "acc_norm": 0.31746031746031744, "acc_norm_stderr": 0.041634530313028585 }, "community|arabic_mmlu:global_facts|0": { "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "community|arabic_mmlu:high_school_biology|0": { "acc_norm": 0.3903225806451613, "acc_norm_stderr": 0.027751256636969576 }, "community|arabic_mmlu:high_school_chemistry|0": { "acc_norm": 0.3645320197044335, "acc_norm_stderr": 0.0338640574606209 }, "community|arabic_mmlu:high_school_computer_science|0": { "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "community|arabic_mmlu:high_school_european_history|0": { "acc_norm": 0.23636363636363636, "acc_norm_stderr": 0.03317505930009179 }, "community|arabic_mmlu:high_school_geography|0": { "acc_norm": 0.3838383838383838, "acc_norm_stderr": 0.03464881675016338 }, "community|arabic_mmlu:high_school_government_and_politics|0": { "acc_norm": 0.38860103626943004, "acc_norm_stderr": 0.03517739796373134 }, "community|arabic_mmlu:high_school_macroeconomics|0": { "acc_norm": 0.37435897435897436, "acc_norm_stderr": 0.024537591572830503 }, "community|arabic_mmlu:high_school_mathematics|0": { "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.027309140588230175 }, "community|arabic_mmlu:high_school_microeconomics|0": { "acc_norm": 0.3319327731092437, "acc_norm_stderr": 0.030588697013783663 }, "community|arabic_mmlu:high_school_physics|0": { "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526731 }, "community|arabic_mmlu:high_school_psychology|0": { "acc_norm": 0.381651376146789, "acc_norm_stderr": 0.020828148517022606 }, "community|arabic_mmlu:high_school_statistics|0": { "acc_norm": 0.24537037037037038, "acc_norm_stderr": 0.029346665094372937 }, "community|arabic_mmlu:high_school_us_history|0": { "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.031321798030832904 }, "community|arabic_mmlu:high_school_world_history|0": { "acc_norm": 0.34177215189873417, "acc_norm_stderr": 0.030874537537553617 }, "community|arabic_mmlu:human_aging|0": { "acc_norm": 0.3991031390134529, "acc_norm_stderr": 0.03286745312567961 }, "community|arabic_mmlu:human_sexuality|0": { "acc_norm": 0.4198473282442748, "acc_norm_stderr": 0.04328577215262972 }, "community|arabic_mmlu:international_law|0": { "acc_norm": 0.5785123966942148, "acc_norm_stderr": 0.04507732278775087 }, "community|arabic_mmlu:jurisprudence|0": { "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.04820403072760627 }, "community|arabic_mmlu:logical_fallacies|0": { "acc_norm": 0.4723926380368098, "acc_norm_stderr": 0.039223782906109894 }, "community|arabic_mmlu:machine_learning|0": { "acc_norm": 0.3482142857142857, "acc_norm_stderr": 0.04521829902833586 }, "community|arabic_mmlu:management|0": { "acc_norm": 0.4368932038834951, "acc_norm_stderr": 0.04911147107365777 }, "community|arabic_mmlu:marketing|0": { "acc_norm": 0.5683760683760684, "acc_norm_stderr": 0.0324483553531149 }, "community|arabic_mmlu:medical_genetics|0": { "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "community|arabic_mmlu:miscellaneous|0": { "acc_norm": 0.4623243933588761, "acc_norm_stderr": 0.017829131764287184 }, "community|arabic_mmlu:moral_disputes|0": { "acc_norm": 0.407514450867052, "acc_norm_stderr": 0.026454578146931505 }, "community|arabic_mmlu:moral_scenarios|0": { "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "community|arabic_mmlu:nutrition|0": { "acc_norm": 0.46405228758169936, "acc_norm_stderr": 0.028555827516528787 }, "community|arabic_mmlu:philosophy|0": { "acc_norm": 0.4212218649517685, "acc_norm_stderr": 0.028043399858210635 }, "community|arabic_mmlu:prehistory|0": { "acc_norm": 0.4104938271604938, "acc_norm_stderr": 0.027371350925124764 }, "community|arabic_mmlu:professional_accounting|0": { "acc_norm": 0.2907801418439716, "acc_norm_stderr": 0.027090664368353178 }, "community|arabic_mmlu:professional_law|0": { "acc_norm": 0.3109517601043025, "acc_norm_stderr": 0.011822252917799205 }, "community|arabic_mmlu:professional_medicine|0": { "acc_norm": 0.21691176470588236, "acc_norm_stderr": 0.02503584522771127 }, "community|arabic_mmlu:professional_psychology|0": { "acc_norm": 0.36764705882352944, "acc_norm_stderr": 0.019506291693954847 }, "community|arabic_mmlu:public_relations|0": { "acc_norm": 0.39090909090909093, "acc_norm_stderr": 0.04673752333670237 }, "community|arabic_mmlu:security_studies|0": { "acc_norm": 0.4326530612244898, "acc_norm_stderr": 0.031717528240626645 }, "community|arabic_mmlu:sociology|0": { "acc_norm": 0.5621890547263682, "acc_norm_stderr": 0.035080801121998406 }, "community|arabic_mmlu:us_foreign_policy|0": { "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "community|arabic_mmlu:virology|0": { "acc_norm": 0.41566265060240964, "acc_norm_stderr": 0.03836722176598052 }, "community|arabic_mmlu:world_religions|0": { "acc_norm": 0.4327485380116959, "acc_norm_stderr": 0.03799978644370608 }, "community|arc_challenge_okapi_ar|0": { "acc_norm": 0.36982758620689654, "acc_norm_stderr": 0.01418037210859747 }, "community|arc_easy_ar|0": { "acc_norm": 0.41455160744500846, "acc_norm_stderr": 0.010134486532149578 }, "community|boolq_ar|0": { "acc_norm": 0.6220858895705521, "acc_norm_stderr": 0.00849336032152894 }, "community|copa_ext_ar|0": { "acc_norm": 0.5666666666666667, "acc_norm_stderr": 0.05252667118728808 }, "community|hellaswag_okapi_ar|0": { "acc_norm": 0.26616508559590013, "acc_norm_stderr": 0.004615195054138359 }, "community|openbook_qa_ext_ar|0": { "acc_norm": 0.4383838383838384, "acc_norm_stderr": 0.02232459513248414 }, "community|piqa_ar|0": { "acc_norm": 0.5575559192580469, "acc_norm_stderr": 0.011604078962074162 }, "community|race_ar|0": { "acc_norm": 0.3590992087644553, "acc_norm_stderr": 0.006833878896866116 }, "community|sciq_ar|0": { "acc_norm": 0.4562814070351759, "acc_norm_stderr": 0.015798297434857358 }, "community|toxigen_ar|0": { "acc_norm": 0.4320855614973262, "acc_norm_stderr": 0.01620887578524445 }, "lighteval|xstory_cloze:ar|0": { "acc": 0.5956320317670417, "acc_stderr": 0.012629580396570928 }, "community|acva:_average|0": { "acc_norm": 0.45897910079456206, "acc_norm_stderr": 0.04763314008202572 }, "community|alghafa:_average|0": { "acc_norm": 0.454600416994282, "acc_norm_stderr": 0.02268116512425296 }, "community|arabic_mmlu:_average|0": { "acc_norm": 0.3834369097890124, "acc_norm_stderr": 0.03575749811075187 } } ``` ## 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]

lingvenvist/more_a_h_2.csv

lingvenvist

--- dataset_info: features: - name: sentences dtype: string - name: tokens sequence: string - name: anim_tags sequence: class_label: names: '0': N '1': A '2': H - name: target-indexes sequence: int64 splits: - name: train num_bytes: 14219555.597162277 num_examples: 38623 - name: test num_bytes: 2217106.2740564435 num_examples: 5872 - name: validation num_bytes: 1879539.3533294506 num_examples: 5143 download_size: 8476322 dataset_size: 18316201.224548172 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---

