Datasets:
davanstrien
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summary-test

Dataset card Data Studio Files
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ai-ml-ops-eng/ru-quiz-qa
--- license: unknown ---
David-Egea/phishing-texts
--- license: mit task_categories: - text-classification language: - en size_categories: - 10K<n<100K tags: - phishing - text pretty_name: Phishing Texts Dataset --- ## Phishing Texts Dataset 🎣 ### Description: This dataset is a collection of data designed for training text classifiers capable of determining whether a message or email is a phishing attempt or not. ### Dataset Information 📨: The dataset consists of more than 20,000 entries of text messages, which are potential phishing attempts. Data is structured in two columns: - `text`: The text of the message or email. - `phising`: An indicator of whether the message in the `text` column is a phishing attempt (1) or not (0). The dataset has undergone a data cleaning process and preprocessing to remove possible duplicate entries. It is worth mentioning that the dataset is **balanced**, with 62% non-phishing and 38% phishing instances. In some of the aforementioned datasets, it was identified that the data overlapped. To avoid redundant values, duplicate entries have been removed from this dataset during the last data cleaning phase. ### Data Sources 📖: This dataset has been constructed from the following sources: - [Hugging Face - Phishing Email Dataset](https://huggingface.co/datasets/zefang-liu/phishing-email-dataset) - [Hugging Face - Phishing Dataset](https://huggingface.co/datasets/ealvaradob/phishing-dataset) - [Kaggle - Phishing Emails](https://www.kaggle.com/datasets/subhajournal/phishingemails) - [Kaggle - Phishing Email Data by Type](https://www.kaggle.com/datasets/charlottehall/phishing-email-data-by-type) > Big thanks to all the creators of these datasets for their awesome work! 🙌 *In some of the aforementioned datasets, it was identified that the data overlapped. To avoid redundant values, duplicate entries have been removed from this dataset during the last data cleaning phase.*
zen-E/ANLI-simcse-roberta-large-embeddings-pca-256
--- task_categories: - sentence-similarity language: - en size_categories: - 100K<n<1M --- A dataset that contains all data except those labeled as 'neutral' in 'https://sbert.net/datasets/AllNLI.tsv.gz'' which the corresponding text embedding produced by 'princeton-nlp/unsup-simcse-roberta-large'. The features are transformed to a size of 256 by the PCA object. In order to load the dictionary of the teacher embeddings corresponding to the anli dataset: ```python !git clone https://huggingface.co/datasets/zen-E/ANLI-simcse-roberta-large-embeddings-pca-256 # if dimension reduction to 256 is required import joblib pca = joblib.load('ANLI-simcse-roberta-large-embeddings-pca-256/pca_model.sav') teacher_embeddings = torch.load("./ANLI-simcse-roberta-large-embeddings-pca-256/anli_train_simcse_robertra_sent_embed.pt") if pca is not None: all_sents = sorted(teacher_embeddings.keys()) teacher_embeddings_values = torch.stack([teacher_embeddings[s] for s in all_sents], dim=0).numpy() teacher_embeddings_values_trans = pca.transform(teacher_embeddings_values) teacher_embeddings = {k:torch.tensor(v) for k, v in zip(all_sents, teacher_embeddings_values_trans)} ```
chenghao/NEWS-COPY-train
--- dataset_info: features: - name: Text 1 dtype: string - name: Text 2 dtype: string - name: Label dtype: string - name: split dtype: string splits: - name: train num_bytes: 285532211 num_examples: 73928 - name: dev num_bytes: 18222482 num_examples: 6288 download_size: 131881405 dataset_size: 303754693 configs: - config_name: default data_files: - split: train path: data/train-* - split: dev path: data/dev-* license: unknown --- # NEWS COPY This dataset contains the trianing sets for the NEWS COPY dataset. Original source can be found at [Github](https://github.com/dell-research-harvard/NEWS-COPY). The license is unclear. It contains the following data: - Historical Newspapers Evaluation datasets can be found at [chenghao/NEWS-COPY-eval](https://huggingface.co/datasets/chenghao/NEWS-COPY-eval/). ## Citation ``` @inproceedings{silcock-etal-2020-noise, title = "Noise-Robust De-Duplication at Scale", author = "Silcock, Emily and D'Amico-Wong, Luca and Yang, Jinglin and Dell, Melissa", booktitle = "International Conference on Learning Representations (ICLR)", year = "2023", } ```
Yorai/detect-waste_loading_script
--- dataset_info: config_name: taco-multi features: - name: image_id dtype: int64 - name: image dtype: image - name: width dtype: int32 - name: height dtype: int32 - name: objects sequence: - name: id dtype: int64 - name: area dtype: int64 - name: bbox sequence: float32 length: 4 - name: category dtype: class_label: names: '0': metals_and_plastic '1': other '2': non_recyclable '3': glass '4': paper '5': bio '6': unknown splits: - name: train num_bytes: 1006510 num_examples: 3647 - name: test num_bytes: 248312 num_examples: 915 download_size: 10265127938 dataset_size: 1254822 language: - en tags: - climate pretty_name: detect-waste size_categories: - 1K<n<10K --- # Dataset Card for detect-waste ## Dataset Description - **Homepage: https://github.com/wimlds-trojmiasto/detect-waste** ### Dataset Summary AI4Good project for detecting waste in environment. www.detectwaste.ml. Our latest results were published in Waste Management journal in article titled Deep learning-based waste detection in natural and urban environments. You can find more technical details in our technical report Waste detection in Pomerania: non-profit project for detecting waste in environment. Did you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled. The idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good. ### Supported Tasks and Leaderboards Object Detection ### Languages English ### Data Fields https://github.com/wimlds-trojmiasto/detect-waste/tree/main/annotations ## Dataset Creation The images are post processed to remove exif and reorient as required. Some images are labelled without the exif rotation in mind thus they're not rotated at all but have their exif metadata removed ### Personal and Sensitive Information **BEWARE** This repository had been created by a third-party and is not affiliated in any way with the original detect-waste creators/ ## Considerations for Using the Data ### Licensing Information https://raw.githubusercontent.com/wimlds-trojmiasto/detect-waste/main/LICENSE
N1lanser/openassistant_best_replies_train-csv
--- license: mit ---
HanxuHU/mmmu_tr_filter
--- dataset_info: - config_name: Accounting features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 106588.13333333333 num_examples: 2 download_size: 188905 dataset_size: 106588.13333333333 - config_name: Agriculture features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 119217599.0 num_examples: 30 download_size: 119223838 dataset_size: 119217599.0 - config_name: Architecture_and_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 433065.8 num_examples: 18 download_size: 468287 dataset_size: 433065.8 - config_name: Art features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 29934575.0 num_examples: 30 download_size: 29942059 dataset_size: 29934575.0 - config_name: Art_Theory features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 33481314.0 num_examples: 30 download_size: 29784005 dataset_size: 33481314.0 - config_name: Basic_Medical_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 3988372.2 num_examples: 29 download_size: 4093748 dataset_size: 3988372.2 - config_name: Biology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 7642794.499999999 num_examples: 27 download_size: 8023622 dataset_size: 7642794.499999999 - config_name: Chemistry features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1366662.0 num_examples: 27 download_size: 1363678 dataset_size: 1366662.0 - config_name: Clinical_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 10882501.0 num_examples: 30 download_size: 10888211 dataset_size: 10882501.0 - config_name: Computer_Science features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1934158.1333333333 num_examples: 28 download_size: 2009878 dataset_size: 1934158.1333333333 - config_name: Design features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 17923052.0 num_examples: 30 download_size: 16227867 dataset_size: 17923052.0 - config_name: Diagnostics_and_Laboratory_Medicine features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 37106101.0 num_examples: 30 download_size: 37090121 dataset_size: 37106101.0 - config_name: Economics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 644572.7666666667 num_examples: 13 download_size: 929257 dataset_size: 644572.7666666667 - config_name: Electronics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 641460.0 num_examples: 30 download_size: 645006 dataset_size: 641460.0 - config_name: Energy_and_Power features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1642432.0 num_examples: 30 download_size: 1647101 dataset_size: 1642432.0 - config_name: Finance features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 35718.433333333334 num_examples: 1 download_size: 31806 dataset_size: 35718.433333333334 - config_name: Geography features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 6448993.233333333 num_examples: 29 download_size: 6612112 dataset_size: 6448993.233333333 - config_name: History features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 8232083.733333333 num_examples: 28 download_size: 8207244 dataset_size: 8232083.733333333 - config_name: Literature features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 14241094.0 num_examples: 30 download_size: 14247199 dataset_size: 14241094.0 - config_name: Manage features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1967091.6 num_examples: 18 download_size: 2084337 dataset_size: 1967091.6 - config_name: Marketing features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 343837.3333333333 num_examples: 7 download_size: 860258 dataset_size: 343837.3333333333 - config_name: Materials features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1997838.6666666667 num_examples: 26 download_size: 2199515 dataset_size: 1997838.6666666667 - config_name: Math features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1396426.2666666666 num_examples: 29 download_size: 1437571 dataset_size: 1396426.2666666666 - config_name: Mechanical_Engineering features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 875271.0 num_examples: 30 download_size: 877212 dataset_size: 875271.0 - config_name: Music features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 9359391.0 num_examples: 30 download_size: 9364095 dataset_size: 9359391.0 - config_name: Pharmacy features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1435675.3333333333 num_examples: 26 download_size: 1330784 dataset_size: 1435675.3333333333 - config_name: Physics features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 1114295.0 num_examples: 30 download_size: 1117802 dataset_size: 1114295.0 - config_name: Psychology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 3964965.3 num_examples: 27 download_size: 3979235 dataset_size: 3964965.3 - config_name: Public_Health features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 251566.83333333334 num_examples: 5 download_size: 672327 dataset_size: 251566.83333333334 - config_name: Sociology features: - name: id dtype: string - name: question dtype: string - name: options dtype: string - name: explanation dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: image_5 dtype: image - name: image_6 dtype: image - name: image_7 dtype: image - name: img_type dtype: string - name: answer dtype: string - name: topic_difficulty dtype: string - name: question_type dtype: string - name: subfield dtype: string splits: - name: validation num_bytes: 17840094.633333333 num_examples: 29 download_size: 17596464 dataset_size: 17840094.633333333 configs: - config_name: Accounting data_files: - split: validation path: Accounting/validation-* - config_name: Agriculture data_files: - split: validation path: Agriculture/validation-* - config_name: Architecture_and_Engineering data_files: - split: validation path: Architecture_and_Engineering/validation-* - config_name: Art data_files: - split: validation path: Art/validation-* - config_name: Art_Theory data_files: - split: validation path: Art_Theory/validation-* - config_name: Basic_Medical_Science data_files: - split: validation path: Basic_Medical_Science/validation-* - config_name: Biology data_files: - split: validation path: Biology/validation-* - config_name: Chemistry data_files: - split: validation path: Chemistry/validation-* - config_name: Clinical_Medicine data_files: - split: validation path: Clinical_Medicine/validation-* - config_name: Computer_Science data_files: - split: validation path: Computer_Science/validation-* - config_name: Design data_files: - split: validation path: Design/validation-* - config_name: Diagnostics_and_Laboratory_Medicine data_files: - split: validation path: Diagnostics_and_Laboratory_Medicine/validation-* - config_name: Economics data_files: - split: validation path: Economics/validation-* - config_name: Electronics data_files: - split: validation path: Electronics/validation-* - config_name: Energy_and_Power data_files: - split: validation path: Energy_and_Power/validation-* - config_name: Finance data_files: - split: validation path: Finance/validation-* - config_name: Geography data_files: - split: validation path: Geography/validation-* - config_name: History data_files: - split: validation path: History/validation-* - config_name: Literature data_files: - split: validation path: Literature/validation-* - config_name: Manage data_files: - split: validation path: Manage/validation-* - config_name: Marketing data_files: - split: validation path: Marketing/validation-* - config_name: Materials data_files: - split: validation path: Materials/validation-* - config_name: Math data_files: - split: validation path: Math/validation-* - config_name: Mechanical_Engineering data_files: - split: validation path: Mechanical_Engineering/validation-* - config_name: Music data_files: - split: validation path: Music/validation-* - config_name: Pharmacy data_files: - split: validation path: Pharmacy/validation-* - config_name: Physics data_files: - split: validation path: Physics/validation-* - config_name: Psychology data_files: - split: validation path: Psychology/validation-* - config_name: Public_Health data_files: - split: validation path: Public_Health/validation-* - config_name: Sociology data_files: - split: validation path: Sociology/validation-* ---
Chasen64/DatasetPruebaChas
--- license: mit ---
Multimodal-Fatima/Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084
--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 101626234.5 num_examples: 6084 - name: fewshot_1_bs_16 num_bytes: 103738576.5 num_examples: 6084 - name: fewshot_3_bs_16 num_bytes: 107968014.5 num_examples: 6084 download_size: 287673188 dataset_size: 313332825.5 --- # Dataset Card for "Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EulerianKnight/breast-histopathology-images-train-test-valid-split
--- license: apache-2.0 task_categories: - image-classification size_categories: - 100K<n<1M --- # Breast Histopathology Image dataset - This dataset is just a rearrangement of the Original dataset at Kaggle: https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images - Data Citation: https://www.ncbi.nlm.nih.gov/pubmed/27563488 , http://spie.org/Publications/Proceedings/Paper/10.1117/12.2043872 - The original dataset has structure: <pre> |-- patient_id |-- class(0 and 1) </pre> - The present dataset has following structure: <pre> |-- train |-- class(0 and 1) |-- valid |-- class(0 and 1) |-- test |-- class(0 and 1)
CyberHarem/silverash_arknights
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of silverash_arknights This is the dataset of silverash_arknights, containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 408 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 408 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 408 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 408 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
Neu256/LLama_ru_for_fine-turing
--- license: mit ---
Hadnet/olavo-articles-17k-dataset-text
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: output dtype: string - name: input dtype: string - name: instruction dtype: string - name: text dtype: string splits: - name: train num_bytes: 9762976 num_examples: 17361 download_size: 5498669 dataset_size: 9762976 --- # Dataset Card for "olavo-notes-dataset-text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nexdata/Japanese_Speaking_English_Speech_Data_by_Mobile_Phone
--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Japanese_Speaking_English_Speech_Data_by_Mobile_Phone ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/1048?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary 400 native Japanese speakers involved, balanced for gender. The recording corpus is rich in content, and it covers a wide domain such as generic command and control category, human-machine interaction category; smart home category; in-car category. The transcription corpus has been manually proofread to ensure high accuracy. For more details, please refer to the link: https://www.nexdata.ai/datasets/1048?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Japanese English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
liuyanchen1015/MULTI_VALUE_mrpc_plural_to_singular_human
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 182718 num_examples: 648 - name: train num_bytes: 399568 num_examples: 1406 - name: validation num_bytes: 38546 num_examples: 134 download_size: 411962 dataset_size: 620832 --- # Dataset Card for "MULTI_VALUE_mrpc_plural_to_singular_human" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pszemraj/fleece2instructions-codealpaca
--- license: cc-by-nc-4.0 task_categories: - text2text-generation - text-generation language: - en size_categories: - 10K<n<100K tags: - instructions - domain adaptation --- # codealpaca for text2text generation This dataset was downloaded from the [sahil280114/codealpaca](https://github.com/sahil280114/codealpaca) github repo and parsed into text2text format for "generating" instructions. It was downloaded under the **wonderful** Creative Commons Attribution-NonCommercial 4.0 International Public License (see snapshots of the [repo](https://web.archive.org/web/20230325040745/https://github.com/sahil280114/codealpaca) and [data license](https://web.archive.org/web/20230325041314/https://github.com/sahil280114/codealpaca/blob/master/DATA_LICENSE)), so that license applies to this dataset. Note that the `inputs` and `instruction` columns in the original dataset have been aggregated together for text2text generation. Each has a token with either `<instruction>` or `<inputs>` in front of the relevant text, both for model understanding and regex separation later. ## structure dataset structure: ```python DatasetDict({ train: Dataset({ features: ['instructions_inputs', 'output'], num_rows: 18014 }) test: Dataset({ features: ['instructions_inputs', 'output'], num_rows: 1000 }) validation: Dataset({ features: ['instructions_inputs', 'output'], num_rows: 1002 }) }) ``` ## example The example shows what rows **without** inputs will look like (approximately 60% of the dataset according to repo). Note the special tokens to identify what is what when the model generates text: `<instruction>` and `<input>`: ![example](https://i.imgur.com/bdZM4NW.png) ## token lengths bart ![bart](https://i.imgur.com/81qBl3e.png) t5 ![t5](https://i.imgur.com/63vOqP4.png)
skarwa/scientific_papers_segmented
--- license: mit ---
TinyPixel/airo-1
--- dataset_info: features: - name: instruction dtype: string - name: response dtype: string - name: category dtype: string - name: question_id dtype: float64 splits: - name: train num_bytes: 57737476 num_examples: 34204 download_size: 30991700 dataset_size: 57737476 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "airo-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Saba06huggingface/resume_dataset
--- license: apache-2.0 task_categories: - text-classification language: - en --- # Dataset Card for Saba06huggingface/resume_dataset A collection of Resume Examples taken from livecareer.com for categorizing a given resume into any of the labels defined in the dataset. This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description About Dataset Context A collection of Resume Examples taken from livecareer.com for categorizing a given resume into any of the labels defined in the dataset. Content Contains 2400+ Resumes in string as well as PDF format. PDF stored in the data folder differentiated into their respective labels as folders with each resume residing inside the folder in pdf form with filename as the id defined in the csv. Inside the CSV: ID: Unique identifier and file name for the respective pdf. Resume_str : Contains the resume text only in string format. Resume_html : Contains the resume data in html format as present while web scrapping. Category : Category of the job the resume was used to apply. Present categories are HR, Designer, Information-Technology, Teacher, Advocate, Business-Development, Healthcare, Fitness, Agriculture, BPO, Sales, Consultant, Digital-Media, Automobile, Chef, Finance, Apparel, Engineering, Accountant, Construction, Public-Relations, Banking, Arts, Aviation ## Dataset Card Contact Saba06huggingface/resume_dataset
ryanyang0/latexify
--- license: mit ---
ludiusvox/OZ
--- license: bsd dataset_info: features: - name: title dtype: string - name: text dtype: string - name: embeddings sequence: float32 splits: - name: train num_bytes: 8601182 num_examples: 31 download_size: 5572388 dataset_size: 8601182 configs: - config_name: default data_files: - split: train path: data/train-* ---
reyrg/thermal-camera_v3
--- license: unknown dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 766087220.0 num_examples: 546 download_size: 49415770 dataset_size: 766087220.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
smangrul/hf-stack-v1
--- dataset_info: features: - name: repo_id dtype: string - name: file_path dtype: string - name: content dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 91907731 num_examples: 5905 download_size: 30589828 dataset_size: 91907731 --- # Dataset Card for "hf-stack-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MohammedNasri/cv_11_arabic_test_denoisy_II
--- dataset_info: features: - name: audio sequence: float64 - name: sentence dtype: string splits: - name: train num_bytes: 5817636498 num_examples: 10440 download_size: 2897757284 dataset_size: 5817636498 --- # Dataset Card for "cv_11_arabic_test_denoisy_II" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
irds/nfcorpus_train
--- pretty_name: '`nfcorpus/train`' viewer: false source_datasets: ['irds/nfcorpus'] task_categories: - text-retrieval --- # Dataset Card for `nfcorpus/train` The `nfcorpus/train` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/nfcorpus#nfcorpus/train). # Data This dataset provides: - `queries` (i.e., topics); count=2,594 - `qrels`: (relevance assessments); count=139,350 - For `docs`, use [`irds/nfcorpus`](https://huggingface.co/datasets/irds/nfcorpus) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/nfcorpus_train', 'queries') for record in queries: record # {'query_id': ..., 'title': ..., 'all': ...} qrels = load_dataset('irds/nfcorpus_train', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Boteva2016Nfcorpus, title="A Full-Text Learning to Rank Dataset for Medical Information Retrieval", author = "Vera Boteva and Demian Gholipour and Artem Sokolov and Stefan Riezler", booktitle = "Proceedings of the European Conference on Information Retrieval ({ECIR})", location = "Padova, Italy", publisher = "Springer", year = 2016 } ```
rokset3/136Mkeystrokes
--- dataset_info: features: - name: PARTICIPANT_ID dtype: int64 - name: TEST_SECTION_ID dtype: int64 - name: SENTENCE dtype: string - name: USER_INPUT dtype: string - name: KEYSTROKE_ID dtype: int64 - name: PRESS_TIME dtype: int64 - name: RELEASE_TIME dtype: int64 - name: LETTER dtype: string - name: KEYCODE dtype: float64 splits: - name: train num_bytes: 17618096680 num_examples: 113719769 download_size: 2735520752 dataset_size: 17618096680 --- # Dataset Card for "136Mkeystrokes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_79_1713094177
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 3160558 num_examples: 7968 download_size: 1589578 dataset_size: 3160558 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_KaeriJenti__Kaori-34b-v2
--- pretty_name: Evaluation run of KaeriJenti/Kaori-34b-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [KaeriJenti/Kaori-34b-v2](https://huggingface.co/KaeriJenti/Kaori-34b-v2) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KaeriJenti__Kaori-34b-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-23T19:17:38.902154](https://huggingface.co/datasets/open-llm-leaderboard/details_KaeriJenti__Kaori-34b-v2/blob/main/results_2023-12-23T19-17-38.902154.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2562435688049368,\n\ \ \"acc_stderr\": 0.03087677995486888,\n \"acc_norm\": 0.25622099120034325,\n\ \ \"acc_norm_stderr\": 0.03166775316506421,\n \"mc1\": 0.2864137086903305,\n\ \ \"mc1_stderr\": 0.015826142439502346,\n \"mc2\": 0.49462441219025927,\n\ \ \"mc2_stderr\": 0.016011015086112988\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.189419795221843,\n \"acc_stderr\": 0.011450705115910769,\n\ \ \"acc_norm\": 0.23890784982935154,\n \"acc_norm_stderr\": 0.012461071376316614\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.27394941246763593,\n\ \ \"acc_stderr\": 0.004450718673552667,\n \"acc_norm\": 0.2896833300139414,\n\ \ \"acc_norm_stderr\": 0.004526883021027624\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2740740740740741,\n\ \ \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.2740740740740741,\n\ \ \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.2236842105263158,\n \"acc_stderr\": 0.033911609343436025,\n\ \ \"acc_norm\": 0.2236842105263158,\n \"acc_norm_stderr\": 0.033911609343436025\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.23,\n\ \ \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.025447863825108594,\n\ \ \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.025447863825108594\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.03476590104304136,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.03476590104304136\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.21965317919075145,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n\ \ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.030135906478517563,\n\ \ \"acc_norm\": 0.30638297872340425,\n \"acc_norm_stderr\": 0.030135906478517563\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.20175438596491227,\n\ \ \"acc_stderr\": 0.037752050135836386,\n \"acc_norm\": 0.20175438596491227,\n\ \ \"acc_norm_stderr\": 0.037752050135836386\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.25517241379310346,\n \"acc_stderr\": 0.03632984052707842,\n\ \ \"acc_norm\": 0.25517241379310346,\n \"acc_norm_stderr\": 0.03632984052707842\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2671957671957672,\n \"acc_stderr\": 0.02278967314577657,\n \"\ acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.02278967314577657\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.0404061017820884,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.0404061017820884\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.3161290322580645,\n \"acc_stderr\": 0.02645087448904277,\n \"\ acc_norm\": 0.3161290322580645,\n \"acc_norm_stderr\": 0.02645087448904277\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.19704433497536947,\n \"acc_stderr\": 0.027986724666736205,\n \"\ acc_norm\": 0.19704433497536947,\n \"acc_norm_stderr\": 0.027986724666736205\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\"\ : 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.23030303030303031,\n \"acc_stderr\": 0.032876667586034886,\n\ \ \"acc_norm\": 0.23030303030303031,\n \"acc_norm_stderr\": 0.032876667586034886\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.35353535353535354,\n \"acc_stderr\": 0.03406086723547153,\n \"\ acc_norm\": 0.35353535353535354,\n \"acc_norm_stderr\": 0.03406086723547153\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.37823834196891193,\n \"acc_stderr\": 0.03499807276193339,\n\ \ \"acc_norm\": 0.37823834196891193,\n \"acc_norm_stderr\": 0.03499807276193339\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2128205128205128,\n \"acc_stderr\": 0.020752423722128016,\n\ \ \"acc_norm\": 0.2128205128205128,\n \"acc_norm_stderr\": 0.020752423722128016\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3037037037037037,\n \"acc_stderr\": 0.02803792996911499,\n \ \ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.02803792996911499\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.026653531596715477,\n\ \ \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715477\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389023,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389023\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.3155963302752294,\n \"acc_stderr\": 0.019926117513869666,\n \"\ acc_norm\": 0.3155963302752294,\n \"acc_norm_stderr\": 0.019926117513869666\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.24074074074074073,\n \"acc_stderr\": 0.0291575221846056,\n \"\ acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.0291575221846056\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.30392156862745096,\n \"acc_stderr\": 0.03228210387037892,\n \"\ acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.03228210387037892\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.22362869198312235,\n \"acc_stderr\": 0.027123298205229972,\n \ \ \"acc_norm\": 0.22362869198312235,\n \"acc_norm_stderr\": 0.027123298205229972\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.24663677130044842,\n\ \ \"acc_stderr\": 0.028930413120910894,\n \"acc_norm\": 0.24663677130044842,\n\ \ \"acc_norm_stderr\": 0.028930413120910894\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2366412213740458,\n \"acc_stderr\": 0.03727673575596918,\n\ \ \"acc_norm\": 0.2366412213740458,\n \"acc_norm_stderr\": 0.03727673575596918\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.3140495867768595,\n \"acc_stderr\": 0.042369647530410184,\n \"\ acc_norm\": 0.3140495867768595,\n \"acc_norm_stderr\": 0.042369647530410184\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2222222222222222,\n\ \ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.2222222222222222,\n\ \ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.2883435582822086,\n \"acc_stderr\": 0.03559039531617342,\n\ \ \"acc_norm\": 0.2883435582822086,\n \"acc_norm_stderr\": 0.03559039531617342\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.23214285714285715,\n\ \ \"acc_stderr\": 0.04007341809755805,\n \"acc_norm\": 0.23214285714285715,\n\ \ \"acc_norm_stderr\": 0.04007341809755805\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.27184466019417475,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.27184466019417475,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.19230769230769232,\n\ \ \"acc_stderr\": 0.025819233256483706,\n \"acc_norm\": 0.19230769230769232,\n\ \ \"acc_norm_stderr\": 0.025819233256483706\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.27330779054916987,\n\ \ \"acc_stderr\": 0.015936681062628556,\n \"acc_norm\": 0.27330779054916987,\n\ \ \"acc_norm_stderr\": 0.015936681062628556\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.2543352601156069,\n \"acc_stderr\": 0.02344582627654554,\n\ \ \"acc_norm\": 0.2543352601156069,\n \"acc_norm_stderr\": 0.02344582627654554\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2636871508379888,\n\ \ \"acc_stderr\": 0.014736926383761973,\n \"acc_norm\": 0.2636871508379888,\n\ \ \"acc_norm_stderr\": 0.014736926383761973\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.2875816993464052,\n \"acc_stderr\": 0.02591780611714716,\n\ \ \"acc_norm\": 0.2875816993464052,\n \"acc_norm_stderr\": 0.02591780611714716\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2572347266881029,\n\ \ \"acc_stderr\": 0.024826171289250888,\n \"acc_norm\": 0.2572347266881029,\n\ \ \"acc_norm_stderr\": 0.024826171289250888\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.25617283950617287,\n \"acc_stderr\": 0.024288533637726095,\n\ \ \"acc_norm\": 0.25617283950617287,\n \"acc_norm_stderr\": 0.024288533637726095\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.24822695035460993,\n \"acc_stderr\": 0.025770015644290396,\n \ \ \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.025770015644290396\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23272490221642764,\n\ \ \"acc_stderr\": 0.010792595553888496,\n \"acc_norm\": 0.23272490221642764,\n\ \ \"acc_norm_stderr\": 0.010792595553888496\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.22426470588235295,\n \"acc_stderr\": 0.02533684856333236,\n\ \ \"acc_norm\": 0.22426470588235295,\n \"acc_norm_stderr\": 0.02533684856333236\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2679738562091503,\n \"acc_stderr\": 0.017917974069594722,\n \ \ \"acc_norm\": 0.2679738562091503,\n \"acc_norm_stderr\": 0.017917974069594722\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2545454545454545,\n\ \ \"acc_stderr\": 0.04172343038705383,\n \"acc_norm\": 0.2545454545454545,\n\ \ \"acc_norm_stderr\": 0.04172343038705383\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.24897959183673468,\n \"acc_stderr\": 0.02768297952296023,\n\ \ \"acc_norm\": 0.24897959183673468,\n \"acc_norm_stderr\": 0.02768297952296023\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.1890547263681592,\n\ \ \"acc_stderr\": 0.027686913588013024,\n \"acc_norm\": 0.1890547263681592,\n\ \ \"acc_norm_stderr\": 0.027686913588013024\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.26506024096385544,\n\ \ \"acc_stderr\": 0.03436024037944966,\n \"acc_norm\": 0.26506024096385544,\n\ \ \"acc_norm_stderr\": 0.03436024037944966\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.035650796707083106,\n\ \ \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.035650796707083106\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2864137086903305,\n\ \ \"mc1_stderr\": 0.015826142439502346,\n \"mc2\": 0.49462441219025927,\n\ \ \"mc2_stderr\": 0.016011015086112988\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5722178374112076,\n \"acc_stderr\": 0.013905134013839957\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \ \ \"acc_stderr\": 0.0022675371022544905\n }\n}\n```" repo_url: https://huggingface.co/KaeriJenti/Kaori-34b-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|arc:challenge|25_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-23T19-17-38.902154.