saluslab/human-machine-interactions

saluslab

--- language: - en license: - mit multilinguality: - monolingual pretty_name: Human-Machine Interactions with a Wire Arc Additive Manufacturing Machine size_categories: - 100K<n<1M source_datasets: - original tags: - human action recognition - skeleton-based human action recognition - joint skeletons - human interaction - cyber-physical-social systems - digital twins task_categories: - video-classification - time-series-forecasting - other task_ids: [] --- # Dataset Card for this Human-Machine Interaction Dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Overview](#dataset-overview) - [Summary of Data](#summary-of-data) - [Motivation for this Dataset](#motivation-for-this-dataset) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Data Contents](#data-contents) - [Data Frame](#data-frame) - [Data Collection](#data-collection) - [Machine of Focus and Facility](#machine-of-focus-and-facility) - [Sensor and Data Modality](#sensor-and-data-modality) - [A Note on Privacy](#a-note-on-privacy) - [Additional Information and Analysis Techniques](#additional-information-and-analysis-techniques) - [Action List](#action-list) - [Skeleton Features](#skeleton-features) - [Machine Learning Techniques](#machine-learning-techniques) - [Acknowledgements](#acknowledgements) - [Dataset Curators](#dataset-curation) - [Funding and Support](#funding-and-support) - [Citation](#citation) ## Dataset Overview This dataset contains a collection of observed interactions between humans and an advanced manufacturing machine, specifically a Wire Arc Additive Manufacuturing (WAAM) machine. The motivations for collecting this dataset, the contents of this dataset, and some ideas for how to analyze and use this dataset can be found below. Additionally, the paper introducing this dataset is undergoing review for publication to the American Society of Mechanical Engineers(ASME)’s Journal of Mechanical Design (JMD) special issue: “Cultivating Datasets for Engineering Design”. If accepted, the paper will be referenced here. ### Motivation for this Dataset The engineering design process for any solution or product is essential to ensure quality results and standards. However, this process can be very tedious and require many re-iterations, especially if it involves manufacturing a product. If engineers and designers are designing a product to be manufactured, but are disconnected from the realities of their available manufacturing capabilities, there can be many redesign iterations stemming from this misunderstanding between design specifications and production / supply chain abilities. Design for Manufacturing (DfM) is a style of design that, relying on accurate simulation and modeling of available manufacturing processes, takes into account the product manufacturing when designing products such that the design reiteration inefficiency is improved. To improve the transparency between manufacturing and design, establishing methods to understand and quantify the various steps in the manufacturing process is crucial. Within this effort, and in manufacturing, one of the most difficult aspects to understand and quantify is the interactions of humans and machinery. While manufacturing is undergoing immense change due to automation technologies and robotics, humans still play a central role in operations, however their behaviors / actions and how it influences the manufacturing process is poorly understood. This dataset attempts to support the understanding of humans in manufacturing by observing realistic interactions between humans and an advanced manufacturing machine. ### Supported Tasks - `video-classification`: Using the series of provided frames of depth images and joint skeletons, machine learning techniques can be used to classify these by human actions. ### Languages English ## Data Contents This dataset comprises 3.87 hours of footage (209,230 frames of data at 15 FPS) representing a total of 1228 interactions captured over 6 months. The depth images were captured from the Microsoft Azure Kinect DK sensor in NFOV mode (More can be found on the [Azure Kinect Hardware Specs Website](https://learn.microsoft.com/en-us/azure/kinect-dk/hardware-specification) )and skeletons extracted of the humans in each frame were extracted using the Azure Kinect Body Tracking SDK (found [here](https://microsoft.github.io/Azure-Kinect-Body-Tracking/release/1.1.x/index.html) ). ### Data Frame Each frame contains the following data points and labels: * image: A 320x288 16-bit grayscale .png file of the depth image captured. This depth image is either from the outer machine perspective or the inner perspective according to the view label. * frame(#): An integer (from 0 - 209230) representing a unique frame identifier number. The frames are numbered in chronological order. * skeleton: An array of 32 3D coordinates. Each skeleton array captures 32 joints on the human body within the frame according to the Microsoft Azure Kinect Body Tracking SDK (linked above). For more information about the indexing of each joint, see this [Azure Kinect Joint Skeleton Webpage](https://learn.microsoft.com/en-us/azure/kinect-dk/body-joints). * action_label: A label of which action the current frame is capturing. A list of all the label actions can be found below. * location_label: A label of where on the machine the human is performing the interaction in the current frame. * user_label: A label of the unique user ID given to the person in the frame. There are a total of 4 users (numbered 0 - 3). This order of user id is also the frequency with which they use the machine - 0 being the most frequent and 3 being the least. * view_label: A label of which sensor perspective best captures the action in the frame (0 for outer perspective and 1 for inner). * action_number: A label (0 - 1227) describing which of the total 1228 actions a particular frame is a part of. The data originally consisted of 1228 depth video clips of each action from its start to finish and all these videos were later split into individual frames. Since analyzing human actions usually needs temporal context, the action number allows for the grouping and ordering (in conjunction with the frame number or timestamp label) of all frames that comprise of a complete action. * datetime: A timestamp of when this frame was captured. This allows for ordering of frames and actions as well as seeing how long was waited in between adjacent actions. This also allows for the splitting of experimental sessions between days. The context of the ordering of actions as well as which may occur at the beginning or end of a day is very useful. ## Data Collection ### Machine of Focus and Facility The machine being interacted with in this dataset is the Lincoln Electric Sculptprint RND Wire Arc Additive Manufacturing (WAAM) machine. The WAAM machine is a large-format metal 3D printer housed in a 2.2m x 4.1m x 2.3 m (LxWxH) chamber that includes a robotic welder arm that deposits molten metal filament upon a specially configured build plate in a layered fashion. We chose this machine as a starting point because it exemplifies a wide variety of different human interactions. Actions range from very direct, hands-on actions like grinding down the metal build plate or refitting parts on the build plate to more indirect hands-off actions like calibrating the robot arm with a joystick or using the digital control panel. Additionally, the machine we studied was housed at Mill19, a manufacturing and robotics research facility run by the Manufacturing Future Institute (MFI) at Carnegie Mellon University. More about this machine and facility can be found at [MFI's page about the WAAM](https://engineering.cmu.edu/mfi/facilities/equipment-details/lincoln-electric-sculptprint-rnd.html). ### Sensor and Data Modality For our data collection, we used 2 Microsoft Azure Kinect DK cameras (Linked again [here](https://learn.microsoft.com/en-us/azure/kinect-dk/hardware-specification) for convenience). Due to the WAAM machine having points of interaction both inside its welding chamber and outside, we installed 2 Azure Kinect sensors to observe human interactions, 1 captures the ‘outer perspective’ and the other the ‘inner perspective’. While the Azure Kinect captures many modalities of data, we chose to focus on depth images (in near-field-of-view ’NFOV’ mode) and human joint skeletons. These were extracted at a rate of 1/15 frames per second. ### A Note on Privacy The choice to focus on just depth and joint skeletons was made in order to preserve the privacy of users being sensed. This is very important to maintain when observing humans in a largely shared environment. This is also important in industry or any public infrastructure settings, thus if we can show that meaningful knowledge can be learned using privacy preserving technologies, more wide-spread use of these technologies can be used safely. ## Additional Information and Analysis Techniques ### Action List A complete list of actions and a brief description include: * using_control_panel : Interfacing with machine start/stop controls and digital screen used for visualizing build files and configuring machine parameters. * using_flexpendant_mounted : Flexpendant being used in its control mode for loading build parameters and viewing machine output logs. * using_flexpendant_mobile : Flexpendant being used in its machine operation mode for moving the robotic arm with the attached joystick. * inspecting_buildplate : Performing light build plate modifications and inspections before or after a build. * preparing_buildplate : Clearing or moving build plate to set up next build. * refit_buildplate : Completely switching out the build plate configuration for a new project. * grinding_buildplate : Grinding down the new build plate to expose conductive metal and level surface. * toggle_lights : Turn the internal WAAM light on/off. * open_door : Opening the WAAM door. * close_door : Closing the WAAM door. * turning_gas_knobs : Turning on/off shielding gas. * adjusting_tool : Installing or modifying new/existing sensors on the robotic welder arm. * wiring : Installing or adjusting wiring of tool sensors. * donning_ppe : Users putting on personal protective equipment. * doffing_ppe : Users taking off personal protective equipment. * observing : Simply looking around or watching WAAM activity. * walking : Simply walking around the WAAM. ### Skeleton Features The skeleton data provided in each frame consists of an array of 32 joint coordinates in 3D space (x,y,z). The units of each coordinate value are in millimeters and the origin is the respective Kinect sensor capturing the particular frame (more on the coordinate system can be found on [the Azure Kinect webpage on the sensor coordinate system](https://learn.microsoft.com/en-us/azure/kinect-dk/coordinate-systems) and the [Body Tracking SDK’s webpage on joints](https://learn.microsoft.com/en-us/azure/kinect-dk/body-joints)). While analysis techniques can be used on these ‘raw’ coordinate, there are many hand-picked features that can be extracted from these coordinates. Some basic and popular examples include: * Joint Coordinate Normalization: The coordinates from the skeletons can be normalized with respect to each other. Additionally, another technique can be to choose a single joint in the center of the body to be the ‘origin’ coordinate, then re-calculate the coordinates of every other joint in relation to this central one. * Joint Velocities: Calculated by the difference in a joint’s coordinates between frames (each frame is 1/15 of a second apart) * Joint Angles: Calculate the angle created at a specific joint by adjacent limbs by performing some trigonometric calculations using the vectors from the joint of focus and its adjacent joints. * Joint Distances: Pick 2 joints of interest and derive the distance between them using some basic geometric calculation. ### Machine Learning Techniques Human action recognition often utilizes deep learning techniques to analyze and identify patterns in human actions. This is due to some deep learning techniques having great ability to analyze data both temporally and spatially. Some popular deep learning models include: * Long-Short Term Memory (LSTM) : This deep learning model is a type of recurrent neural network (RNN) specifically targeted to avoid the vanishing gradient problem and tailored to temporal / sequential data with invariance to large or small gaps in important information distributed through the sequence. * Convolutional Neural Network (CNN) : A powerful image-based model that can extract visual features from complex imagery. * Graph Neural Networks (GCN) : A convolutional model performed over a defined / specialized graph network as opposed to an array of pixels. A specific example of this is the Spatial-Temporal GCN (STGCN), which is popularly used among skeleton-based human action recognition. * Autoencoding : An unsupervised learning technique that can be used to learn sets of patterns and features shared by data. This can be particularly powerful for clustering data and quantifying differences between particular actions. This is also powerful in reducing data dimensionality - being able to represent the data using a smaller set of features than originally. ## Acknowledgements ### Dataset Curators This dataset was collected by John Martins with the guidance of Katherine Flanigan and Christopher McComb The corresponding paper was written by John Martins, Katherine Flanigan, and Chrisopher McComb ### Funding and Support We thank Carnegie Mellon’s Manufacturing Futures Institute for graciously funding and supporting the endeavors to collect this data. We also want to thank Mill19 for granting access to their facilities and allowing us to install sensors. Lastly, we would like to thank the users of the WAAM machine for allowing us to collect data on their uses of the machine over the 6 month data collection period. ### Citation As mentioned before, the paper introducing this dataset is undergoing review for publication to the American Society of Mechanical Engineers(ASME)’s Journal of Mechanical Design (JMD) special issue: “Cultivating Datasets for Engineering Design”. If accepted, the paper will be referenced here.

Zual/chessGPT

Zual

--- license: mit task_categories: - text-generation language: - fr --- Here is some datasets that contains "noisy" chess games. We define the noise as a probability of playing a random (yet legal) chess move. For example, a chess game with 5% of noise is a chess game between Stockfish and himself when each move have 5% of chance to be random.

Null-NaN/ss_test_org_ships_0

Null-NaN

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 10427248.0 num_examples: 180 download_size: 9213189 dataset_size: 10427248.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

fresvyx/alpaca2-imam-ganteng

fresvyx

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 1351672 num_examples: 1000 download_size: 712329 dataset_size: 1351672 configs: - config_name: default data_files: - split: train path: data/train-* ---

damerajee/small_clean_llava-instruct-mix

damerajee

--- dataset_info: features: - name: image dtype: image - name: conversations dtype: string - name: texts list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 8283699598.0 num_examples: 50000 download_size: 8207810675 dataset_size: 8283699598.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

bpalacios/ttgphismall

bpalacios

--- license: mit ---

roshikhan3021/netbrix-dataset

roshikhan3021

--- license: mit ---

asahi417/seamless-align-enA-jaA

asahi417

--- dataset_info: - config_name: subset_1 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 392397221.989 num_examples: 2081 download_size: 386957004 dataset_size: 392397221.989 - config_name: subset_10 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 351659087.73 num_examples: 1965 download_size: 347647106 dataset_size: 351659087.73 - config_name: subset_100 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 347439311.634 num_examples: 1763 download_size: 344710645 dataset_size: 347439311.634 - config_name: subset_101 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 365547481.5 num_examples: 1875 download_size: 362661933 dataset_size: 365547481.5 - config_name: subset_102 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 381968578.736 num_examples: 1881 download_size: 378806119 dataset_size: 381968578.736 - config_name: subset_103 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 377107099.288 num_examples: 1892 download_size: 376129169 dataset_size: 377107099.288 - config_name: subset_104 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - 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name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 328915586.396 num_examples: 1794 download_size: 328763242 dataset_size: 328915586.396 - config_name: subset_24 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 332737609.168 num_examples: 1762 download_size: 325215649 dataset_size: 332737609.168 - 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name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 346798563.768 num_examples: 1904 download_size: 343495987 dataset_size: 346798563.768 - config_name: subset_30 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 361155095.104 num_examples: 1907 download_size: 354721798 dataset_size: 361155095.104 - 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name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 344032713.228 num_examples: 1868 download_size: 348221349 dataset_size: 344032713.228 - config_name: subset_37 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 355957197.391 num_examples: 1893 download_size: 353589260 dataset_size: 355957197.391 - 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name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 368484664.482 num_examples: 1886 download_size: 363208555 dataset_size: 368484664.482 - config_name: subset_81 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 382681091.404 num_examples: 1922 download_size: 378494301 dataset_size: 382681091.404 - config_name: subset_82 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 376125305.856 num_examples: 1912 download_size: 376672124 dataset_size: 376125305.856 - config_name: subset_83 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 371577857.09 num_examples: 1893 download_size: 369316183 dataset_size: 371577857.09 - config_name: subset_84 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 369560463.322 num_examples: 1874 download_size: 365140195 dataset_size: 369560463.322 - config_name: subset_85 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 364358029.998 num_examples: 1889 download_size: 364548198 dataset_size: 364358029.998 - config_name: subset_86 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 376636073.672 num_examples: 1872 download_size: 373150870 dataset_size: 376636073.672 - config_name: subset_87 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 362562530.995 num_examples: 1905 download_size: 369354649 dataset_size: 362562530.995 - config_name: subset_88 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 381751130.869 num_examples: 1909 download_size: 373678648 dataset_size: 381751130.869 - config_name: subset_89 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 371633741.0 num_examples: 1895 download_size: 372501566 dataset_size: 371633741.0 - config_name: subset_9 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 348422943.515 num_examples: 1985 download_size: 351519803 dataset_size: 348422943.515 - config_name: subset_90 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 373825088.18 num_examples: 1924 download_size: 375226691 dataset_size: 373825088.18 - config_name: subset_91 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 373896143.252 num_examples: 1922 download_size: 374160814 dataset_size: 373896143.252 - config_name: subset_92 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 364351588.28 num_examples: 1890 download_size: 365888274 dataset_size: 364351588.28 - config_name: subset_93 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 387866261.842 num_examples: 1881 download_size: 375615708 dataset_size: 387866261.842 - config_name: subset_94 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 377305622.574 num_examples: 1907 download_size: 373264809 dataset_size: 377305622.574 - config_name: subset_95 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 369064503.5 num_examples: 1875 download_size: 367451772 dataset_size: 369064503.5 - config_name: subset_96 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 377780781.221 num_examples: 1903 download_size: 378226431 dataset_size: 377780781.221 - config_name: subset_97 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 373154202.246 num_examples: 1906 download_size: 376439653 dataset_size: 373154202.246 - config_name: subset_98 features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 368477992.163 num_examples: 1911 download_size: 374903560 dataset_size: 368477992.163 - config_name: subset_99 features: - name: enA.audio dtype: audio - name: jaA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 380696639.606 num_examples: 1907 download_size: 380352674 dataset_size: 380696639.606 - config_name: subset_test features: - name: jaA.audio dtype: audio - name: enA.audio dtype: audio - name: line_no dtype: int64 - name: enA.id dtype: string - name: enA.url dtype: string - name: enA.duration_start dtype: int64 - name: enA.duration_end dtype: int64 - name: enA.laser_score dtype: float64 - name: jaA.id dtype: string - name: jaA.url dtype: string - name: jaA.duration_start dtype: int64 - name: jaA.duration_end dtype: int64 - name: jaA.laser_score dtype: float64 splits: - name: train num_bytes: 1530085.0 num_examples: 8 download_size: 1506623 dataset_size: 1530085.0 configs: - config_name: subset_1 data_files: - split: train path: subset_1/train-* - config_name: subset_10 data_files: - split: train path: subset_10/train-* - config_name: subset_100 data_files: - split: train path: subset_100/train-* - config_name: subset_101 data_files: - split: train path: subset_101/train-* - config_name: subset_102 data_files: - split: train path: subset_102/train-* - config_name: subset_103 data_files: - split: train path: subset_103/train-* - config_name: subset_104 data_files: - split: train path: subset_104/train-* - config_name: subset_105 data_files: - split: train path: subset_105/train-* - config_name: subset_106 data_files: - split: train path: subset_106/train-* - config_name: subset_107 data_files: - split: train path: subset_107/train-* - config_name: subset_108 data_files: - split: train path: subset_108/train-* - config_name: subset_109 data_files: - split: train path: subset_109/train-* - config_name: subset_11 data_files: - split: train path: subset_11/train-* - config_name: subset_110 data_files: - split: train path: subset_110/train-* - config_name: subset_111 data_files: - split: train path: subset_111/train-* - config_name: subset_112 data_files: - split: train path: subset_112/train-* - config_name: subset_113 data_files: - split: train path: subset_113/train-* - config_name: subset_114 data_files: - split: train path: subset_114/train-* - config_name: subset_115 data_files: - split: train path: subset_115/train-* - config_name: subset_116 data_files: - split: train path: subset_116/train-* - config_name: subset_117 data_files: - split: train path: subset_117/train-* - config_name: subset_118 data_files: - split: train path: subset_118/train-* - config_name: subset_119 data_files: - split: train path: subset_119/train-* - config_name: subset_12 data_files: - split: train path: subset_12/train-* - config_name: subset_120 data_files: - split: train path: subset_120/train-* - config_name: subset_121 data_files: - split: train path: subset_121/train-* - config_name: subset_122 data_files: - split: train path: subset_122/train-* - config_name: subset_123 data_files: - split: train path: subset_123/train-* - config_name: subset_124 data_files: - split: train path: subset_124/train-* - config_name: subset_125 data_files: - split: train path: subset_125/train-* - config_name: subset_126 data_files: - split: train path: subset_126/train-* - config_name: subset_127 data_files: - split: train path: subset_127/train-* - config_name: subset_128 data_files: - split: train path: subset_128/train-* - config_name: subset_129 data_files: - split: train path: subset_129/train-* - config_name: subset_13 data_files: - split: train path: subset_13/train-* - config_name: subset_130 data_files: - split: train path: subset_130/train-* - config_name: subset_131 data_files: - split: train path: subset_131/train-* - config_name: subset_132 data_files: - split: train path: subset_132/train-* - config_name: subset_133 data_files: - split: train path: subset_133/train-* - config_name: subset_134 data_files: - split: train path: subset_134/train-* - config_name: subset_135 data_files: - split: train path: subset_135/train-* - config_name: subset_136 data_files: - split: train path: subset_136/train-* - config_name: subset_137 data_files: - split: train path: subset_137/train-* - config_name: subset_138 data_files: - split: train path: subset_138/train-* - config_name: subset_139 data_files: - split: train path: subset_139/train-* - config_name: subset_14 data_files: - split: train path: subset_14/train-* - config_name: subset_140 data_files: - split: train path: subset_140/train-* - config_name: subset_141 data_files: - split: train path: subset_141/train-* - config_name: subset_142 data_files: - split: train path: subset_142/train-* - config_name: subset_143 data_files: - split: train path: subset_143/train-* - config_name: subset_144 data_files: - split: train path: subset_144/train-* - config_name: subset_15 data_files: - split: train path: subset_15/train-* - config_name: subset_16 data_files: - split: train path: subset_16/train-* - config_name: subset_17 data_files: - split: train path: subset_17/train-* - config_name: subset_18 data_files: - split: train path: subset_18/train-* - config_name: subset_19 data_files: - split: train path: subset_19/train-* - config_name: subset_2 data_files: - split: train path: subset_2/train-* - config_name: subset_20 data_files: - split: train path: subset_20/train-* - config_name: subset_21 data_files: - split: train path: subset_21/train-* - config_name: subset_22 data_files: - split: train path: subset_22/train-* - config_name: subset_23 data_files: - split: train path: subset_23/train-* - config_name: subset_24 data_files: - split: train path: subset_24/train-* - config_name: subset_25 data_files: - split: train path: subset_25/train-* - config_name: subset_26 data_files: - split: train path: subset_26/train-* - config_name: subset_27 data_files: - split: train path: subset_27/train-* - config_name: subset_28 data_files: - split: train path: subset_28/train-* - config_name: subset_29 data_files: - split: train path: subset_29/train-* - config_name: subset_3 data_files: - split: train path: subset_3/train-* - config_name: subset_30 data_files: - split: train path: subset_30/train-* - config_name: subset_31 data_files: - split: train path: subset_31/train-* - config_name: subset_32 data_files: - split: train path: subset_32/train-* - config_name: subset_33 data_files: - split: train path: subset_33/train-* - config_name: subset_34 data_files: - split: train path: subset_34/train-* - config_name: subset_35 data_files: - split: train path: subset_35/train-* - config_name: subset_36 data_files: - split: train path: subset_36/train-* - config_name: subset_37 data_files: - split: train path: subset_37/train-* - config_name: subset_38 data_files: - split: train path: subset_38/train-* - config_name: subset_39 data_files: - split: train path: subset_39/train-* - config_name: subset_4 data_files: - split: train path: subset_4/train-* - config_name: subset_40 data_files: - split: train path: subset_40/train-* - config_name: subset_41 data_files: - split: train path: subset_41/train-* - config_name: subset_42 data_files: - split: train path: subset_42/train-* - config_name: subset_43 data_files: - split: train path: subset_43/train-* - config_name: subset_44 data_files: - split: train path: subset_44/train-* - config_name: subset_45 data_files: - split: train path: subset_45/train-* - config_name: subset_46 data_files: - split: train path: subset_46/train-* - config_name: subset_47 data_files: - split: train path: subset_47/train-* - config_name: subset_48 data_files: - split: train path: subset_48/train-* - config_name: subset_49 data_files: - split: train path: subset_49/train-* - config_name: subset_5 data_files: - split: train path: subset_5/train-* - config_name: subset_50 data_files: - split: train path: subset_50/train-* - config_name: subset_51 data_files: - split: train path: subset_51/train-* - config_name: subset_52 data_files: - split: train path: subset_52/train-* - config_name: subset_53 data_files: - split: train path: subset_53/train-* - config_name: subset_54 data_files: - split: train path: subset_54/train-* - config_name: subset_55 data_files: - split: train path: subset_55/train-* - config_name: subset_56 data_files: - split: train path: subset_56/train-* - config_name: subset_57 data_files: - split: train path: subset_57/train-* - config_name: subset_58 data_files: - split: train path: subset_58/train-* - config_name: subset_59 data_files: - split: train path: subset_59/train-* - config_name: subset_6 data_files: - split: train path: subset_6/train-* - config_name: subset_60 data_files: - split: train path: subset_60/train-* - config_name: subset_61 data_files: - split: train path: subset_61/train-* - config_name: subset_62 data_files: - split: train path: subset_62/train-* - config_name: subset_63 data_files: - split: train path: subset_63/train-* - config_name: subset_64 data_files: - split: train path: subset_64/train-* - config_name: subset_65 data_files: - split: train path: subset_65/train-* - config_name: subset_66 data_files: - split: train path: subset_66/train-* - config_name: subset_67 data_files: - split: train path: subset_67/train-* - config_name: subset_68 data_files: - split: train path: subset_68/train-* - config_name: subset_69 data_files: - split: train path: subset_69/train-* - config_name: subset_7 data_files: - split: train path: subset_7/train-* - config_name: subset_70 data_files: - split: train path: subset_70/train-* - config_name: subset_71 data_files: - split: train path: subset_71/train-* - config_name: subset_72 data_files: - split: train path: subset_72/train-* - config_name: subset_73 data_files: - split: train path: subset_73/train-* - config_name: subset_74 data_files: - split: train path: subset_74/train-* - config_name: subset_75 data_files: - split: train path: subset_75/train-* - config_name: subset_76 data_files: - split: train path: subset_76/train-* - config_name: subset_77 data_files: - split: train path: subset_77/train-* - config_name: subset_78 data_files: - split: train path: subset_78/train-* - config_name: subset_79 data_files: - split: train path: subset_79/train-* - config_name: subset_8 data_files: - split: train path: subset_8/train-* - config_name: subset_80 data_files: - split: train path: subset_80/train-* - config_name: subset_81 data_files: - split: train path: subset_81/train-* - config_name: subset_82 data_files: - split: train path: subset_82/train-* - config_name: subset_83 data_files: - split: train path: subset_83/train-* - config_name: subset_84 data_files: - split: train path: subset_84/train-* - config_name: subset_85 data_files: - split: train path: subset_85/train-* - config_name: subset_86 data_files: - split: train path: subset_86/train-* - config_name: subset_87 data_files: - split: train path: subset_87/train-* - config_name: subset_88 data_files: - split: train path: subset_88/train-* - config_name: subset_89 data_files: - split: train path: subset_89/train-* - config_name: subset_9 data_files: - split: train path: subset_9/train-* - config_name: subset_90 data_files: - split: train path: subset_90/train-* - config_name: subset_91 data_files: - split: train path: subset_91/train-* - config_name: subset_92 data_files: - split: train path: subset_92/train-* - config_name: subset_93 data_files: - split: train path: subset_93/train-* - config_name: subset_94 data_files: - split: train path: subset_94/train-* - config_name: subset_95 data_files: - split: train path: subset_95/train-* - config_name: subset_96 data_files: - split: train path: subset_96/train-* - config_name: subset_97 data_files: - split: train path: subset_97/train-* - config_name: subset_98 data_files: - split: train path: subset_98/train-* - config_name: subset_99 data_files: - split: train path: subset_99/train-* - config_name: subset_test data_files: - split: train path: subset_test/train-* ---