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|gsm8k|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hellaswag|10_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-23T19-17-38.902154.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-management|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-23T19-17-38.902154.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|truthfulqa:mc|0_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-23T19-17-38.902154.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_23T19_17_38.902154 path: - '**/details_harness|winogrande|5_2023-12-23T19-17-38.902154.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-23T19-17-38.902154.parquet' - config_name: results data_files: - split: 2023_12_23T19_17_38.902154 path: - results_2023-12-23T19-17-38.902154.parquet - split: latest path: - results_2023-12-23T19-17-38.902154.parquet --- # Dataset Card for Evaluation run of KaeriJenti/Kaori-34b-v2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [KaeriJenti/Kaori-34b-v2](https://huggingface.co/KaeriJenti/Kaori-34b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KaeriJenti__Kaori-34b-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-23T19:17:38.902154](https://huggingface.co/datasets/open-llm-leaderboard/details_KaeriJenti__Kaori-34b-v2/blob/main/results_2023-12-23T19-17-38.902154.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.2562435688049368, "acc_stderr": 0.03087677995486888, "acc_norm": 0.25622099120034325, "acc_norm_stderr": 0.03166775316506421, "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502346, "mc2": 0.49462441219025927, "mc2_stderr": 0.016011015086112988 }, "harness|arc:challenge|25": { "acc": 0.189419795221843, "acc_stderr": 0.011450705115910769, "acc_norm": 0.23890784982935154, "acc_norm_stderr": 0.012461071376316614 }, "harness|hellaswag|10": { "acc": 0.27394941246763593, "acc_stderr": 0.004450718673552667, "acc_norm": 0.2896833300139414, "acc_norm_stderr": 0.004526883021027624 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2740740740740741, "acc_stderr": 0.03853254836552003, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.03853254836552003 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.2236842105263158, "acc_stderr": 0.033911609343436025, "acc_norm": 0.2236842105263158, "acc_norm_stderr": 0.033911609343436025 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.23, "acc_stderr": 0.04229525846816506, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2188679245283019, "acc_stderr": 0.025447863825108594, "acc_norm": 0.2188679245283019, "acc_norm_stderr": 0.025447863825108594 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03476590104304136, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03476590104304136 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.22, "acc_stderr": 0.0416333199893227, "acc_norm": 0.22, "acc_norm_stderr": 0.0416333199893227 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.24, "acc_stderr": 0.04292346959909282, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909282 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.21965317919075145, "acc_stderr": 0.031568093627031744, "acc_norm": 0.21965317919075145, "acc_norm_stderr": 0.031568093627031744 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.20588235294117646, "acc_stderr": 0.04023382273617747, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.04023382273617747 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.30638297872340425, "acc_stderr": 0.030135906478517563, "acc_norm": 0.30638297872340425, "acc_norm_stderr": 0.030135906478517563 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.20175438596491227, "acc_stderr": 0.037752050135836386, "acc_norm": 0.20175438596491227, "acc_norm_stderr": 0.037752050135836386 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.25517241379310346, "acc_stderr": 0.03632984052707842, "acc_norm": 0.25517241379310346, "acc_norm_stderr": 0.03632984052707842 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2671957671957672, "acc_stderr": 0.02278967314577657, "acc_norm": 0.2671957671957672, "acc_norm_stderr": 0.02278967314577657 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0404061017820884, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0404061017820884 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3161290322580645, "acc_stderr": 0.02645087448904277, "acc_norm": 0.3161290322580645, "acc_norm_stderr": 0.02645087448904277 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.19704433497536947, "acc_stderr": 0.027986724666736205, "acc_norm": 0.19704433497536947, "acc_norm_stderr": 0.027986724666736205 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.24, "acc_stderr": 0.042923469599092816, "acc_norm": 0.24, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.23030303030303031, "acc_stderr": 0.032876667586034886, "acc_norm": 0.23030303030303031, "acc_norm_stderr": 0.032876667586034886 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.35353535353535354, "acc_stderr": 0.03406086723547153, "acc_norm": 0.35353535353535354, "acc_norm_stderr": 0.03406086723547153 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.37823834196891193, "acc_stderr": 0.03499807276193339, "acc_norm": 0.37823834196891193, "acc_norm_stderr": 0.03499807276193339 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2128205128205128, "acc_stderr": 0.020752423722128016, "acc_norm": 0.2128205128205128, "acc_norm_stderr": 0.020752423722128016 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3037037037037037, "acc_stderr": 0.02803792996911499, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.02803792996911499 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.21428571428571427, "acc_stderr": 0.026653531596715477, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.026653531596715477 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389023, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389023 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.3155963302752294, "acc_stderr": 0.019926117513869666, "acc_norm": 0.3155963302752294, "acc_norm_stderr": 0.019926117513869666 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.24074074074074073, "acc_stderr": 0.0291575221846056, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.0291575221846056 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.30392156862745096, "acc_stderr": 0.03228210387037892, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.03228210387037892 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.22362869198312235, "acc_stderr": 0.027123298205229972, "acc_norm": 0.22362869198312235, "acc_norm_stderr": 0.027123298205229972 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.24663677130044842, "acc_stderr": 0.028930413120910894, "acc_norm": 0.24663677130044842, "acc_norm_stderr": 0.028930413120910894 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2366412213740458, "acc_stderr": 0.03727673575596918, "acc_norm": 0.2366412213740458, "acc_norm_stderr": 0.03727673575596918 }, "harness|hendrycksTest-international_law|5": { "acc": 0.3140495867768595, "acc_stderr": 0.042369647530410184, "acc_norm": 0.3140495867768595, "acc_norm_stderr": 0.042369647530410184 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.2222222222222222, "acc_stderr": 0.040191074725573483, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.040191074725573483 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.2883435582822086, "acc_stderr": 0.03559039531617342, "acc_norm": 0.2883435582822086, "acc_norm_stderr": 0.03559039531617342 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.23214285714285715, "acc_stderr": 0.04007341809755805, "acc_norm": 0.23214285714285715, "acc_norm_stderr": 0.04007341809755805 }, "harness|hendrycksTest-management|5": { "acc": 0.27184466019417475, "acc_stderr": 0.044052680241409216, "acc_norm": 0.27184466019417475, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.19230769230769232, "acc_stderr": 0.025819233256483706, "acc_norm": 0.19230769230769232, "acc_norm_stderr": 0.025819233256483706 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.27330779054916987, "acc_stderr": 0.015936681062628556, "acc_norm": 0.27330779054916987, "acc_norm_stderr": 0.015936681062628556 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2543352601156069, "acc_stderr": 0.02344582627654554, "acc_norm": 0.2543352601156069, "acc_norm_stderr": 0.02344582627654554 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2636871508379888, "acc_stderr": 0.014736926383761973, "acc_norm": 0.2636871508379888, "acc_norm_stderr": 0.014736926383761973 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2875816993464052, "acc_stderr": 0.02591780611714716, "acc_norm": 0.2875816993464052, "acc_norm_stderr": 0.02591780611714716 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2572347266881029, "acc_stderr": 0.024826171289250888, "acc_norm": 0.2572347266881029, "acc_norm_stderr": 0.024826171289250888 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.25617283950617287, "acc_stderr": 0.024288533637726095, "acc_norm": 0.25617283950617287, "acc_norm_stderr": 0.024288533637726095 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.24822695035460993, "acc_stderr": 0.025770015644290396, "acc_norm": 0.24822695035460993, "acc_norm_stderr": 0.025770015644290396 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.23272490221642764, "acc_stderr": 0.010792595553888496, "acc_norm": 0.23272490221642764, "acc_norm_stderr": 0.010792595553888496 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.22426470588235295, "acc_stderr": 0.02533684856333236, "acc_norm": 0.22426470588235295, "acc_norm_stderr": 0.02533684856333236 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2679738562091503, "acc_stderr": 0.017917974069594722, "acc_norm": 0.2679738562091503, "acc_norm_stderr": 0.017917974069594722 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2545454545454545, "acc_stderr": 0.04172343038705383, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.04172343038705383 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.24897959183673468, "acc_stderr": 0.02768297952296023, "acc_norm": 0.24897959183673468, "acc_norm_stderr": 0.02768297952296023 }, "harness|hendrycksTest-sociology|5": { "acc": 0.1890547263681592, "acc_stderr": 0.027686913588013024, "acc_norm": 0.1890547263681592, "acc_norm_stderr": 0.027686913588013024 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-virology|5": { "acc": 0.26506024096385544, "acc_stderr": 0.03436024037944966, "acc_norm": 0.26506024096385544, "acc_norm_stderr": 0.03436024037944966 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.3157894736842105, "acc_stderr": 0.035650796707083106, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.035650796707083106 }, "harness|truthfulqa:mc|0": { "mc1": 0.2864137086903305, "mc1_stderr": 0.015826142439502346, "mc2": 0.49462441219025927, "mc2_stderr": 0.016011015086112988 }, "harness|winogrande|5": { "acc": 0.5722178374112076, "acc_stderr": 0.013905134013839957 }, "harness|gsm8k|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022544905 } } ``` ## 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]
dlibf/glaive-code-assistant
--- configs: - config_name: default data_files: - split: train_sft path: data/train_sft-* - split: test_sft path: data/test_sft-* dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train_sft num_bytes: 210616334.29604948 num_examples: 136009 - name: test_sft num_bytes: 154854.70395051024 num_examples: 100 download_size: 102642844 dataset_size: 210771189.0 --- # Dataset Card for "glaive-code-assistant" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lmqg/qag_dequad
--- license: cc-by-sa-4.0 pretty_name: SQuAD for question generation language: de multilinguality: monolingual size_categories: 1k<n<10K source_datasets: lmqg/qg_dequad task_categories: - text-generation task_ids: - language-modeling tags: - question-generation --- # Dataset Card for "lmqg/qag_dequad" ## Dataset Description - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) - **Point of Contact:** [Asahi Ushio](http://asahiushio.com/) ### Dataset Summary This is the question & answer generation dataset based on the DEQuAD. ### Supported Tasks and Leaderboards * `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation. Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail). ### Languages German (de) ## Dataset Structure An example of 'train' looks as follows. ``` { "paragraph": "51._Bundesstaat === District of Columbia === Der District of Columbia gilt neben Puerto Rico als einer der aussichtsreichen Kandidaten für die Anerkennung als Bundesstaat in naher Zukunft. Die Einwohner des Bundesdistrikts gelten als größte Befürworter dieser Entscheidung, die jedoch einer Verfassungsänderung bedürfte. Die Anhänger nutzen das Motto des Unabhängigkeitskrieges in abgewandelter Form – „Taxation without representation“ –, um auf die mangelnde Repräsentation im Kongress hinzuweisen. Das Motto wird heute auf die Nummernschilder neu zugelassener Autos gedruckt (wobei der Fahrer alternativ die Internet-Adresse des D.C. wählen kann). Bill Clintons Präsidenten-Limousine hatte ein solches Nummernschild kurz vor Ende seiner Amtszeit. George W. Bush ließ diese Nummernschilder nach seinem Amtsantritt wieder entfernen. Die kleine ''D.C. Statehood Party'' vertrat diese Ansicht und vereinte sich mit den Grünen zur ''D.C. Statehood Green Party''. 1978 kamen sie ihrem Ziel am nächsten, als der Kongress das ''District of Columbia Voting Rights Amendment'' verabschiedete. Zwei Jahre später beriefen lokale Bürger mit einer Initiative eine konstitutionelle Versammlung für einen neuen Bundesstaat. 1982 ratifizierten die Wähler die Verfassung des Bundesstaates, der ''New Columbia'' heißen sollte. 1985 wurde der Plan jedoch gestoppt, als das Amendment scheiterte, weil es nicht von genug Staaten innerhalb von sieben Jahren ratifiziert wurde. Eine andere Möglichkeit wäre die Rückgliederung des Gebietes in den Bundesstaat Maryland. Damit würden die Einwohner des D.C. in den Genuss der Vorteile kommen, in einem Bundesstaat zu leben, ohne dass ein 51. Bundesstaat geschaffen werden müsste. Am 26. Juni 2020 stimmte das US-Repräsentantenhaus mit 232 zu 180 Stimmen dafür, den District of Columbia als 51. Bundesstaat anzuerkennen. Ein positives Votum des durch die Republikaner dominierten US-Senats gilt als unwahrscheinlich. Außerdem kündigte Präsident Trump sein Veto gegen ein solches, potenzielles Vorhaben an. Dennoch war es das erste positive Votum einer der beiden Kammern des US-Kongresses für eine Anerkennung als Bundesstaat.", "questions": [ "Was ist das Motto der Befürworter der Anerkennung von District of Columbia als neuer US-Bundesstaat?", "Warum hat die Anerkennung von District of Columbia zu einem neuen US-Bundesstaat 1985 nicht geklappt?", "Was war der potenzielle Name für den neuen US-Bundesstaat anstelle von District of Columbia?", "Aus welchen ehemaligen Parteien bestand die D.C. Statehood Green Party?" ], "answers": [ "das Motto des Unabhängigkeitskrieges in abgewandelter Form – „Taxation without representation“ ", "weil es nicht von genug Staaten innerhalb von sieben Jahren ratifiziert wurde", " ''New Columbia'' ", "Die kleine ''D.C. Statehood Party'' vertrat diese Ansicht und vereinte sich mit den Grünen" ], "questions_answers": "question: Was ist das Motto der Befürworter der Anerkennung von District of Columbia als neuer US-Bundesstaat?, answer: das Motto des Unabhängigkeitskrieges in abgewandelter Form – „Taxation without representation“ | question: Warum hat die Anerkennung von District of Columbia zu einem neuen US-Bundesstaat 1985 nicht geklappt?, answer: weil es nicht von genug Staaten innerhalb von sieben Jahren ratifiziert wurde | question: Was war der potenzielle Name für den neuen US-Bundesstaat anstelle von District of Columbia?, answer: ''New Columbia'' | question: Aus welchen ehemaligen Parteien bestand die D.C. Statehood Green Party?, answer: Die kleine ''D.C. Statehood Party'' vertrat diese Ansicht und vereinte sich mit den Grünen" } ``` The data fields are the same among all splits. - `questions`: a `list` of `string` features. - `answers`: a `list` of `string` features. - `paragraph`: a `string` feature. - `questions_answers`: a `string` feature. ## Data Splits |train|validation|test | |----:|---------:|----:| |2489 | 1476 | 474 | ## Citation Information ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