jlbaker361/fashion

jlbaker361

--- dataset_info: features: - name: subject dtype: string - name: splash dtype: image splits: - name: train num_bytes: 88258039.0 num_examples: 2032 download_size: 87513588 dataset_size: 88258039.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

fresvyx/indo-llama2

fresvyx

--- language: - id ---

OALL/details_Artples__L-MChat-Small

OALL

--- pretty_name: Evaluation run of Artples/L-MChat-Small dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Artples/L-MChat-Small](https://huggingface.co/Artples/L-MChat-Small).\n\nThe\ \ dataset is composed of 136 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.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\"OALL/details_Artples__L-MChat-Small\"\ ,\n\t\"lighteval_xstory_cloze_ar_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2024-05-27T14:39:45.128547](https://huggingface.co/datasets/OALL/details_Artples__L-MChat-Small/blob/main/results_2024-05-27T14-39-45.128547.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_norm\": 0.3996877363610559,\n\ \ \"acc_norm_stderr\": 0.0363328445153769,\n \"acc\": 0.47187293183322304,\n\ \ \"acc_stderr\": 0.012846749995797692\n },\n \"community|acva:Algeria|0\"\ : {\n \"acc_norm\": 0.48717948717948717,\n \"acc_norm_stderr\": 0.03588610523192216\n\ \ },\n \"community|acva:Ancient_Egypt|0\": {\n \"acc_norm\": 0.9015873015873016,\n\ \ \"acc_norm_stderr\": 0.01680988100419675\n },\n \"community|acva:Arab_Empire|0\"\ : {\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118634\n\ \ },\n \"community|acva:Arabic_Architecture|0\": {\n \"acc_norm\":\ \ 0.5333333333333333,\n \"acc_norm_stderr\": 0.03581804596782233\n },\n\ \ \"community|acva:Arabic_Art|0\": {\n \"acc_norm\": 0.6512820512820513,\n\ \ \"acc_norm_stderr\": 0.034215338466705415\n },\n \"community|acva:Arabic_Astronomy|0\"\ : {\n \"acc_norm\": 0.5282051282051282,\n \"acc_norm_stderr\": 0.035840746749208334\n\ \ },\n \"community|acva:Arabic_Calligraphy|0\": {\n \"acc_norm\": 0.5137254901960784,\n\ \ \"acc_norm_stderr\": 0.0313609674469424\n },\n \"community|acva:Arabic_Ceremony|0\"\ : {\n \"acc_norm\": 0.4864864864864865,\n \"acc_norm_stderr\": 0.03684702401944814\n\ \ },\n \"community|acva:Arabic_Clothing|0\": {\n \"acc_norm\": 0.5025641025641026,\n\ \ \"acc_norm_stderr\": 0.035897435897435895\n },\n \"community|acva:Arabic_Culture|0\"\ : {\n \"acc_norm\": 0.7435897435897436,\n \"acc_norm_stderr\": 0.03134970994274493\n\ \ },\n \"community|acva:Arabic_Food|0\": {\n \"acc_norm\": 0.558974358974359,\n\ \ \"acc_norm_stderr\": 0.0356473293185358\n },\n \"community|acva:Arabic_Funeral|0\"\ : {\n \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.05149958471474543\n\ \ },\n \"community|acva:Arabic_Geography|0\": {\n \"acc_norm\": 0.41379310344827586,\n\ \ \"acc_norm_stderr\": 0.04104269211806232\n },\n \"community|acva:Arabic_History|0\"\ : {\n \"acc_norm\": 0.6820512820512821,\n \"acc_norm_stderr\": 0.03343383454355787\n\ \ },\n \"community|acva:Arabic_Language_Origin|0\": {\n \"acc_norm\"\ : 0.4842105263157895,\n \"acc_norm_stderr\": 0.05154534179593067\n },\n\ \ \"community|acva:Arabic_Literature|0\": {\n \"acc_norm\": 0.5310344827586206,\n\ \ \"acc_norm_stderr\": 0.04158632762097828\n },\n \"community|acva:Arabic_Math|0\"\ : {\n \"acc_norm\": 0.6974358974358974,\n \"acc_norm_stderr\": 0.03298070870085618\n\ \ },\n \"community|acva:Arabic_Medicine|0\": {\n \"acc_norm\": 0.5103448275862069,\n\ \ \"acc_norm_stderr\": 0.04165774775728763\n },\n \"community|acva:Arabic_Music|0\"\ : {\n \"acc_norm\": 0.7769784172661871,\n \"acc_norm_stderr\": 0.03543548499561939\n\ \ },\n \"community|acva:Arabic_Ornament|0\": {\n \"acc_norm\": 0.5333333333333333,\n\ \ \"acc_norm_stderr\": 0.03581804596782233\n },\n \"community|acva:Arabic_Philosophy|0\"\ : {\n \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"community|acva:Arabic_Physics_and_Chemistry|0\": {\n \"acc_norm\"\ : 0.5076923076923077,\n \"acc_norm_stderr\": 0.03589365940635213\n },\n\ \ \"community|acva:Arabic_Wedding|0\": {\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.035172622905632896\n },\n \"community|acva:Bahrain|0\"\ : {\n \"acc_norm\": 0.6888888888888889,\n \"acc_norm_stderr\": 0.06979205927323111\n\ \ },\n \"community|acva:Comoros|0\": {\n \"acc_norm\": 0.6222222222222222,\n\ \ \"acc_norm_stderr\": 0.07309112127323451\n },\n \"community|acva:Egypt_modern|0\"\ : {\n \"acc_norm\": 0.6736842105263158,\n \"acc_norm_stderr\": 0.04835966701461423\n\ \ },\n \"community|acva:InfluenceFromAncientEgypt|0\": {\n \"acc_norm\"\ : 0.39487179487179486,\n \"acc_norm_stderr\": 0.035095456022620375\n },\n\ \ \"community|acva:InfluenceFromByzantium|0\": {\n \"acc_norm\": 0.2827586206896552,\n\ \ \"acc_norm_stderr\": 0.03752833958003337\n },\n \"community|acva:InfluenceFromChina|0\"\ : {\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.03384487217112065\n\ \ },\n \"community|acva:InfluenceFromGreece|0\": {\n \"acc_norm\":\ \ 0.36923076923076925,\n \"acc_norm_stderr\": 0.034648411418637566\n },\n\ \ \"community|acva:InfluenceFromIslam|0\": {\n \"acc_norm\": 0.6896551724137931,\n\ \ \"acc_norm_stderr\": 0.03855289616378947\n },\n \"community|acva:InfluenceFromPersia|0\"\ : {\n \"acc_norm\": 0.3028571428571429,\n \"acc_norm_stderr\": 0.03483414676585985\n\ \ },\n \"community|acva:InfluenceFromRome|0\": {\n \"acc_norm\": 0.4307692307692308,\n\ \ \"acc_norm_stderr\": 0.035552132520587594\n },\n \"community|acva:Iraq|0\"\ : {\n \"acc_norm\": 0.5176470588235295,\n \"acc_norm_stderr\": 0.05452048340661897\n\ \ },\n \"community|acva:Islam_Education|0\": {\n \"acc_norm\": 0.558974358974359,\n\ \ \"acc_norm_stderr\": 0.03564732931853579\n },\n \"community|acva:Islam_branches_and_schools|0\"\ : {\n \"acc_norm\": 0.5028571428571429,\n \"acc_norm_stderr\": 0.037904283318347436\n\ \ },\n \"community|acva:Islamic_law_system|0\": {\n \"acc_norm\": 0.5948717948717949,\n\ \ \"acc_norm_stderr\": 0.03524577495610961\n },\n \"community|acva:Jordan|0\"\ : {\n \"acc_norm\": 0.6444444444444445,\n \"acc_norm_stderr\": 0.07216392363431012\n\ \ },\n \"community|acva:Kuwait|0\": {\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.06979205927323111\n },\n \"community|acva:Lebanon|0\"\ : {\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.06666666666666668\n\ \ },\n \"community|acva:Libya|0\": {\n \"acc_norm\": 0.5333333333333333,\n\ \ \"acc_norm_stderr\": 0.0752101433090355\n },\n \"community|acva:Mauritania|0\"\ : {\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.0752101433090355\n\ \ },\n \"community|acva:Mesopotamia_civilization|0\": {\n \"acc_norm\"\ : 0.49032258064516127,\n \"acc_norm_stderr\": 0.04028360076525542\n },\n\ \ \"community|acva:Morocco|0\": {\n \"acc_norm\": 0.7555555555555555,\n\ \ \"acc_norm_stderr\": 0.06478835438717\n },\n \"community|acva:Oman|0\"\ : {\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.06030226891555273\n\ \ },\n \"community|acva:Palestine|0\": {\n \"acc_norm\": 0.7411764705882353,\n\ \ \"acc_norm_stderr\": 0.04778846120374094\n },\n \"community|acva:Qatar|0\"\ : {\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.07446027270295806\n\ \ },\n \"community|acva:Saudi_Arabia|0\": {\n \"acc_norm\": 0.6717948717948717,\n\ \ \"acc_norm_stderr\": 0.03371243782413707\n },\n \"community|acva:Somalia|0\"\ : {\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.07491109582924915\n\ \ },\n \"community|acva:Sudan|0\": {\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.07216392363431012\n },\n \"community|acva:Syria|0\"\ : {\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.07106690545187012\n\ \ },\n \"community|acva:Tunisia|0\": {\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.07216392363431011\n },\n \"community|acva:United_Arab_Emirates|0\"\ : {\n \"acc_norm\": 0.7529411764705882,\n \"acc_norm_stderr\": 0.047058823529411785\n\ \ },\n \"community|acva:Yemen|0\": {\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.16329931618554522\n },\n \"community|acva:communication|0\"\ : {\n \"acc_norm\": 0.5879120879120879,\n \"acc_norm_stderr\": 0.0258343667152563\n\ \ },\n \"community|acva:computer_and_phone|0\": {\n \"acc_norm\": 0.5389830508474577,\n\ \ \"acc_norm_stderr\": 0.0290718276412662\n },\n \"community|acva:daily_life|0\"\ : {\n \"acc_norm\": 0.7270029673590505,\n \"acc_norm_stderr\": 0.024303980960050656\n\ \ },\n \"community|acva:entertainment|0\": {\n \"acc_norm\": 0.711864406779661,\n\ \ \"acc_norm_stderr\": 0.026413346524541644\n },\n \"community|alghafa:mcq_exams_test_ar|0\"\ : {\n \"acc_norm\": 0.2513464991023339,\n \"acc_norm_stderr\": 0.01839668001069939\n\ \ },\n \"community|alghafa:meta_ar_dialects|0\": {\n \"acc_norm\":\ \ 0.24411492122335496,\n \"acc_norm_stderr\": 0.005848837806074586\n },\n\ \ \"community|alghafa:meta_ar_msa|0\": {\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.014422292204808836\n },\n \"community|alghafa:multiple_choice_facts_truefalse_balanced_task|0\"\ : {\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.05807730170189531\n\ \ },\n \"community|alghafa:multiple_choice_grounded_statement_soqal_task|0\"\ : {\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.03622779862191887\n\ \ },\n \"community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0\"\ : {\n \"acc_norm\": 0.24666666666666667,\n \"acc_norm_stderr\": 0.03531471376356937\n\ \ },\n \"community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0\"\ : {\n \"acc_norm\": 0.499812382739212,\n \"acc_norm_stderr\": 0.005592267043694276\n\ \ },\n \"community|alghafa:multiple_choice_rating_sentiment_task|0\": {\n\ \ \"acc_norm\": 0.3402835696413678,\n \"acc_norm_stderr\": 0.006119849906257789\n\ \ },\n \"community|alghafa:multiple_choice_sentiment_task|0\": {\n \ \ \"acc_norm\": 0.33372093023255817,\n \"acc_norm_stderr\": 0.011373178876838192\n\ \ },\n \"community|arabic_exams|0\": {\n \"acc_norm\": 0.2383612662942272,\n\ \ \"acc_norm_stderr\": 0.01840390396129298\n },\n \"community|arabic_mmlu:abstract_algebra|0\"\ : {\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n\ \ },\n \"community|arabic_mmlu:anatomy|0\": {\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.03355677216313142\n },\n \"community|arabic_mmlu:astronomy|0\"\ : {\n \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n\ \ },\n \"community|arabic_mmlu:business_ethics|0\": {\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"community|arabic_mmlu:clinical_knowledge|0\"\ : {\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\n\ \ },\n \"community|arabic_mmlu:college_biology|0\": {\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"community|arabic_mmlu:college_chemistry|0\"\ : {\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n\ \ },\n \"community|arabic_mmlu:college_computer_science|0\": {\n \"\ acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \ \ \"community|arabic_mmlu:college_mathematics|0\": {\n \"acc_norm\": 0.24,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"community|arabic_mmlu:college_medicine|0\"\ : {\n \"acc_norm\": 0.2138728323699422,\n \"acc_norm_stderr\": 0.031265112061730445\n\ \ },\n \"community|arabic_mmlu:college_physics|0\": {\n \"acc_norm\"\ : 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n\ \ \"community|arabic_mmlu:computer_security|0\": {\n \"acc_norm\": 0.27,\n\ \ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"community|arabic_mmlu:conceptual_physics|0\"\ : {\n \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.029101290698386698\n\ \ },\n \"community|arabic_mmlu:econometrics|0\": {\n \"acc_norm\":\ \ 0.2719298245614035,\n \"acc_norm_stderr\": 0.04185774424022056\n },\n\ \ \"community|arabic_mmlu:electrical_engineering|0\": {\n \"acc_norm\"\ : 0.25517241379310346,\n \"acc_norm_stderr\": 0.03632984052707842\n },\n\ \ \"community|arabic_mmlu:elementary_mathematics|0\": {\n \"acc_norm\"\ : 0.21164021164021163,\n \"acc_norm_stderr\": 0.021037331505262886\n },\n\ \ \"community|arabic_mmlu:formal_logic|0\": {\n \"acc_norm\": 0.2619047619047619,\n\ \ \"acc_norm_stderr\": 0.03932537680392872\n },\n \"community|arabic_mmlu:global_facts|0\"\ : {\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n\ \ },\n \"community|arabic_mmlu:high_school_biology|0\": {\n \"acc_norm\"\ : 0.18064516129032257,\n \"acc_norm_stderr\": 0.021886178567172548\n },\n\ \ \"community|arabic_mmlu:high_school_chemistry|0\": {\n \"acc_norm\"\ : 0.1724137931034483,\n \"acc_norm_stderr\": 0.026577672183036572\n },\n\ \ \"community|arabic_mmlu:high_school_computer_science|0\": {\n \"acc_norm\"\ : 0.22,\n \"acc_norm_stderr\": 0.041633319989322716\n },\n \"community|arabic_mmlu:high_school_european_history|0\"\ : {\n \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"community|arabic_mmlu:high_school_geography|0\": {\n \"acc_norm\"\ : 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n },\n\ \ \"community|arabic_mmlu:high_school_government_and_politics|0\": {\n \ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"community|arabic_mmlu:high_school_macroeconomics|0\": {\n \ \ \"acc_norm\": 0.19743589743589743,\n \"acc_norm_stderr\": 0.020182646968674844\n\ \ },\n \"community|arabic_mmlu:high_school_mathematics|0\": {\n \"\ acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.02549753263960955\n\ \ },\n \"community|arabic_mmlu:high_school_microeconomics|0\": {\n \ \ \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715494\n\ \ },\n \"community|arabic_mmlu:high_school_physics|0\": {\n \"acc_norm\"\ : 0.2052980132450331,\n \"acc_norm_stderr\": 0.03297986648473836\n },\n\ \ \"community|arabic_mmlu:high_school_psychology|0\": {\n \"acc_norm\"\ : 0.1889908256880734,\n \"acc_norm_stderr\": 0.01678548115920364\n },\n\ \ \"community|arabic_mmlu:high_school_statistics|0\": {\n \"acc_norm\"\ : 0.1574074074074074,\n \"acc_norm_stderr\": 0.024837173518242387\n },\n\ \ \"community|arabic_mmlu:high_school_us_history|0\": {\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.03039153369274154\n },\n \"community|arabic_mmlu:high_school_world_history|0\"\ : {\n \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"community|arabic_mmlu:human_aging|0\": {\n \"acc_norm\": 0.29596412556053814,\n\ \ \"acc_norm_stderr\": 0.030636591348699813\n },\n \"community|arabic_mmlu:human_sexuality|0\"\ : {\n \"acc_norm\": 0.25190839694656486,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"community|arabic_mmlu:international_law|0\": {\n \"acc_norm\"\ : 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n },\n\ \ \"community|arabic_mmlu:jurisprudence|0\": {\n \"acc_norm\": 0.26851851851851855,\n\ \ \"acc_norm_stderr\": 0.04284467968052192\n },\n \"community|arabic_mmlu:logical_fallacies|0\"\ : {\n \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"community|arabic_mmlu:machine_learning|0\": {\n \"acc_norm\"\ : 0.32142857142857145,\n \"acc_norm_stderr\": 0.04432804055291519\n },\n\ \ \"community|arabic_mmlu:management|0\": {\n \"acc_norm\": 0.17475728155339806,\n\ \ \"acc_norm_stderr\": 0.037601780060266224\n },\n \"community|arabic_mmlu:marketing|0\"\ : {\n \"acc_norm\": 0.3034188034188034,\n \"acc_norm_stderr\": 0.03011821010694267\n\ \ },\n \"community|arabic_mmlu:medical_genetics|0\": {\n \"acc_norm\"\ : 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"community|arabic_mmlu:miscellaneous|0\"\ : {\n \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.015302380123542089\n\ \ },\n \"community|arabic_mmlu:moral_disputes|0\": {\n \"acc_norm\"\ : 0.2514450867052023,\n \"acc_norm_stderr\": 0.02335736578587404\n },\n\ \ \"community|arabic_mmlu:moral_scenarios|0\": {\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"community|arabic_mmlu:nutrition|0\"\ : {\n \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.02417084087934101\n\ \ },\n \"community|arabic_mmlu:philosophy|0\": {\n \"acc_norm\": 0.1864951768488746,\n\ \ \"acc_norm_stderr\": 0.02212243977248078\n },\n \"community|arabic_mmlu:prehistory|0\"\ : {\n \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.02289916291844581\n\ \ },\n \"community|arabic_mmlu:professional_accounting|0\": {\n \"\ acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.025770015644290396\n\ \ },\n \"community|arabic_mmlu:professional_law|0\": {\n \"acc_norm\"\ : 0.24511082138200782,\n \"acc_norm_stderr\": 0.010986307870045517\n },\n\ \ \"community|arabic_mmlu:professional_medicine|0\": {\n \"acc_norm\"\ : 0.19117647058823528,\n \"acc_norm_stderr\": 0.02388688192244034\n },\n\ \ \"community|arabic_mmlu:professional_psychology|0\": {\n \"acc_norm\"\ : 0.2549019607843137,\n \"acc_norm_stderr\": 0.017630827375148383\n },\n\ \ \"community|arabic_mmlu:public_relations|0\": {\n \"acc_norm\": 0.22727272727272727,\n\ \ \"acc_norm_stderr\": 0.04013964554072775\n },\n \"community|arabic_mmlu:security_studies|0\"\ : {\n \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.02500025603954621\n\ \ },\n \"community|arabic_mmlu:sociology|0\": {\n \"acc_norm\": 0.24378109452736318,\n\ \ \"acc_norm_stderr\": 0.03036049015401465\n },\n \"community|arabic_mmlu:us_foreign_policy|0\"\ : {\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n\ \ },\n \"community|arabic_mmlu:virology|0\": {\n \"acc_norm\": 0.28313253012048195,\n\ \ \"acc_norm_stderr\": 0.03507295431370518\n },\n \"community|arabic_mmlu:world_religions|0\"\ : {\n \"acc_norm\": 0.3216374269005848,\n \"acc_norm_stderr\": 0.03582529442573122\n\ \ },\n \"community|arc_challenge_okapi_ar|0\": {\n \"acc_norm\": 0.2525862068965517,\n\ \ \"acc_norm_stderr\": 0.012762732057795888\n },\n \"community|arc_easy_ar|0\"\ : {\n \"acc_norm\": 0.2593062605752961,\n \"acc_norm_stderr\": 0.00901558634852351\n\ \ },\n \"community|boolq_ar|0\": {\n \"acc_norm\": 0.40950920245398775,\n\ \ \"acc_norm_stderr\": 0.008613828474130074\n },\n \"community|copa_ext_ar|0\"\ : {\n \"acc_norm\": 0.4888888888888889,\n \"acc_norm_stderr\": 0.05298680599073449\n\ \ },\n \"community|hellaswag_okapi_ar|0\": {\n \"acc_norm\": 0.24882782684549123,\n\ \ \"acc_norm_stderr\": 0.004514758778737428\n },\n \"community|openbook_qa_ext_ar|0\"\ : {\n \"acc_norm\": 0.3656565656565657,\n \"acc_norm_stderr\": 0.021668828786750326\n\ \ },\n \"community|piqa_ar|0\": {\n \"acc_norm\": 0.5040916530278232,\n\ \ \"acc_norm_stderr\": 0.011681341688982008\n },\n \"community|race_ar|0\"\ : {\n \"acc_norm\": 0.2803814161087442,\n \"acc_norm_stderr\": 0.006398680832644407\n\ \ },\n \"community|sciq_ar|0\": {\n \"acc_norm\": 0.3085427135678392,\n\ \ \"acc_norm_stderr\": 0.0146503207768712\n },\n \"community|toxigen_ar|0\"\ : {\n \"acc_norm\": 0.4320855614973262,\n \"acc_norm_stderr\": 0.01620887578524445\n\ \ },\n \"lighteval|xstory_cloze:ar|0\": {\n \"acc\": 0.47187293183322304,\n\ \ \"acc_stderr\": 0.012846749995797692\n },\n \"community|acva:_average|0\"\ : {\n \"acc_norm\": 0.5823110081794227,\n \"acc_norm_stderr\": 0.04701755519626288\n\ \ },\n \"community|alghafa:_average|0\": {\n \"acc_norm\": 0.3277265567304262,\n\ \ \"acc_norm_stderr\": 0.021263657770639627\n },\n \"community|arabic_mmlu:_average|0\"\ : {\n \"acc_norm\": 0.23589525196402603,\n \"acc_norm_stderr\": 0.03174802148728372\n\ \ }\n}\n```" repo_url: https://huggingface.co/Artples/L-MChat-Small configs: - config_name: community_acva_Algeria_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Algeria|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Algeria|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Ancient_Egypt_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Ancient_Egypt|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Ancient_Egypt|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arab_Empire_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arab_Empire|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arab_Empire|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Architecture_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Architecture|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Architecture|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Art_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Art|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Art|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Astronomy_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Astronomy|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Astronomy|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Calligraphy_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Calligraphy|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Calligraphy|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Ceremony_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Ceremony|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Ceremony|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Clothing_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Clothing|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Clothing|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Culture_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Culture|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Culture|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Food_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Food|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Food|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Funeral_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Funeral|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Funeral|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Geography_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Geography|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Geography|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_History_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_History|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_History|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Language_Origin_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Language_Origin|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Language_Origin|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Literature_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Literature|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Literature|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Math_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Math|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Math|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Medicine_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Medicine|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Medicine|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Music_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Music|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Music|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Ornament_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Ornament|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Ornament|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Philosophy_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Philosophy|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Philosophy|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Physics_and_Chemistry_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Physics_and_Chemistry|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Physics_and_Chemistry|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Arabic_Wedding_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Arabic_Wedding|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Arabic_Wedding|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Bahrain_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Bahrain|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Bahrain|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Comoros_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Comoros|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Comoros|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Egypt_modern_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Egypt_modern|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Egypt_modern|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_InfluenceFromAncientEgypt_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:InfluenceFromAncientEgypt|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromAncientEgypt|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_InfluenceFromByzantium_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:InfluenceFromByzantium|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromByzantium|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_InfluenceFromChina_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:InfluenceFromChina|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromChina|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_InfluenceFromGreece_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:InfluenceFromGreece|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromGreece|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_InfluenceFromIslam_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:InfluenceFromIslam|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromIslam|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_InfluenceFromPersia_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:InfluenceFromPersia|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromPersia|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_InfluenceFromRome_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:InfluenceFromRome|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:InfluenceFromRome|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Iraq_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Iraq|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Iraq|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Islam_Education_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Islam_Education|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Islam_Education|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Islam_branches_and_schools_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Islam_branches_and_schools|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Islam_branches_and_schools|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Islamic_law_system_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Islamic_law_system|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Islamic_law_system|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Jordan_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Jordan|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Jordan|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Kuwait_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Kuwait|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Kuwait|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Lebanon_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Lebanon|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Lebanon|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Libya_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Libya|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Libya|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Mauritania_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Mauritania|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Mauritania|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Mesopotamia_civilization_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Mesopotamia_civilization|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Mesopotamia_civilization|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Morocco_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Morocco|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Morocco|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Oman_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Oman|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Oman|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Palestine_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Palestine|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Palestine|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Qatar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Qatar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Qatar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Saudi_Arabia_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Saudi_Arabia|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Saudi_Arabia|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Somalia_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Somalia|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Somalia|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Sudan_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Sudan|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Sudan|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Syria_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Syria|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Syria|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Tunisia_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Tunisia|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Tunisia|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_United_Arab_Emirates_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:United_Arab_Emirates|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:United_Arab_Emirates|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_Yemen_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:Yemen|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:Yemen|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_communication_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:communication|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:communication|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_computer_and_phone_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:computer_and_phone|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:computer_and_phone|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_daily_life_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:daily_life|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:daily_life|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_acva_entertainment_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|acva:entertainment|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|acva:entertainment|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_mcq_exams_test_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:mcq_exams_test_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:mcq_exams_test_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_meta_ar_dialects_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:meta_ar_dialects|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:meta_ar_dialects|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_meta_ar_msa_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:meta_ar_msa|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:meta_ar_msa|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_multiple_choice_facts_truefalse_balanced_task_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:multiple_choice_facts_truefalse_balanced_task|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_facts_truefalse_balanced_task|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_multiple_choice_grounded_statement_soqal_task_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:multiple_choice_grounded_statement_soqal_task|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_grounded_statement_soqal_task|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