Nolan1206/WhisperSmallTest20000
--- dataset_info: features: - name: audio sequence: float32 - name: sentence dtype: string splits: - name: train num_bytes: 3882577961 num_examples: 18450 - name: test num_bytes: 40879128 num_examples: 377 download_size: 3938134546 dataset_size: 3923457089 --- # Dataset Card for "WhisperSmallTest20000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hamtech/tst
--- license: pddl language: - en pretty_name: tst size_categories: - 100B<n<1T ---
israelfx/brunoleonardo
--- license: openrail ---
Multimodal-Fatima/VizWiz_train
--- dataset_info: features: - name: id dtype: int32 - name: image dtype: image - name: filename dtype: string - name: question dtype: string - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_type dtype: string - name: answerable dtype: int32 - name: id_image dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: clip_tags_LAION_ViT_H_14_2B sequence: string - name: blip_caption_beam_5 dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string splits: - name: train num_bytes: 9906518637.0 num_examples: 20523 download_size: 9880125036 dataset_size: 9906518637.0 --- # Dataset Card for "VizWiz_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arieg/bw_spec_cls_4_14_s_200
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '1197' '1': '1270' '2': '1276' '3': '1277' splits: - name: train num_bytes: 43731623.0 num_examples: 800 - name: test num_bytes: 1102972.0 num_examples: 20 download_size: 37991761 dataset_size: 44834595.0 --- # Dataset Card for "bw_spec_cls_4_14_s_200" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
maghwa/OpenHermes-2-AR-10K-41-850k-860k
--- dataset_info: features: - name: topic dtype: 'null' - name: conversations dtype: string - name: source dtype: string - name: category dtype: 'null' - name: title dtype: 'null' - name: idx dtype: 'null' - name: language dtype: 'null' - name: custom_instruction dtype: 'null' - name: avatarUrl dtype: 'null' - name: model_name dtype: 'null' - name: model dtype: 'null' - name: hash dtype: 'null' - name: views dtype: float64 - name: id dtype: 'null' - name: system_prompt dtype: 'null' - name: skip_prompt_formatting dtype: 'null' splits: - name: train num_bytes: 26796849 num_examples: 10001 download_size: 11296502 dataset_size: 26796849 configs: - config_name: default data_files: - split: train path: data/train-* ---
one-sec-cv12/chunk_228
--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 22202359632.125 num_examples: 231159 download_size: 18820040745 dataset_size: 22202359632.125 --- # Dataset Card for "chunk_228" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
abhinand/tamil-alpaca
--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string - name: system_prompt dtype: string splits: - name: train num_bytes: 287556653 num_examples: 51876 download_size: 0 dataset_size: 287556653 configs: - config_name: default data_files: - split: train path: data/train-* license: gpl-3.0 task_categories: - text-generation language: - ta pretty_name: tamil-alpaca size_categories: - 10K<n<100K --- # Dataset Card for "tamil-alpaca" This repository includes a Tamil-translated version of the [Alpaca dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned). This dataset is part of the release of Tamil LLaMA family of models – an important step in advancing LLMs for the Tamil language. To dive deep into the development and capabilities of this model, please read the [research paper](https://arxiv.org/abs/2311.05845) and the [introductory blog post (WIP) ]() that outlines our journey and the model's potential impact. **GitHub Repository:** [https://github.com/abhinand5/tamil-llama](https://github.com/abhinand5/tamil-llama) ## Models trained using this dataset | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Tamil LLaMA 7B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/abhinand/tamil-llama-7b-instruct-v0.1) | | Tamil LLaMA 13B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | [HF Hub](abhinand/tamil-llama-13b-instruct-v0.1) | ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - [Abhinand Balachandran](https://www.linkedin.com/in/abhinand-05/) ## Citation If you use this model or any of the the Tamil-Llama datasets in your research, please cite: ```bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
pinzhenchen/alpaca-cleaned-zh
--- license: cc-by-nc-4.0 task_categories: - text-generation - question-answering language: - zh tags: - instruction tuning size_categories: - 10K<n<100K --- ### Data Description This HF data repository contains the Chinese Alpaca dataset used in our study of monolingual versus multilingual instruction tuning. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Creation * Machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) into Chinese. #### Usage * This data is intended to be used for Chinese instruction tuning. * The dataset has roughly 52K instances in the JSON format. * Each instance has an instruction, an output, and an optional input. An example is shown below: ``` { "instruction": "三种主要颜色是什么?", "input": "", "output": "三种主要颜色是红色, 蓝色和黄色。 这些颜色被称为主要颜色, 因为它们不能通过混合其他颜色来创建, 所有其他颜色都可以通过以各种比例组合来创建。 在添加色系中, 用于光线的主要颜色是红色, 绿色和蓝色 (R GB) 。" } ``` #### Known issues * The machine translation process might have corrupted data containing code, cross-lingual tasks, grammatical error correction tasks, etc. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
KSmart/chinese_traditional_chengyu
--- license: apache-2.0 ---
marup/PhoebeTonkinRVC
--- license: openrail ---
davidberenstein1957/ultra-feedback-dutch-cleaned-hq-with-responses
--- dataset_info: features: - name: input dtype: string - name: generations sequence: string splits: - name: train num_bytes: 55243593 num_examples: 21577 - name: test num_bytes: 2917623 num_examples: 1136 download_size: 34331801 dataset_size: 58161216 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
IdoAi/FypDatasetWithSplitsRgb
--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1997747885.8 num_examples: 10700 - name: validation num_bytes: 204033799.13 num_examples: 1094 - name: test num_bytes: 68700437.0 num_examples: 365 download_size: 2263896820 dataset_size: 2270482121.93 --- # Dataset Card for "FypDatasetWithSplitsRgb" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pesc101/spyder-ide-respository-raw-chunks
--- dataset_info: features: - name: code dtype: string - name: meta_data.file_name dtype: string - name: meta_data.module dtype: string - name: meta_data.contains_class dtype: bool - name: meta_data.contains_function dtype: bool - name: meta_data.file_imports sequence: string - name: meta_data.start_line dtype: int64 - name: meta_data.end_line dtype: int64 splits: - name: train num_bytes: 17221590 num_examples: 7943 download_size: 3531423 dataset_size: 17221590 configs: - config_name: default data_files: - split: train path: data/train-* ---
mlo0ollm/cj_ko_words
--- license: openrail ---
SyedAunZaidi/cv-corpus-16.0-ur
--- dataset_info: features: - name: audio dtype: audio - name: client_id dtype: string - name: path dtype: string - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accents dtype: string - name: variant dtype: float64 - name: locale dtype: string - name: segment dtype: float64 - name: config dtype: string splits: - name: train num_bytes: 134956314.16 num_examples: 5368 - name: test num_bytes: 101379458.192 num_examples: 4014 - name: validation num_bytes: 101379458.192 num_examples: 4014 download_size: 330546792 dataset_size: 337715230.544 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
boapps/alpaca-cleaned-gemini-hun-ratings
--- license: apache-2.0 language: - hu --- Ez az adathalmaz úgy keletkezett, hogy a [Bazsalanszky/alpaca-cleaned-gemini-hun](https://huggingface.co/datasets/Bazsalanszky/alpaca-cleaned-gemini-hun)-n lefuttattam egy llm által támogatott értékelést. Az értékelő modell a gemini-pro (az ingyenes) volt. A használt kód az alpagasus módosítása: https://github.com/boapps/alpagasus-hu
vitorsonic/emi
--- license: openrail ---
dim/kinopoisk_raw
--- dataset_info: features: - name: content dtype: string - name: title dtype: string - name: grade3 dtype: string - name: movie_name dtype: string - name: part dtype: string - name: review_id dtype: string - name: author dtype: string - name: date dtype: string - name: grade10 dtype: string - name: Idx dtype: int32 splits: - name: train num_bytes: 138684842 num_examples: 36591 download_size: 70387577 dataset_size: 138684842 --- # Dataset Card for "kinopoisk_raw" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
LFBMS/class_dataset_real2_donut
--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': bilanz_h '1': bilanz_v '2': guv '3': kontennachweis_bilanz '4': kontennachweis_guv '5': other - name: ground_truth dtype: string splits: - name: train num_bytes: 340313532.0 num_examples: 1117 - name: test num_bytes: 87116926.0 num_examples: 280 download_size: 400625159 dataset_size: 427430458.0 --- # Dataset Card for "class_dataset_real2_donut" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lewtun/bulk-superb-s3p-superb-49606
--- benchmark: superb task: asr type: prediction --- # Batch job model_id: lewtun/superb-s3prl-osanseviero__hubert_base-asr-cbcd177a dataset_name: superb dataset_config: asr dataset_split: test dataset_column: file
dipudl/hc3-and-gpt-wiki-intro-with-perplexity-and-128-window
--- dataset_info: features: - name: prompt dtype: string - name: text dtype: string - name: source dtype: string - name: label dtype: int64 - name: perplexity dtype: float64 splits: - name: train num_bytes: 396594042.354058 num_examples: 330344 - name: test num_bytes: 20925699.0 num_examples: 17387 download_size: 251966356 dataset_size: 417519741.354058 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Vinomaly/1k-sample-comex
--- task_categories: - feature-extraction - text-generation language: - es size_categories: - 1K<n<10K ---
Elatar/Elatar
--- dataset_info: features: - name: func_code_string dtype: string - name: func_documentation_string dtype: string splits: - name: train num_bytes: 41208091 num_examples: 48791 - name: test num_bytes: 1920701 num_examples: 2279 - name: validation num_bytes: 1711210 num_examples: 2209 download_size: 16729518 dataset_size: 44840002 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* ---
vietgpt/arxiv
--- dataset_info: features: - name: text dtype: string - name: meta struct: - name: timestamp dtype: timestamp[s] - name: yymm dtype: string - name: arxiv_id dtype: string - name: language dtype: string - name: url dtype: string splits: - name: train num_bytes: 89337072771 num_examples: 1558306 download_size: 40941434576 dataset_size: 89337072771 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "arxiv" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
somosnlp/Reglamento_aeronautico_Colombiano_QA_RAC1_FULL
--- dataset_info: features: - name: pagina dtype: int64 - name: id dtype: int64 - name: pregunta dtype: string - name: respuesta dtype: string splits: - name: train num_bytes: 488315 num_examples: 2205 download_size: 155260 dataset_size: 488315 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - question-answering language: - es tags: - legal size_categories: - 1K<n<10K --- ## Reglamento aeronautico Colombiano QA ![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/j5PUoYm1teNHwjNHTIdnt.png) ## Descripción geneneral. Este contenido se refiere a un conjunto de datos (dataset) que se ha elaborado basándose en el Reglamento Aeronáutico Colombiano. A partir del contenido original del reglamento, se ha utilizado inteligencia artificial para extraer información relevante y crear un conjunto de preguntas y respuestas. Este proceso permite transformar el reglamento, que puede ser extenso y complejo, en un formato más accesible y comprensible, facilitando el aprendizaje y la revisión de sus normativas para personas interesadas en la aviación colombiana, ya sean profesionales del sector, estudiantes o entusiastas. La utilización de IA para este propósito no solo mejora la eficiencia en la generación de material educativo, sino que también asegura que las preguntas y respuestas sean precisas y estén alineadas con el contenido y espíritu del reglamento. ## Descripción de los objetivos. El proyecto descrito tiene como objetivo principal la creación de un dataset de alta calidad a partir del Reglamento Aeronáutico Colombiano mediante un proceso en dos etapas, utilizando tanto inteligencia artificial como intervención humana. En la primera etapa, se emplea una inteligencia artificial para extraer datos relevantes del reglamento y generar un conjunto preliminar de preguntas y respuestas. Este enfoque automatizado permite cubrir de manera eficiente un amplio espectro del material, identificando temas clave y generando preguntas pertinentes que reflejan el contenido y la estructura del reglamento. En la segunda etapa, estos datos son revisados por etiquetadores humanos. Este equipo de revisores valida las respuestas generadas por la IA, realizando correcciones y ajustes según sea necesario para asegurar la precisión y relevancia del contenido. Este paso de validación es crucial para garantizar la calidad del dataset, pues permite incorporar el entendimiento humano y la interpretación precisa de las normativas, algo que la IA, por avanzada que sea, puede no lograr a la perfección. El dataset final, validado y refinado, se destina a entrenar un modelo de inteligencia artificial más específico y de menor escala. Este modelo está diseñado para realizar tareas concretas relacionadas con el Reglamento Aeronáutico Colombiano, posiblemente incluyendo la automatización de consultas, asistencia en la interpretación de las normativas, y apoyo en la formación y evaluación de personal en el sector aeronáutico. El entrenamiento con datos de alta calidad asegura que el modelo sea efectivo, confiable y preciso en sus tareas designadas, reflejando así el compromiso con la excelencia y la seguridad que caracteriza al sector aeronáutico. ## modelo a fine-tune. Este modelo previamente se a entrenado con el dataset de 'OpenAssistant/oasst2' que contiene mas de 15 idiomas y se hizo un filtro de datos. ``` https://huggingface.co/NickyNicky/gemma-2b-it_oasst2_all_chatML_Unsloth_V1 ``` ## Ejemplo de plantilla basica. Es una plantilla de ejemplo para entrenamiento de gemma-2b. El proposito de esta plantilla es que el modelo aprenda a generalizar sobre las normativas aeronauticas Colombiana. ``` <bos><start_of_turn>system You are a helpful AI assistant. Eres un agente experto en la normativa aeronautica Colombiana.<end_of_turn> <start_of_turn>user ¿Qué aspectos se tratan en el CAPÍTULO II del RAC 1?<end_of_turn> <start_of_turn>model En el CAPÍTULO II del RAC 1 se tratan las expresiones de uso aeronáutico y su significado.<end_of_turn> ``` ## Ejemplo en una variable en Python. ```py # con esto se elimina los interrogantes incompletos. question = "Qué aspectos se tratan en el CAPÍTULO II del RAC 1?".replace("¿","").replace("?","") text = f"""<bos><start_of_turn>system You are a helpful AI assistant. Eres un agente experto en la normativa aeronautica Colombiana.<end_of_turn> <start_of_turn>user ¿{question}?<end_of_turn> <start_of_turn>model """ ``` ## Posibles nombres del modelo. ``` name 1: AeroReg_Col_AI name 2: AeroReg_Cop_AI name 3: AeroReg_AI ``` ## Codigo entrenamiento. ``` En Kamino... ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/WsG8AuwMwgCeruHN7twY7.png)
xu3kev/BIRD-SQL-data
--- dataset_info: features: - name: db_id dtype: string - name: question dtype: string - name: evidence dtype: string - name: SQL dtype: string - name: schema dtype: string splits: - name: train num_bytes: 1039491 num_examples: 200 download_size: 98914 dataset_size: 1039491 --- # Dataset Card for "BIRD-SQL-data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
health360/Healix-V1
--- license: odc-by dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 427613608 num_examples: 796239 download_size: 213902701 dataset_size: 427613608 language: - en tags: - biology - medical size_categories: - 100K<n<1M --- # Healix-V1 Dataset ## Description Healix-V1 is a rich and diverse dataset consisting of 809k Question-Answer pairs within the medical domain. This dataset has been meticulously curated to fuel research initiatives in the areas of medical language understanding, medical dialogue systems, and knowledge extraction. Healix-V1 serves as a valuable resource for developing and improving machine learning models for healthcare applications, enabling them to understand and generate human-like responses in medical context The dataset follows the format used in ALPACA model fine-tuning: ```plaintext ### Input: Question ### Response: Answer ## Data Sources The dataset has been compiled from a variety of valuable and authoritative sources, each contributing different kinds of medical question-answer pairs: 1. **Medical books**: 426,241 QA pairs - These pairs are derived from an array of reputable medical books. The questions were extracted and provided as prompts to GPT-3.5, which in turn generated the corresponding answers. 2. **[jianghc/medical_chatbot](URL)**: 46,867 QA pairs - This is a dataset derived from a medical chatbot project. 3. **The Medical Question and Answering dataset(MQuAD)**: 23,802 QA pairs - MQuAD is a medical dataset specifically designed for the task of question answering. 4. **PubMed**: 1,000 QA pairs - These are pairs extracted from the extensive library of medical articles on PubMed. 5. **GenMedGPT**: 5,000 QA pairs - Derived from the GenMedGPT project aimed at generating medical language. 6. **iCliniq**: 7,321 QA pairs - iCliniq is a platform where users ask health-related questions which are answered by certified doctors. 7. **HealthCareMagic**: 100,000 QA pairs - HealthCareMagic is an interactive health platform with a vast amount of user-generated medical QAs. 8. **medical_meadow_wikidoc**: 10,000 QA pairs - These pairs are extracted from WikiDoc, a free medical textbook. 9. **medical_meadow_wikidoc_medical_flashcards**: 33,955 QA pairs - Medical flashcards provide concise medical information in a Q&A format. 10. **MedQA-USMLE-4-options**: 10,178 QA pairs - These are QAs similar to the format of the USMLE exam for medical licensing in the U.S. ## Potential Applications Healix-V1 can serve a multitude of purposes such as: - Training AI models for medical chatbots - Developing advanced search engines for medical databases - Creating tutoring systems for medical students - Enhancing automated patient assistance systems - Helping in developing systems for medical examination preparation ## Data Length Distribution - (0.0, 256.0]: 96.724181% - (256.0, 512.0]: 2.903792% - (512.0, 768.0]: 0.299476% - (768.0, 1024.0]: 0.050675% - (1024.0, 2048.0]: 0.018910% ## Metadata - **License:** ODC-BY - **Language:** English - **Tags:** Biology, Medical - **Size Categories:** 100K<n<1M ## Dataset Info - **Features:** - name: text - dtype: string - **Splits:** - name: train - num_bytes: 419605911 - num_examples: 798902 - **Download Size:** 209261302 bytes - **Dataset Size:** 419605911 bytes
liuyanchen1015/MULTI_VALUE_mrpc_chaining_main_verbs
--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 7187 num_examples: 28 - name: train num_bytes: 12715 num_examples: 50 - name: validation num_bytes: 1695 num_examples: 6 download_size: 25439 dataset_size: 21597 --- # Dataset Card for "MULTI_VALUE_mrpc_chaining_main_verbs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AlixCF/sample
--- license: cc ---
distilled-from-one-sec-cv12/chunk_222
--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1292622500 num_examples: 251875 download_size: 1321159341 dataset_size: 1292622500 --- # Dataset Card for "chunk_222" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
noxneural/synthetic_beard_styles
--- license: cc-by-4.0 ---
zolak/twitter_dataset_79_1713214792
--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 1437799 num_examples: 3478 download_size: 723323 dataset_size: 1437799 configs: - config_name: default data_files: - split: train path: data/train-* ---
scirik/forecasts
--- license: unknown ---
d0rj/oasst1_pairwise_rlhf_reward-ru
--- dataset_info: features: - name: lang dtype: string - name: parent_id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 67126933.0 num_examples: 17966 - name: validation num_bytes: 3526794.0 num_examples: 952 download_size: 32509550 dataset_size: 70653727.0 --- # Dataset Card for "oasst1_pairwise_rlhf_reward-ru" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yangyz1230/H3
--- dataset_info: features: - name: name dtype: string - name: sequence dtype: string - name: chrom dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: strand dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 307324 num_examples: 545 - name: test num_bytes: 34159 num_examples: 61 download_size: 171279 dataset_size: 341483 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
SAMControlNet/sam-controlnet-sprint-larg-v1
--- dataset_info: features: - name: original_image dtype: image - name: conditioning_image dtype: image - name: overlaid dtype: image - name: caption dtype: string splits: - name: train num_bytes: 915499786.747 num_examples: 2047 download_size: 920626486 dataset_size: 915499786.747 --- # Dataset Card for "sam-controlnet-sprint-larg-v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AV3RT/DATASETS
--- license: openrail ---
longhoang06/Vi-GSM8K
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 5450234 num_examples: 8792 download_size: 2753130 dataset_size: 5450234 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Vi-GSM8K" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mstz/speeddating
--- language: - en tags: - speeddating - tabular_classification - binary_classification pretty_name: Speed dating size_categories: - 1K<n<10K task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts - tabular-classification configs: - dating --- # Speed dating The [Speed dating dataset](https://www.openml.org/search?type=data&sort=nr_of_likes&status=active&id=40536) from OpenML. # Configurations and tasks | **Configuration** | **Task** | Description | |-------------------|---------------------------|---------------------------------------------------------------| | dating | Binary classification | Will the two date? | # Usage ```python from datasets import load_dataset dataset = load_dataset("mstz/speeddating")["train"] ``` # Features |**Features** |**Type** | |---------------------------------------------------|---------| |`is_dater_male` |`int8` | |`dater_age` |`int8` | |`dated_age` |`int8` | |`age_difference` |`int8` | |`dater_race` |`string` | |`dated_race` |`string` | |`are_same_race` |`int8` | |`same_race_importance_for_dater` |`float64`| |`same_religion_importance_for_dater` |`float64`| |`attractiveness_importance_for_dated` |`float64`| |`sincerity_importance_for_dated` |`float64`| |`intelligence_importance_for_dated` |`float64`| |`humor_importance_for_dated` |`float64`| |`ambition_importance_for_dated` |`float64`| |`shared_interests_importance_for_dated` |`float64`| |`attractiveness_score_of_dater_from_dated` |`float64`| |`sincerity_score_of_dater_from_dated` |`float64`| |`intelligence_score_of_dater_from_dated` |`float64`| |`humor_score_of_dater_from_dated` |`float64`| |`ambition_score_of_dater_from_dated` |`float64`| |`shared_interests_score_of_dater_from_dated` |`float64`| |`attractiveness_importance_for_dater` |`float64`| |`sincerity_importance_for_dater` |`float64`| |`intelligence_importance_for_dater` |`float64`| |`humor_importance_for_dater` |`float64`| |`ambition_importance_for_dater` |`float64`| |`shared_interests_importance_for_dater` |`float64`| |`self_reported_attractiveness_of_dater` |`float64`| |`self_reported_sincerity_of_dater` |`float64`| |`self_reported_intelligence_of_dater` |`float64`| |`self_reported_humor_of_dater` |`float64`| |`self_reported_ambition_of_dater` |`float64`| |`reported_attractiveness_of_dated_from_dater` |`float64`| |`reported_sincerity_of_dated_from_dater` |`float64`| |`reported_intelligence_of_dated_from_dater` |`float64`| |`reported_humor_of_dated_from_dater` |`float64`| |`reported_ambition_of_dated_from_dater` |`float64`| |`reported_shared_interests_of_dated_from_dater` |`float64`| |`dater_interest_in_sports` |`float64`| |`dater_interest_in_tvsports` |`float64`| |`dater_interest_in_exercise` |`float64`| |`dater_interest_in_dining` |`float64`| |`dater_interest_in_museums` |`float64`| |`dater_interest_in_art` |`float64`| |`dater_interest_in_hiking` |`float64`| |`dater_interest_in_gaming` |`float64`| |`dater_interest_in_clubbing` |`float64`| |`dater_interest_in_reading` |`float64`| |`dater_interest_in_tv` |`float64`| |`dater_interest_in_theater` |`float64`| |`dater_interest_in_movies` |`float64`| |`dater_interest_in_concerts` |`float64`| |`dater_interest_in_music` |`float64`| |`dater_interest_in_shopping` |`float64`| |`dater_interest_in_yoga` |`float64`| |`interests_correlation` |`float64`| |`expected_satisfaction_of_dater` |`float64`| |`expected_number_of_likes_of_dater_from_20_people` |`int8` | |`expected_number_of_dates_for_dater` |`int8` | |`dater_liked_dated` |`float64`| |`probability_dated_wants_to_date` |`float64`| |`already_met_before` |`int8` | |`dater_wants_to_date` |`int8` | |`dated_wants_to_date` |`int8` |
open-llm-leaderboard/details_TheBloke__chronos-wizardlm-uc-scot-st-13B-GPTQ
--- pretty_name: Evaluation run of TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ](https://huggingface.co/TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__chronos-wizardlm-uc-scot-st-13B-GPTQ_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-07T17:01:57.084059](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__chronos-wizardlm-uc-scot-st-13B-GPTQ_public/blob/main/results_2023-11-07T17-01-57.084059.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.008284395973154363,\n\ \ \"em_stderr\": 0.0009282472025612514,\n \"f1\": 0.0820406879194631,\n\ \ \"f1_stderr\": 0.0018086518070639704,\n \"acc\": 0.40702937397863653,\n\ \ \"acc_stderr\": 0.009614901402107493\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.008284395973154363,\n \"em_stderr\": 0.0009282472025612514,\n\ \ \"f1\": 0.0820406879194631,\n \"f1_stderr\": 0.0018086518070639704\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06899166034874905,\n \ \ \"acc_stderr\": 0.006980995834838566\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n\ \ }\n}\n```" repo_url: https://huggingface.co/TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_11_05T09_19_09.913548 path: - '**/details_harness|drop|3_2023-11-05T09-19-09.913548.parquet' - split: 2023_11_07T17_01_57.084059 path: - '**/details_harness|drop|3_2023-11-07T17-01-57.084059.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-07T17-01-57.084059.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_05T09_19_09.913548 path: - '**/details_harness|gsm8k|5_2023-11-05T09-19-09.913548.parquet' - split: 2023_11_07T17_01_57.084059 path: - '**/details_harness|gsm8k|5_2023-11-07T17-01-57.084059.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-07T17-01-57.084059.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_05T09_19_09.913548 path: - '**/details_harness|winogrande|5_2023-11-05T09-19-09.913548.parquet' - split: 2023_11_07T17_01_57.084059 path: - '**/details_harness|winogrande|5_2023-11-07T17-01-57.084059.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-07T17-01-57.084059.parquet' - config_name: results data_files: - split: 2023_11_05T09_19_09.913548 path: - results_2023-11-05T09-19-09.913548.parquet - split: 2023_11_07T17_01_57.084059 path: - results_2023-11-07T17-01-57.084059.parquet - split: latest path: - results_2023-11-07T17-01-57.084059.parquet --- # Dataset Card for Evaluation run of TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ](https://huggingface.co/TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TheBloke__chronos-wizardlm-uc-scot-st-13B-GPTQ_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-07T17:01:57.084059](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__chronos-wizardlm-uc-scot-st-13B-GPTQ_public/blob/main/results_2023-11-07T17-01-57.084059.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.008284395973154363, "em_stderr": 0.0009282472025612514, "f1": 0.0820406879194631, "f1_stderr": 0.0018086518070639704, "acc": 0.40702937397863653, "acc_stderr": 0.009614901402107493 }, "harness|drop|3": { "em": 0.008284395973154363, "em_stderr": 0.0009282472025612514, "f1": 0.0820406879194631, "f1_stderr": 0.0018086518070639704 }, "harness|gsm8k|5": { "acc": 0.06899166034874905, "acc_stderr": 0.006980995834838566 }, "harness|winogrande|5": { "acc": 0.745067087608524, "acc_stderr": 0.012248806969376422 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
shidowake/cosmopedia-japanese-subset_from_aixsatoshi_filtered-sharegpt-format-no-system-prompt_split_4
--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 19834076.0 num_examples: 2495 download_size: 11956113 dataset_size: 19834076.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-e92f99-1572955855
--- type: predictions tags: - autotrain - evaluation datasets: - mathemakitten/winobias_antistereotype_dev eval_info: task: text_zero_shot_classification model: facebook/opt-350m metrics: [] dataset_name: mathemakitten/winobias_antistereotype_dev dataset_config: mathemakitten--winobias_antistereotype_dev dataset_split: validation col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-350m * Dataset: mathemakitten/winobias_antistereotype_dev * Config: mathemakitten--winobias_antistereotype_dev * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
Rakshit122/1
--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: string splits: - name: train num_bytes: 46270 num_examples: 226 download_size: 16707 dataset_size: 46270 --- # Dataset Card for "1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ChuckMcSneed/guides
--- license: wtfpl --- [LOCAL LLM SPEEDRUN GUIDE](LOCAL%20LLM%20SPEEDRUN%20GUIDE.pdf) - Guide for quick local LLM setup
enelpe/MorSpra_all
--- dataset_info: features: - name: Sentences sequence: string - name: Labels sequence: int64 splits: - name: train num_bytes: 7587838 num_examples: 23196 download_size: 0 dataset_size: 7587838 --- # Dataset Card for "MorSpra_all" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rubentito/mp-docvqa
--- pretty_name: MP-DocVQA (Multipage Document Visual Question Answering) license: mit task_categories: - question-answering - document-question-answering - document-visual-question-answering language: - en multilinguality: - monolingual source_datasets: - Single Page Document Visual Question Answering --- # Dataset Card for Multipage Document Visual Question Answering (MP-DocVQA) ## Dataset Description - **Homepage: [Robust Reading Competition Portal](https://rrc.cvc.uab.es/?ch=17&com=introduction)** - **Repository: [Robust Reading Competition Portal](https://rrc.cvc.uab.es/?ch=17&com=downloads)** - **Paper: [Hierarchical multimodal transformers for Multi-Page DocVQA](https://arxiv.org/abs/2212.05935.pdf])** - **Leaderboard: [Task 4 of DocVQA on the Robust Reading Competition Portal](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=4)** ### Dataset Summary The dataset is aimed to perform Visual Question Answering on multipage industry scanned documents. The questions and answers are reused from Single Page DocVQA (SP-DocVQA) dataset. The images also corresponds to the same in original dataset with previous and posterior pages with a limit of up to 20 pages per document. ### Download the Dataset The dataset is not integrated with Huggingface yet. But you can download it from the [DocVQA Challenge](https://rrc.cvc.uab.es/?ch=17) in the RRC Portal, [Downloads section](https://rrc.cvc.uab.es/?ch=17&com=downloads). ### Leaderboard You can also check the live leaderboard at the [RRC Portal](https://rrc.cvc.uab.es/?ch=17&com=evaluation&task=4) ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits | | Train | Validation | Test | Total | |----------|:-----:|:-----------:|:------:|:-------:| |**Questions** |36230 | 5187 |5019 | 46436 | |**Documents** |5131 | 927 |959 | 5929 | |**Pages / Images** |37269 | 6510 |6223 | 47952 | Note that some documents might appear in both validation and test set. But they are never seen during training. ### Citation Information ```tex @article{tito2022hierarchical, title={Hierarchical multimodal transformers for Multi-Page DocVQA}, author={Tito, Rub{\`e}n and Karatzas, Dimosthenis and Valveny, Ernest}, journal={arXiv preprint arXiv:2212.05935}, year={2022} } ```
Carlosgg14/gojovoicemakers
--- license: openrail ---
irds/mmarco_v2_zh
--- pretty_name: '`mmarco/v2/zh`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `mmarco/v2/zh` The `mmarco/v2/zh` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/zh). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=8,841,823 This dataset is used by: [`mmarco_v2_zh_dev`](https://huggingface.co/datasets/irds/mmarco_v2_zh_dev), [`mmarco_v2_zh_train`](https://huggingface.co/datasets/irds/mmarco_v2_zh_train) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/mmarco_v2_zh', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Bonifacio2021MMarco, title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset}, author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira}, year={2021}, journal={arXiv:2108.13897} } ```
autoevaluate/autoeval-eval-phpthinh__exampletx-toxic-7252ee-1708159806
--- type: predictions tags: - autotrain - evaluation datasets: - phpthinh/exampletx eval_info: task: text_zero_shot_classification model: bigscience/bloom-7b1 metrics: [] dataset_name: phpthinh/exampletx dataset_config: toxic dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: bigscience/bloom-7b1 * Dataset: phpthinh/exampletx * Config: toxic * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.