_multiple_choice_grounded_statement_xglue_mlqa_task_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_multiple_choice_rating_sentiment_no_neutral_task_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_multiple_choice_rating_sentiment_task_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_task|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_rating_sentiment_task|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_alghafa_multiple_choice_sentiment_task_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|alghafa:multiple_choice_sentiment_task|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|alghafa:multiple_choice_sentiment_task|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_exams_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_exams|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_exams|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_abstract_algebra_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:abstract_algebra|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:abstract_algebra|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_anatomy_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:anatomy|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:anatomy|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_astronomy_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:astronomy|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:astronomy|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_business_ethics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:business_ethics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:business_ethics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_clinical_knowledge_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:clinical_knowledge|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:clinical_knowledge|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_college_biology_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:college_biology|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_biology|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_college_chemistry_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:college_chemistry|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_chemistry|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_college_computer_science_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:college_computer_science|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_computer_science|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_college_mathematics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:college_mathematics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_mathematics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_college_medicine_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:college_medicine|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_medicine|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_college_physics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:college_physics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:college_physics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_computer_security_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:computer_security|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:computer_security|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_conceptual_physics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:conceptual_physics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:conceptual_physics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_econometrics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:econometrics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:econometrics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_electrical_engineering_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:electrical_engineering|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:electrical_engineering|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_elementary_mathematics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:elementary_mathematics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:elementary_mathematics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_formal_logic_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:formal_logic|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:formal_logic|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_global_facts_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:global_facts|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:global_facts|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_biology_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_biology|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_biology|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_chemistry_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_chemistry|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_chemistry|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_computer_science_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_computer_science|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_computer_science|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_european_history_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_european_history|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_european_history|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_geography_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_geography|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_geography|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_government_and_politics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_government_and_politics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_government_and_politics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_macroeconomics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_macroeconomics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_macroeconomics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_mathematics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_mathematics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_mathematics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_microeconomics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_microeconomics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_microeconomics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_physics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_physics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_physics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_psychology_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_psychology|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_psychology|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_statistics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_statistics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_statistics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_us_history_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_us_history|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_us_history|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_high_school_world_history_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:high_school_world_history|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:high_school_world_history|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_human_aging_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:human_aging|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:human_aging|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_human_sexuality_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:human_sexuality|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:human_sexuality|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_international_law_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:international_law|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:international_law|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_jurisprudence_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:jurisprudence|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:jurisprudence|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_logical_fallacies_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:logical_fallacies|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:logical_fallacies|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_machine_learning_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:machine_learning|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:machine_learning|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_management_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:management|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:management|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_marketing_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:marketing|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:marketing|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_medical_genetics_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:medical_genetics|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:medical_genetics|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_miscellaneous_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:miscellaneous|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:miscellaneous|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_moral_disputes_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:moral_disputes|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:moral_disputes|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_moral_scenarios_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:moral_scenarios|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:moral_scenarios|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_nutrition_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:nutrition|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:nutrition|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_philosophy_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:philosophy|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:philosophy|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_prehistory_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:prehistory|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:prehistory|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_professional_accounting_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:professional_accounting|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_accounting|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_professional_law_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:professional_law|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_law|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_professional_medicine_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:professional_medicine|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_medicine|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_professional_psychology_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:professional_psychology|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:professional_psychology|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_public_relations_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:public_relations|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:public_relations|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_security_studies_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:security_studies|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:security_studies|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_sociology_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:sociology|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:sociology|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_us_foreign_policy_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:us_foreign_policy|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:us_foreign_policy|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_virology_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:virology|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:virology|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arabic_mmlu_world_religions_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arabic_mmlu:world_religions|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arabic_mmlu:world_religions|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arc_challenge_okapi_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arc_challenge_okapi_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arc_challenge_okapi_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_arc_easy_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|arc_easy_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|arc_easy_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_boolq_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|boolq_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|boolq_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_copa_ext_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|copa_ext_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|copa_ext_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_hellaswag_okapi_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|hellaswag_okapi_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|hellaswag_okapi_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_openbook_qa_ext_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|openbook_qa_ext_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|openbook_qa_ext_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_piqa_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|piqa_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|piqa_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_race_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|race_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|race_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_sciq_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|sciq_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|sciq_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: community_toxigen_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_community|toxigen_ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_community|toxigen_ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: lighteval_xstory_cloze_ar_0 data_files: - split: 2024_05_27T14_39_45.128547 path: - '**/details_lighteval|xstory_cloze:ar|0_2024-05-27T14-39-45.128547.parquet' - split: latest path: - '**/details_lighteval|xstory_cloze:ar|0_2024-05-27T14-39-45.128547.parquet' - config_name: results data_files: - split: 2024_05_27T14_39_45.128547 path: - results_2024-05-27T14-39-45.128547.parquet - split: latest path: - results_2024-05-27T14-39-45.128547.parquet --- # Dataset Card for Evaluation run of Artples/L-MChat-Small <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Artples/L-MChat-Small](https://huggingface.co/Artples/L-MChat-Small). The dataset is composed of 136 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. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("OALL/details_Artples__L-MChat-Small", "lighteval_xstory_cloze_ar_0", split="train") ``` ## Latest results These are the [latest results from run 2024-05-27T14:39:45.128547](https://huggingface.co/datasets/OALL/details_Artples__L-MChat-Small/blob/main/results_2024-05-27T14-39-45.128547.