DZN222/taspio
--- license: openrail ---
abhinand/argilla-dpo-mix-7k-singleturn
--- dataset_info: features: - name: dataset dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_rating dtype: float64 - name: rejected_rating dtype: float64 - name: prompt dtype: string - name: system dtype: string splits: - name: train num_bytes: 14543208 num_examples: 4901 download_size: 8237623 dataset_size: 14543208 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/lana_fireemblem
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of lana (Fire Emblem) This is the dataset of lana (Fire Emblem), containing 22 images and their tags. The core tags of this character are `short_hair, brown_eyes, orange_hair, brown_hair, blonde_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 22 | 20.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lana_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 22 | 14.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lana_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 41 | 26.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lana_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 22 | 20.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lana_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 41 | 33.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lana_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/lana_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------| | 0 | 22 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, smile, solo, open_mouth, blush, dress, staff | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | open_mouth | blush | dress | staff | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-------------|:--------|:--------|:--------| | 0 | 22 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X |
dumyy/test_dummy
--- license: openrail dataset_info: features: - name: pokemon dtype: string - name: label dtype: string splits: - name: train num_bytes: 43 num_examples: 2 download_size: 1219 dataset_size: 43 configs: - config_name: default data_files: - split: train path: data/train-* ---
introspector/meta-coq-utils
--- license: mit ---
Tverous/misinfo-clusters3
--- dataset_info: features: - name: cluster_id dtype: string - name: doc_id dtype: string - name: main_text dtype: string - name: image dtype: image - name: video dtype: string - name: audio dtype: string - name: kg_embedding sequence: sequence: float32 splits: - name: train num_bytes: 198061.0 num_examples: 1 download_size: 177682 dataset_size: 198061.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "misinfo-clusters3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Giacinta/djy
--- license: apache-2.0 task_categories: - text-classification language: - zh tags: - medical pretty_name: djy size_categories: - n<1K configs: - config_name: default data_files: - split: train path: "60_percent_data.csv" - split: test path: "part1.csv" - split: eval path: "part2.csv" ---
CerebralAI/ActionRoutes_Phi2_ZeroShot
--- dataset_info: features: - name: texts dtype: string splits: - name: train num_bytes: 7101466 num_examples: 5020 download_size: 1044489 dataset_size: 7101466 configs: - config_name: default data_files: - split: train path: data/train-* ---
chrisociepa/wikipedia-pl-20230401
--- license: cc-by-sa-3.0 dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 2883878741 num_examples: 1562327 download_size: 1761971402 dataset_size: 2883878741 task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling language: - pl pretty_name: Polish Wikipedia 2023-04-01 size_categories: - 1M<n<10M tags: - pretraining - language modelling - wikipedia - web --- # Dataset Card for April 2023 Polish Wikipedia Wikipedia dataset containing cleaned articles of Polish language. The dataset has been built from the Wikipedia dump (https://dumps.wikimedia.org/) using the [OLM Project](https://github.com/huggingface/olm-datasets). Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). ### Licensing Information Most of Wikipedia's text and many of its images are co-licensed under the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License) (CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License) (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text. ### Citation Information ``` @ONLINE{wikidump, author = "Wikimedia Foundation", title = "Wikimedia Downloads", url = "https://dumps.wikimedia.org" } ```
mertllc/f
--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string - name: speaker_id dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 10350476.0 num_examples: 500 download_size: 10292806 dataset_size: 10350476.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
as-cle-bert/breastcancer-semantic-segmentation
--- license: cc dataset_info: features: - name: pixel_values dtype: image - name: label dtype: image splits: - name: train num_bytes: 48963186.0 num_examples: 40 download_size: 9355520 dataset_size: 48963186.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
grasshoff/lhc_sents
--- license: bsd ---
Saaddazhhar/predictiveswotanalysis
--- license: cc0-1.0 ---
BangumiBase/sailormoon1990s
--- license: mit tags: - art size_categories: - 10K<n<100K --- # Bangumi Image Base of Sailor Moon (1990s) This is the image base of bangumi Sailor Moon (1990s), we detected 132 characters, 14684 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------| | 0 | 3008 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 94 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 696 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 49 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 29 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 176 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 95 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 72 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 180 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 75 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 108 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 113 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 32 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 42 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 47 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 602 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 1066 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 395 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 208 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 79 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 86 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 62 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 50 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 53 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 76 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 141 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 67 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 45 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 750 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 103 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 34 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | ![preview 7](30/preview_7.png) | ![preview 8](30/preview_8.png) | | 31 | 42 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | ![preview 8](31/preview_8.png) | | 32 | 20 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 67 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 79 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 40 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 45 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | ![preview 8](36/preview_8.png) | | 37 | 118 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 41 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 62 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 93 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 79 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | 42 | 920 | [Download](42/dataset.zip) | ![preview 1](42/preview_1.png) | ![preview 2](42/preview_2.png) | ![preview 3](42/preview_3.png) | ![preview 4](42/preview_4.png) | ![preview 5](42/preview_5.png) | ![preview 6](42/preview_6.png) | ![preview 7](42/preview_7.png) | ![preview 8](42/preview_8.png) | | 43 | 55 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 75 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 36 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | ![preview 6](45/preview_6.png) | ![preview 7](45/preview_7.png) | ![preview 8](45/preview_8.png) | | 46 | 15 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | ![preview 8](46/preview_8.png) | | 47 | 126 | [Download](47/dataset.zip) | ![preview 1](47/preview_1.png) | ![preview 2](47/preview_2.png) | ![preview 3](47/preview_3.png) | ![preview 4](47/preview_4.png) | ![preview 5](47/preview_5.png) | ![preview 6](47/preview_6.png) | ![preview 7](47/preview_7.png) | ![preview 8](47/preview_8.png) | | 48 | 41 | [Download](48/dataset.zip) | ![preview 1](48/preview_1.png) | ![preview 2](48/preview_2.png) | ![preview 3](48/preview_3.png) | ![preview 4](48/preview_4.png) | ![preview 5](48/preview_5.png) | ![preview 6](48/preview_6.png) | ![preview 7](48/preview_7.png) | ![preview 8](48/preview_8.png) | | 49 | 46 | [Download](49/dataset.zip) | ![preview 1](49/preview_1.png) | ![preview 2](49/preview_2.png) | ![preview 3](49/preview_3.png) | ![preview 4](49/preview_4.png) | ![preview 5](49/preview_5.png) | ![preview 6](49/preview_6.png) | ![preview 7](49/preview_7.png) | ![preview 8](49/preview_8.png) | | 50 | 100 | [Download](50/dataset.zip) | ![preview 1](50/preview_1.png) | ![preview 2](50/preview_2.png) | ![preview 3](50/preview_3.png) | ![preview 4](50/preview_4.png) | ![preview 5](50/preview_5.png) | ![preview 6](50/preview_6.png) | ![preview 7](50/preview_7.png) | ![preview 8](50/preview_8.png) | | 51 | 121 | [Download](51/dataset.zip) | ![preview 1](51/preview_1.png) | ![preview 2](51/preview_2.png) | ![preview 3](51/preview_3.png) | ![preview 4](51/preview_4.png) | ![preview 5](51/preview_5.png) | ![preview 6](51/preview_6.png) | ![preview 7](51/preview_7.png) | ![preview 8](51/preview_8.png) | | 52 | 36 | [Download](52/dataset.zip) | ![preview 1](52/preview_1.png) | ![preview 2](52/preview_2.png) | ![preview 3](52/preview_3.png) | ![preview 4](52/preview_4.png) | ![preview 5](52/preview_5.png) | ![preview 6](52/preview_6.png) | ![preview 7](52/preview_7.png) | ![preview 8](52/preview_8.png) | | 53 | 102 | [Download](53/dataset.zip) | ![preview 1](53/preview_1.png) | ![preview 2](53/preview_2.png) | ![preview 3](53/preview_3.png) | ![preview 4](53/preview_4.png) | ![preview 5](53/preview_5.png) | ![preview 6](53/preview_6.png) | ![preview 7](53/preview_7.png) | ![preview 8](53/preview_8.png) | | 54 | 50 | [Download](54/dataset.zip) | ![preview 1](54/preview_1.png) | ![preview 2](54/preview_2.png) | ![preview 3](54/preview_3.png) | ![preview 4](54/preview_4.png) | ![preview 5](54/preview_5.png) | ![preview 6](54/preview_6.png) | ![preview 7](54/preview_7.png) | ![preview 8](54/preview_8.png) | | 55 | 105 | [Download](55/dataset.zip) | ![preview 1](55/preview_1.png) | ![preview 2](55/preview_2.png) | ![preview 3](55/preview_3.png) | ![preview 4](55/preview_4.png) | ![preview 5](55/preview_5.png) | ![preview 6](55/preview_6.png) | ![preview 7](55/preview_7.png) | ![preview 8](55/preview_8.png) | | 56 | 47 | [Download](56/dataset.zip) | ![preview 1](56/preview_1.png) | ![preview 2](56/preview_2.png) | ![preview 3](56/preview_3.png) | ![preview 4](56/preview_4.png) | ![preview 5](56/preview_5.png) | ![preview 6](56/preview_6.png) | ![preview 7](56/preview_7.png) | ![preview 8](56/preview_8.png) | | 57 | 60 | [Download](57/dataset.zip) | ![preview 1](57/preview_1.png) | ![preview 2](57/preview_2.png) | ![preview 3](57/preview_3.png) | ![preview 4](57/preview_4.png) | ![preview 5](57/preview_5.png) | ![preview 6](57/preview_6.png) | ![preview 7](57/preview_7.png) | ![preview 8](57/preview_8.png) | | 58 | 26 | [Download](58/dataset.zip) | ![preview 1](58/preview_1.png) | ![preview 2](58/preview_2.png) | ![preview 3](58/preview_3.png) | ![preview 4](58/preview_4.png) | ![preview 5](58/preview_5.png) | ![preview 6](58/preview_6.png) | ![preview 7](58/preview_7.png) | ![preview 8](58/preview_8.png) | | 59 | 47 | [Download](59/dataset.zip) | ![preview 1](59/preview_1.png) | ![preview 2](59/preview_2.png) | ![preview 3](59/preview_3.png) | ![preview 4](59/preview_4.png) | ![preview 5](59/preview_5.png) | ![preview 6](59/preview_6.png) | ![preview 7](59/preview_7.png) | ![preview 8](59/preview_8.png) | | 60 | 79 | [Download](60/dataset.zip) | ![preview 1](60/preview_1.png) | ![preview 2](60/preview_2.png) | ![preview 3](60/preview_3.png) | ![preview 4](60/preview_4.png) | ![preview 5](60/preview_5.png) | ![preview 6](60/preview_6.png) | ![preview 7](60/preview_7.png) | ![preview 8](60/preview_8.png) | | 61 | 74 | [Download](61/dataset.zip) | ![preview 1](61/preview_1.png) | ![preview 2](61/preview_2.png) | ![preview 3](61/preview_3.png) | ![preview 4](61/preview_4.png) | ![preview 5](61/preview_5.png) | ![preview 6](61/preview_6.png) | ![preview 7](61/preview_7.png) | ![preview 8](61/preview_8.png) | | 62 | 11 | [Download](62/dataset.zip) | ![preview 1](62/preview_1.png) | ![preview 2](62/preview_2.png) | ![preview 3](62/preview_3.png) | ![preview 4](62/preview_4.png) | ![preview 5](62/preview_5.png) | ![preview 6](62/preview_6.png) | ![preview 7](62/preview_7.png) | ![preview 8](62/preview_8.png) | | 63 | 73 | [Download](63/dataset.zip) | ![preview 1](63/preview_1.png) | ![preview 2](63/preview_2.png) | ![preview 3](63/preview_3.png) | ![preview 4](63/preview_4.png) | ![preview 5](63/preview_5.png) | ![preview 6](63/preview_6.png) | ![preview 7](63/preview_7.png) | ![preview 8](63/preview_8.png) | | 64 | 30 | [Download](64/dataset.zip) | ![preview 1](64/preview_1.png) | ![preview 2](64/preview_2.png) | ![preview 3](64/preview_3.png) | ![preview 4](64/preview_4.png) | ![preview 5](64/preview_5.png) | ![preview 6](64/preview_6.png) | ![preview 7](64/preview_7.png) | ![preview 8](64/preview_8.png) | | 65 | 32 | [Download](65/dataset.zip) | ![preview 1](65/preview_1.png) | ![preview 2](65/preview_2.png) | ![preview 3](65/preview_3.png) | ![preview 4](65/preview_4.png) | ![preview 5](65/preview_5.png) | ![preview 6](65/preview_6.png) | ![preview 7](65/preview_7.png) | ![preview 8](65/preview_8.png) | | 66 | 102 | [Download](66/dataset.zip) | ![preview 1](66/preview_1.png) | ![preview 2](66/preview_2.png) | ![preview 3](66/preview_3.png) | ![preview 4](66/preview_4.png) | ![preview 5](66/preview_5.png) | ![preview 6](66/preview_6.png) | ![preview 7](66/preview_7.png) | ![preview 8](66/preview_8.png) | | 67 | 17 | [Download](67/dataset.zip) | ![preview 1](67/preview_1.png) | ![preview 2](67/preview_2.png) | ![preview 3](67/preview_3.png) | ![preview 4](67/preview_4.png) | ![preview 5](67/preview_5.png) | ![preview 6](67/preview_6.png) | ![preview 7](67/preview_7.png) | ![preview 8](67/preview_8.png) | | 68 | 49 | [Download](68/dataset.zip) | ![preview 1](68/preview_1.png) | ![preview 2](68/preview_2.png) | ![preview 3](68/preview_3.png) | ![preview 4](68/preview_4.png) | ![preview 5](68/preview_5.png) | ![preview 6](68/preview_6.png) | ![preview 7](68/preview_7.png) | ![preview 8](68/preview_8.png) | | 69 | 24 | [Download](69/dataset.zip) | ![preview 1](69/preview_1.png) | ![preview 2](69/preview_2.png) | ![preview 3](69/preview_3.png) | ![preview 4](69/preview_4.png) | ![preview 5](69/preview_5.png) | ![preview 6](69/preview_6.png) | ![preview 7](69/preview_7.png) | ![preview 8](69/preview_8.png) | | 70 | 28 | [Download](70/dataset.zip) | ![preview 1](70/preview_1.png) | ![preview 2](70/preview_2.png) | ![preview 3](70/preview_3.png) | ![preview 4](70/preview_4.png) | ![preview 5](70/preview_5.png) | ![preview 6](70/preview_6.png) | ![preview 7](70/preview_7.png) | ![preview 8](70/preview_8.png) | | 71 | 38 | [Download](71/dataset.zip) | ![preview 1](71/preview_1.png) | ![preview 2](71/preview_2.png) | ![preview 3](71/preview_3.png) | ![preview 4](71/preview_4.png) | ![preview 5](71/preview_5.png) | ![preview 6](71/preview_6.png) | ![preview 7](71/preview_7.png) | ![preview 8](71/preview_8.png) | | 72 | 96 | [Download](72/dataset.zip) | ![preview 1](72/preview_1.png) | ![preview 2](72/preview_2.png) | ![preview 3](72/preview_3.png) | ![preview 4](72/preview_4.png) | ![preview 5](72/preview_5.png) | ![preview 6](72/preview_6.png) | ![preview 7](72/preview_7.png) | ![preview 8](72/preview_8.png) | | 73 | 52 | [Download](73/dataset.zip) | ![preview 1](73/preview_1.png) | ![preview 2](73/preview_2.png) | ![preview 3](73/preview_3.png) | ![preview 4](73/preview_4.png) | ![preview 5](73/preview_5.png) | ![preview 6](73/preview_6.png) | ![preview 7](73/preview_7.png) | ![preview 8](73/preview_8.png) | | 74 | 747 | [Download](74/dataset.zip) | ![preview 1](74/preview_1.png) | ![preview 2](74/preview_2.png) | ![preview 3](74/preview_3.png) | ![preview 4](74/preview_4.png) | ![preview 5](74/preview_5.png) | ![preview 6](74/preview_6.png) | ![preview 7](74/preview_7.png) | ![preview 8](74/preview_8.png) | | 75 | 50 | [Download](75/dataset.zip) | ![preview 1](75/preview_1.png) | ![preview 2](75/preview_2.png) | ![preview 3](75/preview_3.png) | ![preview 4](75/preview_4.png) | ![preview 5](75/preview_5.png) | ![preview 6](75/preview_6.png) | ![preview 7](75/preview_7.png) | ![preview 8](75/preview_8.png) | | 76 | 43 | [Download](76/dataset.zip) | ![preview 1](76/preview_1.png) | ![preview 2](76/preview_2.png) | ![preview 3](76/preview_3.png) | ![preview 4](76/preview_4.png) | ![preview 5](76/preview_5.png) | ![preview 6](76/preview_6.png) | ![preview 7](76/preview_7.png) | ![preview 8](76/preview_8.png) | | 77 | 21 | [Download](77/dataset.zip) | ![preview 1](77/preview_1.png) | ![preview 2](77/preview_2.png) | ![preview 3](77/preview_3.png) | ![preview 4](77/preview_4.png) | ![preview 5](77/preview_5.png) | ![preview 6](77/preview_6.png) | ![preview 7](77/preview_7.png) | ![preview 8](77/preview_8.png) | | 78 | 22 | [Download](78/dataset.zip) | ![preview 1](78/preview_1.png) | ![preview 2](78/preview_2.png) | ![preview 3](78/preview_3.png) | ![preview 4](78/preview_4.png) | ![preview 5](78/preview_5.png) | ![preview 6](78/preview_6.png) | ![preview 7](78/preview_7.png) | ![preview 8](78/preview_8.png) | | 79 | 23 | [Download](79/dataset.zip) | ![preview 1](79/preview_1.png) | ![preview 2](79/preview_2.png) | ![preview 3](79/preview_3.png) | ![preview 4](79/preview_4.png) | ![preview 5](79/preview_5.png) | ![preview 6](79/preview_6.png) | ![preview 7](79/preview_7.png) | ![preview 8](79/preview_8.png) | | 80 | 38 | [Download](80/dataset.zip) | ![preview 1](80/preview_1.png) | ![preview 2](80/preview_2.png) | ![preview 3](80/preview_3.png) | ![preview 4](80/preview_4.png) | ![preview 5](80/preview_5.png) | ![preview 6](80/preview_6.png) | ![preview 7](80/preview_7.png) | ![preview 8](80/preview_8.png) | | 81 | 20 | [Download](81/dataset.zip) | ![preview 1](81/preview_1.png) | ![preview 2](81/preview_2.png) | ![preview 3](81/preview_3.png) | ![preview 4](81/preview_4.png) | ![preview 5](81/preview_5.png) | ![preview 6](81/preview_6.png) | ![preview 7](81/preview_7.png) | ![preview 8](81/preview_8.png) | | 82 | 44 | [Download](82/dataset.zip) | ![preview 1](82/preview_1.png) | ![preview 2](82/preview_2.png) | ![preview 3](82/preview_3.png) | ![preview 4](82/preview_4.png) | ![preview 5](82/preview_5.png) | ![preview 6](82/preview_6.png) | ![preview 7](82/preview_7.png) | ![preview 8](82/preview_8.png) | | 83 | 19 | [Download](83/dataset.zip) | ![preview 1](83/preview_1.png) | ![preview 2](83/preview_2.png) | ![preview 3](83/preview_3.png) | ![preview 4](83/preview_4.png) | ![preview 5](83/preview_5.png) | ![preview 6](83/preview_6.png) | ![preview 7](83/preview_7.png) | ![preview 8](83/preview_8.png) | | 84 | 19 | [Download](84/dataset.zip) | ![preview 1](84/preview_1.png) | ![preview 2](84/preview_2.png) | ![preview 3](84/preview_3.png) | ![preview 4](84/preview_4.png) | ![preview 5](84/preview_5.png) | ![preview 6](84/preview_6.png) | ![preview 7](84/preview_7.png) | ![preview 8](84/preview_8.png) | | 85 | 19 | [Download](85/dataset.zip) | ![preview 1](85/preview_1.png) | ![preview 2](85/preview_2.png) | ![preview 3](85/preview_3.png) | ![preview 4](85/preview_4.png) | ![preview 5](85/preview_5.png) | ![preview 6](85/preview_6.png) | ![preview 7](85/preview_7.png) | ![preview 8](85/preview_8.png) | | 86 | 11 | [Download](86/dataset.zip) | ![preview 1](86/preview_1.png) | ![preview 2](86/preview_2.png) | ![preview 3](86/preview_3.png) | ![preview 4](86/preview_4.png) | ![preview 5](86/preview_5.png) | ![preview 6](86/preview_6.png) | ![preview 7](86/preview_7.png) | ![preview 8](86/preview_8.png) | | 87 | 48 | [Download](87/dataset.zip) | ![preview 1](87/preview_1.png) | ![preview 2](87/preview_2.png) | ![preview 3](87/preview_3.png) | ![preview 4](87/preview_4.png) | ![preview 5](87/preview_5.png) | ![preview 6](87/preview_6.png) | ![preview 7](87/preview_7.png) | ![preview 8](87/preview_8.png) | | 88 | 18 | [Download](88/dataset.zip) | ![preview 1](88/preview_1.png) | ![preview 2](88/preview_2.png) | ![preview 3](88/preview_3.png) | ![preview 4](88/preview_4.png) | ![preview 5](88/preview_5.png) | ![preview 6](88/preview_6.png) | ![preview 7](88/preview_7.png) | ![preview 8](88/preview_8.png) | | 89 | 14 | [Download](89/dataset.zip) | ![preview 1](89/preview_1.png) | ![preview 2](89/preview_2.png) | ![preview 3](89/preview_3.png) | ![preview 4](89/preview_4.png) | ![preview 5](89/preview_5.png) | ![preview 6](89/preview_6.png) | ![preview 7](89/preview_7.png) | ![preview 8](89/preview_8.png) | | 90 | 24 | [Download](90/dataset.zip) | ![preview 1](90/preview_1.png) | ![preview 2](90/preview_2.png) | ![preview 3](90/preview_3.png) | ![preview 4](90/preview_4.png) | ![preview 5](90/preview_5.png) | ![preview 6](90/preview_6.png) | ![preview 7](90/preview_7.png) | ![preview 8](90/preview_8.png) | | 91 | 19 | [Download](91/dataset.zip) | ![preview 1](91/preview_1.png) | ![preview 2](91/preview_2.png) | ![preview 3](91/preview_3.png) | ![preview 4](91/preview_4.png) | ![preview 5](91/preview_5.png) | ![preview 6](91/preview_6.png) | ![preview 7](91/preview_7.png) | ![preview 8](91/preview_8.png) | | 92 | 10 | [Download](92/dataset.zip) | ![preview 1](92/preview_1.png) | ![preview 2](92/preview_2.png) | ![preview 3](92/preview_3.png) | ![preview 4](92/preview_4.png) | ![preview 5](92/preview_5.png) | ![preview 6](92/preview_6.png) | ![preview 7](92/preview_7.png) | ![preview 8](92/preview_8.png) | | 93 | 10 | [Download](93/dataset.zip) | ![preview 1](93/preview_1.png) | ![preview 2](93/preview_2.png) | ![preview 3](93/preview_3.png) | ![preview 4](93/preview_4.png) | ![preview 5](93/preview_5.png) | ![preview 6](93/preview_6.png) | ![preview 7](93/preview_7.png) | ![preview 8](93/preview_8.png) | | 94 | 33 | [Download](94/dataset.zip) | ![preview 1](94/preview_1.png) | ![preview 2](94/preview_2.png) | ![preview 3](94/preview_3.png) | ![preview 4](94/preview_4.png) | ![preview 5](94/preview_5.png) | ![preview 6](94/preview_6.png) | ![preview 7](94/preview_7.png) | ![preview 8](94/preview_8.png) | | 95 | 28 | [Download](95/dataset.zip) | ![preview 1](95/preview_1.png) | ![preview 2](95/preview_2.png) | ![preview 3](95/preview_3.png) | ![preview 4](95/preview_4.png) | ![preview 5](95/preview_5.png) | ![preview 6](95/preview_6.png) | ![preview 7](95/preview_7.png) | ![preview 8](95/preview_8.png) | | 96 | 58 | [Download](96/dataset.zip) | ![preview 1](96/preview_1.png) | ![preview 2](96/preview_2.png) | ![preview 3](96/preview_3.png) | ![preview 4](96/preview_4.png) | ![preview 5](96/preview_5.png) | ![preview 6](96/preview_6.png) | ![preview 7](96/preview_7.png) | ![preview 8](96/preview_8.png) | | 97 | 13 | [Download](97/dataset.zip) | ![preview 1](97/preview_1.png) | ![preview 2](97/preview_2.png) | ![preview 3](97/preview_3.png) | ![preview 4](97/preview_4.png) | ![preview 5](97/preview_5.png) | ![preview 6](97/preview_6.png) | ![preview 7](97/preview_7.png) | ![preview 8](97/preview_8.png) | | 98 | 29 | [Download](98/dataset.zip) | ![preview 1](98/preview_1.png) | ![preview 2](98/preview_2.png) | ![preview 3](98/preview_3.png) | ![preview 4](98/preview_4.png) | ![preview 5](98/preview_5.png) | ![preview 6](98/preview_6.png) | ![preview 7](98/preview_7.png) | ![preview 8](98/preview_8.png) | | 99 | 17 | [Download](99/dataset.zip) | ![preview 1](99/preview_1.png) | ![preview 2](99/preview_2.png) | ![preview 3](99/preview_3.png) | ![preview 4](99/preview_4.png) | ![preview 5](99/preview_5.png) | ![preview 6](99/preview_6.png) | ![preview 7](99/preview_7.png) | ![preview 8](99/preview_8.png) | | 100 | 32 | [Download](100/dataset.zip) | ![preview 1](100/preview_1.png) | ![preview 2](100/preview_2.png) | ![preview 3](100/preview_3.png) | ![preview 4](100/preview_4.png) | ![preview 5](100/preview_5.png) | ![preview 6](100/preview_6.png) | ![preview 7](100/preview_7.png) | ![preview 8](100/preview_8.png) | | 101 | 21 | [Download](101/dataset.zip) | ![preview 1](101/preview_1.png) | ![preview 2](101/preview_2.png) | ![preview 3](101/preview_3.png) | ![preview 4](101/preview_4.png) | ![preview 5](101/preview_5.png) | ![preview 6](101/preview_6.png) | ![preview 7](101/preview_7.png) | ![preview 8](101/preview_8.png) | | 102 | 27 | [Download](102/dataset.zip) | ![preview 1](102/preview_1.png) | ![preview 2](102/preview_2.png) | ![preview 3](102/preview_3.png) | ![preview 4](102/preview_4.png) | ![preview 5](102/preview_5.png) | ![preview 6](102/preview_6.png) | ![preview 7](102/preview_7.png) | ![preview 8](102/preview_8.png) | | 103 | 22 | [Download](103/dataset.zip) | ![preview 1](103/preview_1.png) | ![preview 2](103/preview_2.png) | ![preview 3](103/preview_3.png) | ![preview 4](103/preview_4.png) | ![preview 5](103/preview_5.png) | ![preview 6](103/preview_6.png) | ![preview 7](103/preview_7.png) | ![preview 8](103/preview_8.png) | | 104 | 11 | [Download](104/dataset.zip) | ![preview 1](104/preview_1.png) | ![preview 2](104/preview_2.png) | ![preview 3](104/preview_3.png) | ![preview 4](104/preview_4.png) | ![preview 5](104/preview_5.png) | ![preview 6](104/preview_6.png) | ![preview 7](104/preview_7.png) | ![preview 8](104/preview_8.png) | | 105 | 7 | [Download](105/dataset.zip) | ![preview 1](105/preview_1.png) | ![preview 2](105/preview_2.png) | ![preview 3](105/preview_3.png) | ![preview 4](105/preview_4.png) | ![preview 5](105/preview_5.png) | ![preview 6](105/preview_6.png) | ![preview 7](105/preview_7.png) | N/A | | 106 | 12 | [Download](106/dataset.zip) | ![preview 1](106/preview_1.png) | ![preview 2](106/preview_2.png) | ![preview 3](106/preview_3.png) | ![preview 4](106/preview_4.png) | ![preview 5](106/preview_5.png) | ![preview 6](106/preview_6.png) | ![preview 7](106/preview_7.png) | ![preview 8](106/preview_8.png) | | 107 | 14 | [Download](107/dataset.zip) | ![preview 1](107/preview_1.png) | ![preview 2](107/preview_2.png) | ![preview 3](107/preview_3.png) | ![preview 4](107/preview_4.png) | ![preview 5](107/preview_5.png) | ![preview 6](107/preview_6.png) | ![preview 7](107/preview_7.png) | ![preview 8](107/preview_8.png) | | 108 | 22 | [Download](108/dataset.zip) | ![preview 1](108/preview_1.png) | ![preview 2](108/preview_2.png) | ![preview 3](108/preview_3.png) | ![preview 4](108/preview_4.png) | ![preview 5](108/preview_5.png) | ![preview 6](108/preview_6.png) | ![preview 7](108/preview_7.png) | ![preview 8](108/preview_8.png) | | 109 | 21 | [Download](109/dataset.zip) | ![preview 1](109/preview_1.png) | ![preview 2](109/preview_2.png) | ![preview 3](109/preview_3.png) | ![preview 4](109/preview_4.png) | ![preview 5](109/preview_5.png) | ![preview 6](109/preview_6.png) | ![preview 7](109/preview_7.png) | ![preview 8](109/preview_8.png) | | 110 | 25 | [Download](110/dataset.zip) | ![preview 1](110/preview_1.png) | ![preview 2](110/preview_2.png) | ![preview 3](110/preview_3.png) | ![preview 4](110/preview_4.png) | ![preview 5](110/preview_5.png) | ![preview 6](110/preview_6.png) | ![preview 7](110/preview_7.png) | ![preview 8](110/preview_8.png) | | 111 | 45 | [Download](111/dataset.zip) | ![preview 1](111/preview_1.png) | ![preview 2](111/preview_2.png) | ![preview 3](111/preview_3.png) | ![preview 