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_norm": 0.3996877363610559, "acc_norm_stderr": 0.0363328445153769, "acc": 0.47187293183322304, "acc_stderr": 0.012846749995797692 }, "community|acva:Algeria|0": { "acc_norm": 0.48717948717948717, "acc_norm_stderr": 0.03588610523192216 }, "community|acva:Ancient_Egypt|0": { "acc_norm": 0.9015873015873016, "acc_norm_stderr": 0.01680988100419675 }, "community|acva:Arab_Empire|0": { "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.028450154794118634 }, "community|acva:Arabic_Architecture|0": { "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.03581804596782233 }, "community|acva:Arabic_Art|0": { "acc_norm": 0.6512820512820513, "acc_norm_stderr": 0.034215338466705415 }, "community|acva:Arabic_Astronomy|0": { "acc_norm": 0.5282051282051282, "acc_norm_stderr": 0.035840746749208334 }, "community|acva:Arabic_Calligraphy|0": { "acc_norm": 0.5137254901960784, "acc_norm_stderr": 0.0313609674469424 }, "community|acva:Arabic_Ceremony|0": { "acc_norm": 0.4864864864864865, "acc_norm_stderr": 0.03684702401944814 }, "community|acva:Arabic_Clothing|0": { "acc_norm": 0.5025641025641026, "acc_norm_stderr": 0.035897435897435895 }, "community|acva:Arabic_Culture|0": { "acc_norm": 0.7435897435897436, "acc_norm_stderr": 0.03134970994274493 }, "community|acva:Arabic_Food|0": { "acc_norm": 0.558974358974359, "acc_norm_stderr": 0.0356473293185358 }, "community|acva:Arabic_Funeral|0": { "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.05149958471474543 }, "community|acva:Arabic_Geography|0": { "acc_norm": 0.41379310344827586, "acc_norm_stderr": 0.04104269211806232 }, "community|acva:Arabic_History|0": { "acc_norm": 0.6820512820512821, "acc_norm_stderr": 0.03343383454355787 }, "community|acva:Arabic_Language_Origin|0": { "acc_norm": 0.4842105263157895, "acc_norm_stderr": 0.05154534179593067 }, "community|acva:Arabic_Literature|0": { "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "community|acva:Arabic_Math|0": { "acc_norm": 0.6974358974358974, "acc_norm_stderr": 0.03298070870085618 }, "community|acva:Arabic_Medicine|0": { "acc_norm": 0.5103448275862069, "acc_norm_stderr": 0.04165774775728763 }, "community|acva:Arabic_Music|0": { "acc_norm": 0.7769784172661871, "acc_norm_stderr": 0.03543548499561939 }, "community|acva:Arabic_Ornament|0": { "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.03581804596782233 }, "community|acva:Arabic_Philosophy|0": { "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.04154659671707548 }, "community|acva:Arabic_Physics_and_Chemistry|0": { "acc_norm": 0.5076923076923077, "acc_norm_stderr": 0.03589365940635213 }, "community|acva:Arabic_Wedding|0": { "acc_norm": 0.6, "acc_norm_stderr": 0.035172622905632896 }, "community|acva:Bahrain|0": { "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.06979205927323111 }, "community|acva:Comoros|0": { "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.07309112127323451 }, "community|acva:Egypt_modern|0": { "acc_norm": 0.6736842105263158, "acc_norm_stderr": 0.04835966701461423 }, "community|acva:InfluenceFromAncientEgypt|0": { "acc_norm": 0.39487179487179486, "acc_norm_stderr": 0.035095456022620375 }, "community|acva:InfluenceFromByzantium|0": { "acc_norm": 0.2827586206896552, "acc_norm_stderr": 0.03752833958003337 }, "community|acva:InfluenceFromChina|0": { "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.03384487217112065 }, "community|acva:InfluenceFromGreece|0": { "acc_norm": 0.36923076923076925, "acc_norm_stderr": 0.034648411418637566 }, "community|acva:InfluenceFromIslam|0": { "acc_norm": 0.6896551724137931, "acc_norm_stderr": 0.03855289616378947 }, "community|acva:InfluenceFromPersia|0": { "acc_norm": 0.3028571428571429, "acc_norm_stderr": 0.03483414676585985 }, "community|acva:InfluenceFromRome|0": { "acc_norm": 0.4307692307692308, "acc_norm_stderr": 0.035552132520587594 }, "community|acva:Iraq|0": { "acc_norm": 0.5176470588235295, "acc_norm_stderr": 0.05452048340661897 }, "community|acva:Islam_Education|0": { "acc_norm": 0.558974358974359, "acc_norm_stderr": 0.03564732931853579 }, "community|acva:Islam_branches_and_schools|0": { "acc_norm": 0.5028571428571429, "acc_norm_stderr": 0.037904283318347436 }, "community|acva:Islamic_law_system|0": { "acc_norm": 0.5948717948717949, "acc_norm_stderr": 0.03524577495610961 }, "community|acva:Jordan|0": { "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.07216392363431012 }, "community|acva:Kuwait|0": { "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.06979205927323111 }, "community|acva:Lebanon|0": { "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.06666666666666668 }, "community|acva:Libya|0": { "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.0752101433090355 }, "community|acva:Mauritania|0": { "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.0752101433090355 }, "community|acva:Mesopotamia_civilization|0": { "acc_norm": 0.49032258064516127, "acc_norm_stderr": 0.04028360076525542 }, "community|acva:Morocco|0": { "acc_norm": 0.7555555555555555, "acc_norm_stderr": 0.06478835438717 }, "community|acva:Oman|0": { "acc_norm": 0.8, "acc_norm_stderr": 0.06030226891555273 }, "community|acva:Palestine|0": { "acc_norm": 0.7411764705882353, "acc_norm_stderr": 0.04778846120374094 }, "community|acva:Qatar|0": { "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.07446027270295806 }, "community|acva:Saudi_Arabia|0": { "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.03371243782413707 }, "community|acva:Somalia|0": { "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.07491109582924915 }, "community|acva:Sudan|0": { "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.07216392363431012 }, "community|acva:Syria|0": { "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.07106690545187012 }, "community|acva:Tunisia|0": { "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.07216392363431011 }, "community|acva:United_Arab_Emirates|0": { "acc_norm": 0.7529411764705882, "acc_norm_stderr": 0.047058823529411785 }, "community|acva:Yemen|0": { "acc_norm": 0.6, "acc_norm_stderr": 0.16329931618554522 }, "community|acva:communication|0": { "acc_norm": 0.5879120879120879, "acc_norm_stderr": 0.0258343667152563 }, "community|acva:computer_and_phone|0": { "acc_norm": 0.5389830508474577, "acc_norm_stderr": 0.0290718276412662 }, "community|acva:daily_life|0": { "acc_norm": 0.7270029673590505, "acc_norm_stderr": 0.024303980960050656 }, "community|acva:entertainment|0": { "acc_norm": 0.711864406779661, "acc_norm_stderr": 0.026413346524541644 }, "community|alghafa:mcq_exams_test_ar|0": { "acc_norm": 0.2513464991023339, "acc_norm_stderr": 0.01839668001069939 }, "community|alghafa:meta_ar_dialects|0": { "acc_norm": 0.24411492122335496, "acc_norm_stderr": 0.005848837806074586 }, "community|alghafa:meta_ar_msa|0": { "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808836 }, "community|alghafa:multiple_choice_facts_truefalse_balanced_task|0": { "acc_norm": 0.52, "acc_norm_stderr": 0.05807730170189531 }, "community|alghafa:multiple_choice_grounded_statement_soqal_task|0": { "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.03622779862191887 }, "community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|0": { "acc_norm": 0.24666666666666667, "acc_norm_stderr": 0.03531471376356937 }, "community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|0": { "acc_norm": 0.499812382739212, "acc_norm_stderr": 0.005592267043694276 }, "community|alghafa:multiple_choice_rating_sentiment_task|0": { "acc_norm": 0.3402835696413678, "acc_norm_stderr": 0.006119849906257789 }, "community|alghafa:multiple_choice_sentiment_task|0": { "acc_norm": 0.33372093023255817, "acc_norm_stderr": 0.011373178876838192 }, "community|arabic_exams|0": { "acc_norm": 0.2383612662942272, "acc_norm_stderr": 0.01840390396129298 }, "community|arabic_mmlu:abstract_algebra|0": { "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "community|arabic_mmlu:anatomy|0": { "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03355677216313142 }, "community|arabic_mmlu:astronomy|0": { "acc_norm": 0.18421052631578946, "acc_norm_stderr": 0.0315469804508223 }, "community|arabic_mmlu:business_ethics|0": { "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "community|arabic_mmlu:clinical_knowledge|0": { "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.02528839450289137 }, "community|arabic_mmlu:college_biology|0": { "acc_norm": 0.25, "acc_norm_stderr": 0.03621034121889507 }, "community|arabic_mmlu:college_chemistry|0": { "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366234 }, "community|arabic_mmlu:college_computer_science|0": { "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "community|arabic_mmlu:college_mathematics|0": { "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "community|arabic_mmlu:college_medicine|0": { "acc_norm": 0.2138728323699422, "acc_norm_stderr": 0.031265112061730445 }, "community|arabic_mmlu:college_physics|0": { "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "community|arabic_mmlu:computer_security|0": { "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "community|arabic_mmlu:conceptual_physics|0": { "acc_norm": 0.2723404255319149, "acc_norm_stderr": 0.029101290698386698 }, "community|arabic_mmlu:econometrics|0": { "acc_norm": 0.2719298245614035, "acc_norm_stderr": 0.04185774424022056 }, "community|arabic_mmlu:electrical_engineering|0": { "acc_norm": 0.25517241379310346, "acc_norm_stderr": 0.03632984052707842 }, "community|arabic_mmlu:elementary_mathematics|0": { "acc_norm": 0.21164021164021163, "acc_norm_stderr": 0.021037331505262886 }, "community|arabic_mmlu:formal_logic|0": { "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.03932537680392872 }, "community|arabic_mmlu:global_facts|0": { "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366234 }, "community|arabic_mmlu:high_school_biology|0": { "acc_norm": 0.18064516129032257, "acc_norm_stderr": 0.021886178567172548 }, "community|arabic_mmlu:high_school_chemistry|0": { "acc_norm": 0.1724137931034483, "acc_norm_stderr": 0.026577672183036572 }, "community|arabic_mmlu:high_school_computer_science|0": { "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322716 }, "community|arabic_mmlu:high_school_european_history|0": { "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "community|arabic_mmlu:high_school_geography|0": { "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "community|arabic_mmlu:high_school_government_and_politics|0": { "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "community|arabic_mmlu:high_school_macroeconomics|0": { "acc_norm": 0.19743589743589743, "acc_norm_stderr": 0.020182646968674844 }, "community|arabic_mmlu:high_school_mathematics|0": { "acc_norm": 0.22592592592592592, "acc_norm_stderr": 0.02549753263960955 }, "community|arabic_mmlu:high_school_microeconomics|0": { "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.026653531596715494 }, "community|arabic_mmlu:high_school_physics|0": { "acc_norm": 0.2052980132450331, "acc_norm_stderr": 0.03297986648473836 }, "community|arabic_mmlu:high_school_psychology|0": { "acc_norm": 0.1889908256880734, "acc_norm_stderr": 0.01678548115920364 }, "community|arabic_mmlu:high_school_statistics|0": { "acc_norm": 0.1574074074074074, "acc_norm_stderr": 0.024837173518242387 }, "community|arabic_mmlu:high_school_us_history|0": { "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "community|arabic_mmlu:high_school_world_history|0": { "acc_norm": 0.270042194092827, "acc_norm_stderr": 0.028900721906293426 }, "community|arabic_mmlu:human_aging|0": { "acc_norm": 0.29596412556053814, "acc_norm_stderr": 0.030636591348699813 }, "community|arabic_mmlu:human_sexuality|0": { "acc_norm": 0.25190839694656486, "acc_norm_stderr": 0.03807387116306086 }, "community|arabic_mmlu:international_law|0": { "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "community|arabic_mmlu:jurisprudence|0": { "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.04284467968052192 }, "community|arabic_mmlu:logical_fallacies|0": { "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "community|arabic_mmlu:machine_learning|0": { "acc_norm": 0.32142857142857145, "acc_norm_stderr": 0.04432804055291519 }, "community|arabic_mmlu:management|0": { "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "community|arabic_mmlu:marketing|0": { "acc_norm": 0.3034188034188034, "acc_norm_stderr": 0.03011821010694267 }, "community|arabic_mmlu:medical_genetics|0": { "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "community|arabic_mmlu:miscellaneous|0": { "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.015302380123542089 }, "community|arabic_mmlu:moral_disputes|0": { "acc_norm": 0.2514450867052023, "acc_norm_stderr": 0.02335736578587404 }, "community|arabic_mmlu:moral_scenarios|0": { "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "community|arabic_mmlu:nutrition|0": { "acc_norm": 0.23202614379084968, "acc_norm_stderr": 0.02417084087934101 }, "community|arabic_mmlu:philosophy|0": { "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248078 }, "community|arabic_mmlu:prehistory|0": { "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.02289916291844581 }, "community|arabic_mmlu:professional_accounting|0": { "acc_norm": 0.24822695035460993, "acc_norm_stderr": 0.025770015644290396 }, "community|arabic_mmlu:professional_law|0": { "acc_norm": 0.24511082138200782, "acc_norm_stderr": 0.010986307870045517 }, "community|arabic_mmlu:professional_medicine|0": { "acc_norm": 0.19117647058823528, "acc_norm_stderr": 0.02388688192244034 }, "community|arabic_mmlu:professional_psychology|0": { "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.017630827375148383 }, "community|arabic_mmlu:public_relations|0": { "acc_norm": 0.22727272727272727, "acc_norm_stderr": 0.04013964554072775 }, "community|arabic_mmlu:security_studies|0": { "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "community|arabic_mmlu:sociology|0": { "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "community|arabic_mmlu:us_foreign_policy|0": { "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "community|arabic_mmlu:virology|0": { "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370518 }, "community|arabic_mmlu:world_religions|0": { "acc_norm": 0.3216374269005848, "acc_norm_stderr": 0.03582529442573122 }, "community|arc_challenge_okapi_ar|0": { "acc_norm": 0.2525862068965517, "acc_norm_stderr": 0.012762732057795888 }, "community|arc_easy_ar|0": { "acc_norm": 0.2593062605752961, "acc_norm_stderr": 0.00901558634852351 }, "community|boolq_ar|0": { "acc_norm": 0.40950920245398775, "acc_norm_stderr": 0.008613828474130074 }, "community|copa_ext_ar|0": { "acc_norm": 0.4888888888888889, "acc_norm_stderr": 0.05298680599073449 }, "community|hellaswag_okapi_ar|0": { "acc_norm": 0.24882782684549123, "acc_norm_stderr": 0.004514758778737428 }, "community|openbook_qa_ext_ar|0": { "acc_norm": 0.3656565656565657, "acc_norm_stderr": 0.021668828786750326 }, "community|piqa_ar|0": { "acc_norm": 0.5040916530278232, "acc_norm_stderr": 0.011681341688982008 }, "community|race_ar|0": { "acc_norm": 0.2803814161087442, "acc_norm_stderr": 0.006398680832644407 }, "community|sciq_ar|0": { "acc_norm": 0.3085427135678392, "acc_norm_stderr": 0.0146503207768712 }, "community|toxigen_ar|0": { "acc_norm": 0.4320855614973262, "acc_norm_stderr": 0.01620887578524445 }, "lighteval|xstory_cloze:ar|0": { "acc": 0.47187293183322304, "acc_stderr": 0.012846749995797692 }, "community|acva:_average|0": { "acc_norm": 0.5823110081794227, "acc_norm_stderr": 0.04701755519626288 }, "community|alghafa:_average|0": { "acc_norm": 0.3277265567304262, "acc_norm_stderr": 0.021263657770639627 }, "community|arabic_mmlu:_average|0": { "acc_norm": 0.23589525196402603, "acc_norm_stderr": 0.03174802148728372 } } ``` ## 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]

haixuantao/wrist_gripper

haixuantao

--- license: apache-2.0 dataset_info: features: - name: index dtype: int64 - name: observation.images.cam_right_wrist dtype: video_frame - name: observation.velocity sequence: float32 length: 14 - name: observation.images.cam_high dtype: video_frame - name: observation.state sequence: float32 length: 14 - name: observation.images.cam_left_wrist dtype: video_frame - name: observation.effort sequence: float32 length: 14 - name: action sequence: float32 length: 14 - name: episode_index dtype: int64 - name: observation.images.cam_low dtype: video_frame - name: frame_index dtype: int64 - name: next.done dtype: bool - name: timestamp dtype: float32 splits: - name: train num_bytes: 6243840 num_examples: 12288 download_size: 730984 dataset_size: 6243840 configs: - config_name: default data_files: - split: train path: data/train-* ---

ibivibiv/summary_instruct

ibivibiv

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 925781633 num_examples: 320939 - name: validation num_bytes: 51556561 num_examples: 17935 - name: test num_bytes: 51517414 num_examples: 17830 download_size: 649422645 dataset_size: 1028855608 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

alexandreteles/confessio_fraternitatis_multiturn

alexandreteles

--- license: c-uda dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 121531 num_examples: 66 download_size: 35719 dataset_size: 121531 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-generation language: - en tags: - spirituality - occultism - esoterism size_categories: - n<1K pretty_name: Confessio Fraternitatis (multiturn) --- # Confessio Fraternitatis Multiturn Conversation Dataset ## Overview This dataset consists of structured multiturn conversations modeled around the esoteric and philosophical themes of the "Confessio Fraternitatis." The text, known for its deep allegorical content, serves as the foundation for generating dialogues that involve rigorous inquiry into the occult and philosophical. ## Objective The primary objective of this dataset is to facilitate the development and testing of AI models specialized in understanding and generating responses grounded in esoteric philosophy. By providing a rich set of dialogues based on the "Confessio Fraternitatis," the dataset aims to promote deeper intellectual engagement with historic philosophical texts and their contemporary relevance in esoteric studies. ## Access The dataset is available under the [c-uda](https://github.com/microsoft/Computational-Use-of-Data-Agreement/blob/master/C-UDA-1.0.md) license, intended for educational and research purposes.. ## Contribution Contributions to the dataset are welcome, especially those that expand the depth and range of philosophical inquiries.

rakshya34/filtered_voice_english_v1.1_tag_5k

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: text_description dtype: string splits: - name: train num_bytes: 3124403 num_examples: 5000 download_size: 1151539 dataset_size: 3124403 configs: - config_name: default data_files: - split: train path: data/train-* ---

anomievision/filename-meta

anomievision

--- language: - en license: cc-by-4.0 --- # ProjectM Datasets *This respository contains custom datasets, tailered for the [ProjectM](https://github.com/projectM-visualizer/projectm) community.* ### Datasets - 4000-preset-filename-prompts - Name - Authors - Prompt - Results

rakshya34/filtered_voice_english_v1.2_tag_5k

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: text_description dtype: string splits: - name: train num_bytes: 3138513 num_examples: 5000 download_size: 1174684 dataset_size: 3138513 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.10_tag_5k

rakshya34

--- language: - en dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: text_description dtype: string splits: - name: train num_bytes: 3151169 num_examples: 5000 download_size: 1172392 dataset_size: 3151169 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.11_tag_5k

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: text_description dtype: string splits: - name: train num_bytes: 3152736 num_examples: 5000 download_size: 1171259 dataset_size: 3152736 configs: - config_name: default data_files: - split: train path: data/train-* ---

rakshya34/filtered_voice_english_v1.12_tag_5k

rakshya34

--- dataset_info: features: - name: path dtype: string - name: text dtype: string - name: normalized_text dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: string - name: phonemes dtype: string - name: noise dtype: string - name: reverberation dtype: string - name: speech_monotony dtype: string - name: text_description dtype: string splits: - name: train num_bytes: 3148031 num_examples: 5000 download_size: 1167308 dataset_size: 3148031 configs: - config_name: default data_files: - split: train path: data/train-* ---