4](111/preview_4.png) | ![preview 5](111/preview_5.png) | ![preview 6](111/preview_6.png) | ![preview 7](111/preview_7.png) | ![preview 8](111/preview_8.png) | | 112 | 11 | [Download](112/dataset.zip) | ![preview 1](112/preview_1.png) | ![preview 2](112/preview_2.png) | ![preview 3](112/preview_3.png) | ![preview 4](112/preview_4.png) | ![preview 5](112/preview_5.png) | ![preview 6](112/preview_6.png) | ![preview 7](112/preview_7.png) | ![preview 8](112/preview_8.png) | | 113 | 23 | [Download](113/dataset.zip) | ![preview 1](113/preview_1.png) | ![preview 2](113/preview_2.png) | ![preview 3](113/preview_3.png) | ![preview 4](113/preview_4.png) | ![preview 5](113/preview_5.png) | ![preview 6](113/preview_6.png) | ![preview 7](113/preview_7.png) | ![preview 8](113/preview_8.png) | | 114 | 14 | [Download](114/dataset.zip) | ![preview 1](114/preview_1.png) | ![preview 2](114/preview_2.png) | ![preview 3](114/preview_3.png) | ![preview 4](114/preview_4.png) | ![preview 5](114/preview_5.png) | ![preview 6](114/preview_6.png) | ![preview 7](114/preview_7.png) | ![preview 8](114/preview_8.png) | | 115 | 39 | [Download](115/dataset.zip) | ![preview 1](115/preview_1.png) | ![preview 2](115/preview_2.png) | ![preview 3](115/preview_3.png) | ![preview 4](115/preview_4.png) | ![preview 5](115/preview_5.png) | ![preview 6](115/preview_6.png) | ![preview 7](115/preview_7.png) | ![preview 8](115/preview_8.png) | | 116 | 17 | [Download](116/dataset.zip) | ![preview 1](116/preview_1.png) | ![preview 2](116/preview_2.png) | ![preview 3](116/preview_3.png) | ![preview 4](116/preview_4.png) | ![preview 5](116/preview_5.png) | ![preview 6](116/preview_6.png) | ![preview 7](116/preview_7.png) | ![preview 8](116/preview_8.png) | | 117 | 27 | [Download](117/dataset.zip) | ![preview 1](117/preview_1.png) | ![preview 2](117/preview_2.png) | ![preview 3](117/preview_3.png) | ![preview 4](117/preview_4.png) | ![preview 5](117/preview_5.png) | ![preview 6](117/preview_6.png) | ![preview 7](117/preview_7.png) | ![preview 8](117/preview_8.png) | | 118 | 56 | [Download](118/dataset.zip) | ![preview 1](118/preview_1.png) | ![preview 2](118/preview_2.png) | ![preview 3](118/preview_3.png) | ![preview 4](118/preview_4.png) | ![preview 5](118/preview_5.png) | ![preview 6](118/preview_6.png) | ![preview 7](118/preview_7.png) | ![preview 8](118/preview_8.png) | | 119 | 19 | [Download](119/dataset.zip) | ![preview 1](119/preview_1.png) | ![preview 2](119/preview_2.png) | ![preview 3](119/preview_3.png) | ![preview 4](119/preview_4.png) | ![preview 5](119/preview_5.png) | ![preview 6](119/preview_6.png) | ![preview 7](119/preview_7.png) | ![preview 8](119/preview_8.png) | | 120 | 17 | [Download](120/dataset.zip) | ![preview 1](120/preview_1.png) | ![preview 2](120/preview_2.png) | ![preview 3](120/preview_3.png) | ![preview 4](120/preview_4.png) | ![preview 5](120/preview_5.png) | ![preview 6](120/preview_6.png) | ![preview 7](120/preview_7.png) | ![preview 8](120/preview_8.png) | | 121 | 14 | [Download](121/dataset.zip) | ![preview 1](121/preview_1.png) | ![preview 2](121/preview_2.png) | ![preview 3](121/preview_3.png) | ![preview 4](121/preview_4.png) | ![preview 5](121/preview_5.png) | ![preview 6](121/preview_6.png) | ![preview 7](121/preview_7.png) | ![preview 8](121/preview_8.png) | | 122 | 12 | [Download](122/dataset.zip) | ![preview 1](122/preview_1.png) | ![preview 2](122/preview_2.png) | ![preview 3](122/preview_3.png) | ![preview 4](122/preview_4.png) | ![preview 5](122/preview_5.png) | ![preview 6](122/preview_6.png) | ![preview 7](122/preview_7.png) | ![preview 8](122/preview_8.png) | | 123 | 103 | [Download](123/dataset.zip) | ![preview 1](123/preview_1.png) | ![preview 2](123/preview_2.png) | ![preview 3](123/preview_3.png) | ![preview 4](123/preview_4.png) | ![preview 5](123/preview_5.png) | ![preview 6](123/preview_6.png) | ![preview 7](123/preview_7.png) | ![preview 8](123/preview_8.png) | | 124 | 39 | [Download](124/dataset.zip) | ![preview 1](124/preview_1.png) | ![preview 2](124/preview_2.png) | ![preview 3](124/preview_3.png) | ![preview 4](124/preview_4.png) | ![preview 5](124/preview_5.png) | ![preview 6](124/preview_6.png) | ![preview 7](124/preview_7.png) | ![preview 8](124/preview_8.png) | | 125 | 15 | [Download](125/dataset.zip) | ![preview 1](125/preview_1.png) | ![preview 2](125/preview_2.png) | ![preview 3](125/preview_3.png) | ![preview 4](125/preview_4.png) | ![preview 5](125/preview_5.png) | ![preview 6](125/preview_6.png) | ![preview 7](125/preview_7.png) | ![preview 8](125/preview_8.png) | | 126 | 19 | [Download](126/dataset.zip) | ![preview 1](126/preview_1.png) | ![preview 2](126/preview_2.png) | ![preview 3](126/preview_3.png) | ![preview 4](126/preview_4.png) | ![preview 5](126/preview_5.png) | ![preview 6](126/preview_6.png) | ![preview 7](126/preview_7.png) | ![preview 8](126/preview_8.png) | | 127 | 11 | [Download](127/dataset.zip) | ![preview 1](127/preview_1.png) | ![preview 2](127/preview_2.png) | ![preview 3](127/preview_3.png) | ![preview 4](127/preview_4.png) | ![preview 5](127/preview_5.png) | ![preview 6](127/preview_6.png) | ![preview 7](127/preview_7.png) | ![preview 8](127/preview_8.png) | | 128 | 15 | [Download](128/dataset.zip) | ![preview 1](128/preview_1.png) | ![preview 2](128/preview_2.png) | ![preview 3](128/preview_3.png) | ![preview 4](128/preview_4.png) | ![preview 5](128/preview_5.png) | ![preview 6](128/preview_6.png) | ![preview 7](128/preview_7.png) | ![preview 8](128/preview_8.png) | | 129 | 8 | [Download](129/dataset.zip) | ![preview 1](129/preview_1.png) | ![preview 2](129/preview_2.png) | ![preview 3](129/preview_3.png) | ![preview 4](129/preview_4.png) | ![preview 5](129/preview_5.png) | ![preview 6](129/preview_6.png) | ![preview 7](129/preview_7.png) | ![preview 8](129/preview_8.png) | | 130 | 9 | [Download](130/dataset.zip) | ![preview 1](130/preview_1.png) | ![preview 2](130/preview_2.png) | ![preview 3](130/preview_3.png) | ![preview 4](130/preview_4.png) | ![preview 5](130/preview_5.png) | ![preview 6](130/preview_6.png) | ![preview 7](130/preview_7.png) | ![preview 8](130/preview_8.png) | | noise | 528 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |
tyzhu/squad_title_v4_train_30_eval_10_permute5
--- dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 625263.266025641 num_examples: 399 - name: validation num_bytes: 50807 num_examples: 50 download_size: 144382 dataset_size: 676070.266025641 --- # Dataset Card for "squad_title_v4_train_30_eval_10_permute5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/find_second_sent_train_50_eval_40
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 180317 num_examples: 140 - name: validation num_bytes: 39419 num_examples: 40 download_size: 0 dataset_size: 219736 --- # Dataset Card for "find_second_sent_train_50_eval_40" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/tatari_kogasa_touhou
--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tatari_kogasa/祟小傘 (Touhou) This is the dataset of tatari_kogasa/祟小傘 (Touhou), containing 27 images and their tags. The core tags of this character are `blue_hair, red_eyes, blue_eyes, heterochromia, breasts, short_hair, medium_breasts, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 27 | 25.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tatari_kogasa_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 27 | 16.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tatari_kogasa_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 52 | 30.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tatari_kogasa_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 27 | 22.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tatari_kogasa_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 52 | 40.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tatari_kogasa_touhou/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/tatari_kogasa_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, nipples, blush, karakasa_obake, purple_umbrella, tongue, navel, panties, nude, open_clothes, pussy, shirt | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, alternate_hair_length, long_hair, solo, dress, smile, aged_up, cleavage | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | nipples | blush | karakasa_obake | purple_umbrella | tongue | navel | panties | nude | open_clothes | pussy | shirt | alternate_hair_length | long_hair | dress | smile | aged_up | cleavage | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:--------|:-----------------|:------------------|:---------|:--------|:----------|:-------|:---------------|:--------|:--------|:------------------------|:------------|:--------|:--------|:----------|:-----------| | 0 | 18 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | | | | | | | | | | X | X | X | X | X | X |
tyzhu/find_word_train_10000_eval_100
--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 1441035 num_examples: 20100 - name: eval_find_word num_bytes: 5323 num_examples: 100 download_size: 0 dataset_size: 1446358 --- # Dataset Card for "find_word_train_10000_eval_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
links-ads/mmflood
--- license: mit task_categories: - image-segmentation language: - en tags: - semantic segmentation - remote sensing - sentinel-1 - flood pretty_name: MMFlood size_categories: - 1K<n<10K --- # MMFlood A Multimodal Dataset for Flood Delineation from Satellite Imagery. ![preview](resources/preview.png) ## Download The dataset has been compacted into tarfiles and zipped, you will need to recompose it before working with it: ```bash # clone the repository $ git clone git@hf.co:datasets/links-ads/mmflood # rebuild and extract the files $ cat activations.tar.*.gz.part > activations.tar.gz $ tar -xvzf activations.tar.gz ``` ## Structure The dataset is organized in directories, with a JSON file providing metadata and other information such as the split configuration we selected. Its internal structure is as follows: ``` activations/ ├─ EMSR107-1/ ├─ .../ ├─ EMSR548-0/ │ ├─ DEM/ │ │ ├─ EMSR548-0-0.tif │ │ ├─ EMSR548-0-1.tif │ │ ├─ ... │ ├─ hydro/ │ │ ├─ EMSR548-0-0.tif │ │ ├─ EMSR548-0-1.tif │ │ ├─ ... │ ├─ mask/ │ │ ├─ EMSR548-0-0.tif │ │ ├─ EMSR548-0-1.tif │ │ ├─ ... │ ├─ s1_raw/ │ │ ├─ EMSR548-0-0.tif │ │ ├─ EMSR548-0-1.tif │ │ ├─ ... activations.json ``` Each folder is named after the Copernicus EMS code it refers to. Since most of them actually contain more than one area, an incremental counter is added to the name, e.g., `EMSR458-0`, `EMSR458-1` and so on. Inside each EMSR folder there are four subfolders containing every available modality and the ground truth, in GeoTIFF format: - DEM: contains the Digital Elevation Model - hydro: contains the hydrography map for that region, if present - s1_raw: contains the Sentinel-1 image in VV-VH format - mask: contains the flood map, rasterized from EMS polygons Every EMSR subregion contains a variable number of tiles. However, for the same area, each modality always contains the same amount of files with the same name. Names have the following format: `<emsr_code>-<emsr_region>_<tile_count>`. For different reasons (retrieval, storage), areas larger than 2500x2500 pixels were divided in large tiles. > **Note:** Every modality is guaranteed to contain at least one image, except for the hydrography that may be missing. Last, the `activations.json` contains informations about each EMS activation, as extracted from the Copernicus Rapid Mapping site, as such: ```json { "EMSR107": { ... }, "EMSR548": { "title": "Flood in Eastern Sicily, Italy", "type": "Flood", "country": "Italy", "start": "2021-10-27T11:31:00", "end": "2021-10-28T12:35:19", "lat": 37.435056244442684, "lon": 14.954437192250033, "subset": "test", "delineations": [ "EMSR548_AOI01_DEL_PRODUCT_r1_VECTORS_v1_vector.zip" ] }, } ``` ## Data specifications |Image | Description | Format | Bands |S1 raw | Sentinel-1 (IW GRD) | GeoTIFF | Float32 0: VV, 1: VH |DEM | MapZen Digital Elevation Model | GeoTIFF | Float32 0: elevation |Hydrogr. | Permanent water basins, OSM | GeoTIFF | Uint8 0: hydro |Mask | Ground truth label, CEMS | GeoTIFF | Uint8 0: gt ### Image metadata Every image also contains the following contextual information, as GDAL metadata tags: ```xml <GDALMetadata> <Item name="acquisition_date">2021-10-31T16:56:28</Item> <Item name="code">EMSR548-0</Item> <Item name="country">Italy</Item> <Item name="event_date">2021-10-27T11:31:00</Item> </GDALMetadata> ``` - `acquisition_date` refers to the acquisition timestamp of the Sentinel-1 image - `event_date` refers to official event start date reported by Copernicus EMS ## Run experiments You can find the associated code in the following repository: ```console git clone git@github.com:edornd/mmflood.git && cd mmflood python3 -m venv .venv pip install -r requirements.txt ``` Everything goes through the run command. Run python run.py --help for more information about commands and their arguments. ### Data preparation To prepare the raw data by tiling and preprocessing, you can run: `python run.py prepare --data-source [PATH_TO_ACTIVATIONS] --data-processed [DESTINATION]` ### Training Training uses HuggingFace accelerate to provide single-gpu and multi-gpu support. To launch on a single GPU: ```console CUDA_VISIBLE_DEVICES=... python run.py train [ARGS] ``` You can find an example script with parameters in the scripts folder. ### Testing Testing is run on non-tiled images (the preprocessing will format them without tiling). You can run the test on a single GPU using the test command. At the very least, you need to point the script to the output directory. If no checkpoint is provided, the best one (according to the monitored metric) will be selected automatically. You can also avoid storing outputs with `--no-store-predictions`. ```console CUDA_VISIBLE_DEVICES=... python run.py test --data-root [PATH_TO_OUTPUT_DIR] [--checkpoint-path [PATH]] ``` ## Data Attribution and Licenses For the realization of this project, the following data sources were used: - Copernicus EMS - Copernicus Sentinel-1 - MapZen/TileZen Elevation - OpenStreetMap water layers
TaiyouIllusion/wiki40b_binidx
--- license: other ---